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Wang SY, Gao JC, Wu SD. Artificial intelligence for reducing missed detection of adenomas and polyps in colonoscopy: A systematic review and meta-analysis. World J Gastroenterol 2025; 31:105753. [DOI: 10.3748/wjg.v31.i21.105753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2025] [Revised: 03/21/2025] [Accepted: 05/19/2025] [Indexed: 06/06/2025] Open
Abstract
BACKGROUND Colorectal cancer has a high incidence and mortality rate, and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise. In recent years, computer-aided detection (CADe) systems have been increasingly integrated into colonoscopy to improve detection accuracy. However, while most studies have focused on adenoma detection rate (ADR) as the primary outcome, the more sensitive adenoma miss rate (AMR) has been less frequently analyzed.
AIM To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.
METHODS A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2, 2024. Statistical analyses were performed to compare outcomes between groups, and potential publication bias was assessed using funnel plots. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations, Assessment, Development, and Evaluation approach.
RESULTS Five studies comprising 1624 patients met the inclusion criteria. AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group (147/927, 15.9% vs 345/960, 35.9%; P < 0.01). However, CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or ≥ 10 mm. The polyp miss rate (PMR) was also lower in the CADe-assisted group [odds ratio (OR), 0.35; 95% confidence interval (CI): 0.23-0.52; P < 0.01]. While the overall ADR did not differ significantly between groups, the ADR during the first-pass examination was higher in the CADe-assisted group (OR, 1.37; 95%CI: 1.10-1.69; P = 0.004). The level of evidence for the included randomized controlled trials was graded as moderate.
CONCLUSION CADe can significantly reduce AMR and PMR while improving ADR during initial detection, demonstrating its potential to enhance colonoscopy performance. These findings highlight the value of CADe in improving the detection of colorectal neoplasms, particularly small and histologically distinct adenomas.
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Affiliation(s)
- Sheng-Yu Wang
- The Second Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Jia-Cheng Gao
- Department of Orthopedic Surgery, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Shuo-Dong Wu
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
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Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y. Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications. Cancer Lett 2025; 614:217555. [PMID: 39952597 DOI: 10.1016/j.canlet.2025.217555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 01/31/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025]
Abstract
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.
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Affiliation(s)
- Shengyuan Zhou
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yi Xie
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xujiao Feng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yanyan Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China; Department of Gastrointestinal Cancer, Beijing GoBroad Hospital, Beijing, 102200, China.
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Gallagher G, Malallah R, Epperlein JP, Dalli J, Hardy N, Jindal A, MacAonghusa PG, Cahill RA. A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization. PLoS One 2025; 20:e0317771. [PMID: 40080492 PMCID: PMC11906065 DOI: 10.1371/journal.pone.0317771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 01/03/2025] [Indexed: 03/15/2025] Open
Abstract
Real-time endoscopic rectal lesion characterization employing artificial intelligence (AI) and near-infrared (NIR) imaging of the fluorescence perfusion indicator agent Indocyanine Green (ICG) has demonstrated promise. However, commercially available fluorescence endoscopes do not possess the flexibility and anatomical reach capabilities of colonoscopy while commercial flexible scopes do not yet provide beyond visible spectral imaging. This limits the application of this AI-NIR classification technology. Here, to close this technical gap, we present our development of a colonoscope-compatible flexible imaging probe for NIR-ICG visualization combined with a full field of view machine learning (ML) algorithm for fluorescence quantification and perfusion pattern cross-correlation (including first in human testing). The imaging probe is capable of 133µm minimum object resolution, with a maximum working distance of 50mm and an excitation illumination power of 52mW with 75o average field of illumination (meaning minimum device tip distance from target is 13 mm for a 2 cm polyp). The system demonstrated ex-vivo and in-vivo NIR visualization of clinically relevant concentrations of ICG in both resected and in situ (extracorporeally) colon in patients undergoing colorectal resection. A previously developed AI-NIR perfusion quantification algorithm was applied to videos of a bench model of varying ICG flow captured with the developed flexible system with added ML features generated full field of view pixel-level fluorescence time-series measurements capable of distinguishing distinct ICG flow regions in the image via correlative dynamic fluorescence intensity profiles. Jaccard Index comparison of the AI -generated flow regions against manually delineated flow regions resulted in 79% accuracy. While further clinical validation of the AI-NIR polyp classification method is on-going (in the Horizon Europe Awarded CLASSICA project), other use case applications of NIR colonoscopy include simpler perioperative perfusion assessment in patients undergoing colorectal resection and combination with targeted agents in development thus encouraging continuing development and design optimization of this flexible NIR imaging probe to enable clinical and commercial translation.
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Affiliation(s)
- Gareth Gallagher
- School of Medicine, The Mater Misericordiae University Hospital and University College Dublin, Dublin, Ireland
| | - Ra’ed Malallah
- School of Medicine, The Mater Misericordiae University Hospital and University College Dublin, Dublin, Ireland
- Physics Department, Faculty of Science, University of Basrah, Garmat Ali, Basrah, Iraq
| | | | - Jeffrey Dalli
- School of Medicine, The Mater Misericordiae University Hospital and University College Dublin, Dublin, Ireland
| | - Niall Hardy
- School of Medicine, The Mater Misericordiae University Hospital and University College Dublin, Dublin, Ireland
| | - Abhinav Jindal
- School of Medicine, The Mater Misericordiae University Hospital and University College Dublin, Dublin, Ireland
| | | | - Ronan A. Cahill
- School of Medicine, The Mater Misericordiae University Hospital and University College Dublin, Dublin, Ireland
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Jahn B, Bundo M, Arvandi M, Schaffner M, Todorovic J, Sroczynski G, Knudsen A, Fischer T, Schiller-Fruehwirth I, Öfner D, Renner F, Jonas M, Kuchin I, Kruse J, Santamaria J, Ferlitsch M, Siebert U. One in three adenomas could be missed by white-light colonoscopy - findings from a systematic review and meta-analysis. BMC Gastroenterol 2025; 25:170. [PMID: 40082770 PMCID: PMC11908064 DOI: 10.1186/s12876-025-03679-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Accepted: 02/11/2025] [Indexed: 03/16/2025] Open
Abstract
BACKGROUND White light (conventional) colonoscopy (WLC) is widely used for colorectal cancer screening, diagnosis and surveillance but endoscopists may fail to detect adenomas. Our goal was to assess and synthesize overall and subgroup-specific adenoma miss rates (AMR) of WLC in daily practice. METHODS We conducted a systematic review in MEDLINE, EMBASE, Cochrane Library, and grey literature on studies evaluating diagnostic WLC accuracy in tandem studies with novel-colonoscopic technologies (NCT) in subjects undergoing screening, diagnostic or surveillance colonoscopy. Information on study design, AMR overall and specific for adenoma size, histology, location, morphology and further outcomes were extracted and reported in standardized evidence tables. Study quality was assessed using the QUADAS-2 tool. Random-effects meta-analyses and meta-regression were performed to estimate pooled estimates for AMR with 95% confidence intervals (95% CI) and to explain heterogeneity. RESULTS Out of 5,963 identified studies, we included sixteen studies with 4,101 individuals in our meta-analysis. One in three adenomas (34%; 95% CI: 30-38%) was missed by WLC in daily practice individuals. Subgroup analyses showed significant AMR differences by size (36%, adenomas 1-5 mm; 27%, adenomas 6-9 mm; 12%, adenomas ≥ 10 mm), histology (non-advanced: 42%, advanced: 21%), morphology (flat: 50%, polypoid: 27%), but not by location (distal: 36%, proximal: 36%). CONCLUSIONS Based on our meta-analysis, one in three adenomas could be missed by WLC. This may significantly contribute to interval cancers. Our results should be considered in health technology assessment when interpreting sensitivity of fecal occult blood or other screening tests derived from studies using WLC as "gold standard".
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Affiliation(s)
- Beate Jahn
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Marvin Bundo
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Marjan Arvandi
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Monika Schaffner
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Jovan Todorovic
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Gaby Sroczynski
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Amy Knudsen
- Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Timo Fischer
- Main Association of Austrian Social Security Institutions, Vienna, Austria
| | | | - Dietmar Öfner
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Michael Jonas
- Medical Association of Vorarlberg, Dornbirn, Austria
| | - Igor Kuchin
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Julia Kruse
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Júlia Santamaria
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Monika Ferlitsch
- Department of Internal Medicine III, Division of Gastroenterology and Hepatology, Medical University of Vienna, Vienna, Austria
| | - Uwe Siebert
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria.
- Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria.
- Center for Health Decision Science, Departments of Epidemiology and Health Policy & Management, Harvard T. H. Chan School of Public Health, Boston, USA.
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Hassan C, Bisschops R, Sharma P, Mori Y. Colon Cancer Screening, Surveillance, and Treatment: Novel Artificial Intelligence Driving Strategies in the Management of Colon Lesions. Gastroenterology 2025:S0016-5085(25)00478-0. [PMID: 40054749 DOI: 10.1053/j.gastro.2025.02.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 02/09/2025] [Accepted: 02/15/2025] [Indexed: 03/25/2025]
Abstract
Colonoscopy, a crucial procedure for detecting and removing colorectal polyps, has seen transformative advancements through the integration of artificial intelligence, specifically in computer-aided detection (CADe) and diagnosis (CADx). These tools enhance real-time detection and characterization of lesions, potentially reducing human error, and standardizing the quality of colonoscopy across endoscopists. CADe has proven effective in increasing adenoma detection rate, potentially reducing long-term colorectal cancer incidence. However, CADe's benefits are accompanied by challenges, such as potentially longer procedure times, increased non-neoplastic polyp resections, and a higher surveillance burden. CADx, although promising in differentiating neoplastic and non-neoplastic diminutive polyps, encounters limitations in accuracy, particularly in the proximal colon. Real-world data also revealed gaps between trial efficacy and practical outcomes, emphasizing the need for further research in uncontrolled settings. Moreover, CADx limited specificity and binary output underscore the necessity for explainable artificial intelligence to gain endoscopists' trust. This review aimed to explore the benefits, harms, and limitations of artificial intelligence for colon cancer screening, surveillance, and treatment focusing on CADe and CADx systems for lesion detection and characterization, respectively, while addressing challenges in integrating these technologies into clinical practice.
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Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Department of Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico, Humanitas Research Hospital, Rozzano, Italy.
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium; Translational Research Center in Gastrointestinal Disorders, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City Veterans Affairs Medical Center, Kansas City, Missouri
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
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6
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Spadaccini M, Hassan C, Mori Y, Massimi D, Correale L, Facciorusso A, Patel HK, Rizkala T, Khalaf K, Ramai D, Rondonotti E, Maselli R, Rex DK, Bhandari P, Sharma P, Repici A. Variability in computer-aided detection effect on adenoma detection rate in randomized controlled trials: A meta-regression analysis. Dig Liver Dis 2025:S1590-8658(25)00205-1. [PMID: 39924430 DOI: 10.1016/j.dld.2025.01.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Revised: 12/16/2024] [Accepted: 01/21/2025] [Indexed: 02/11/2025]
Abstract
BACKGROUND Computer-aided detection (CADe) systems may increase adenoma detection rate (ADR) during colonoscopy. However, the variable results of CADe effects in different RCTs warrant investigation into factors influencing these results. AIMS Investigate the different variables possibly affecting the impact of CADe-assisted colonoscopy and its effect on ADR. METHODS We searched MEDLINE, EMBASE, and Scopus databases until July 2023 for RCTs reporting performance of CADe systems in the detection of colorectal neoplasia. The main outcome was pooled ADR. A random-effects meta-analysis was performed to obtain the pooled risk ratios (RR) with 95 % confidence intervals (CI)). To explore sources of heterogeneity, we conducted a meta-regression analysis using both univariable and multivariable mixed-effects models. Potential explanatory variables included factors influencing adenoma prevalence, such as patient gender, age, and colonoscopy indication. We also included both key (ADR), and minor (Withdrawal time) performance measures considered as quality indicators for colonoscopy. RESULTS Twenty-three randomized controlled trials (RCTs) on 19,077 patients were include. ADR was higher in the CADe group (46 % [95 % CI 39-52]) than in the standard colonoscopy group (38 % [95 % CI 31-46]) with a risk ratio of 1.22 [95 % CI 1.14-1.29]); and a substantial level of heterogeneity (I2 = 67.69 %). In the univariable meta-regression analysis, patient age, ADR in control arms, and withdrawal time were the strongest predictors of CADe effect on ADR (P < .001). In multivariable meta-regression, ADR in control arms, and withdrawal time were simultaneous significant predictors of the proportion of the CADe effect on ADR. CONCLUSION The substantial level of heterogeneity found appeared to be associated with variability in colonoscopy quality performances across the studies, namely ADR in control arm, and withdrawal time.
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Affiliation(s)
- Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy.
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Yuichi Mori
- University of Oslo, Clinical Effectiveness Research Group, Oslo, Norway; Showa University Northern Yokohama Hospital, Digestive Disease Center, Yokohama, Japan
| | - Davide Massimi
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Loredana Correale
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Antonio Facciorusso
- University of Oslo, Clinical Effectiveness Research Group, Oslo, Norway; University of Salento, Gastroenterology Unit, Department of Experimental Medicine, Lecce, Italy
| | - Harsh K Patel
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, United States
| | - Tommy Rizkala
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy
| | - Kareem Khalaf
- St. Michael's Hospital, University of Toronto, Division of Gastroenterology, Toronto, Ontario, Canada
| | - Daryl Ramai
- University of Utah Health, Gastroenterology and Hepatology, Salt Lake City, UT, USA
| | | | - Roberta Maselli
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Douglas K Rex
- Indiana University School of Medicine, Division of Gastroenterology, Indianapolis, Indiana, USA
| | - Pradeep Bhandari
- Queen Alexandra Hospital, Department of Gastroenterology, Portsmouth, UK
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, United States
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
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Parikh M, Tejaswi S, Girotra T, Chopra S, Ramai D, Tabibian JH, Jagannath S, Ofosu A, Barakat MT, Mishra R, Girotra M. Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection. J Clin Gastroenterol 2025; 59:121-128. [PMID: 39774596 DOI: 10.1097/mcg.0000000000002115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2025]
Abstract
Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural Network models to enhance predictive accuracy for severe lower gastrointestinal (LGI) bleeding outcomes, including the need for surgery. To this end, artificial intelligence (AI)-guided predictive models have shown promise in improving management outcomes. While much literature focuses on AI in early neoplasia detection, this review highlights AI's role in managing LGI and small bowel disorders, including risk stratification for LGI bleeding, quality control, evaluation of inflammatory bowel disease, and video capsule endoscopy reading. Overall, the integration of AI into routine clinical practice is still developing, with ongoing research aimed at addressing current limitations and gaps in patient care.
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Affiliation(s)
| | - Sooraj Tejaswi
- University of California, Davis
- Sutter Health, Sacramento
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Hiratsuka Y, Hisabe T, Ohtsu K, Yasaka T, Takeda K, Miyaoka M, Ono Y, Kanemitsu T, Imamura K, Takeda T, Nimura S, Yao K. Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps. J Anus Rectum Colon 2025; 9:79-87. [PMID: 39882222 PMCID: PMC11772790 DOI: 10.23922/jarc.2024-057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 10/03/2024] [Indexed: 01/31/2025] Open
Abstract
Objectives Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYEⓇ (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR). Methods This randomized, open-label, single-center, tandem study was conducted at Fukuoka University Chikushi Hospital from February 2022 to November 2022. Patients were randomly assigned to the CADe or non-CADe group. Immediately after the completion of the first endoscopy by an endoscopist, a new endoscopist was assigned to perform the second endoscopy. As a result, different endoscopists performed the examinations in a tandem fashion. A missed lesion was defined as a newly detected colorectal polyp by the second endoscopy. Finally, the AMR was compared between the two groups. Results The study population comprised 48 patients in the CADe group and 46 patients in the non-CADe group. The AMR was 17.4% in the CADe group and 30.3% in the non-CADe group. Therefore, the AMR in the CADe group was statistically significantly lower than that in the non-CADe group (P=0.009). Conclusions The application of CAD-EYEⓇ to colonoscopy reduced the AMR. Overall, CAD-EYEⓇ might be useful for reducing missed colorectal adenomas.
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Affiliation(s)
- Yuya Hiratsuka
- Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Takashi Hisabe
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Kensei Ohtsu
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Tatsuhisa Yasaka
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Kazuhiro Takeda
- Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Masaki Miyaoka
- Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Yoichiro Ono
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Takao Kanemitsu
- Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Kentaro Imamura
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Teruyuki Takeda
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Satoshi Nimura
- Department of Pathology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Kenshi Yao
- Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan
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Liu J, Zhou R, Liu C, Liu H, Cui Z, Guo Z, Zhao W, Zhong X, Zhang X, Li J, Wang S, Xing L, Zhao Y, Ma R, Ni J, Li Z, Li Y, Zuo X. Automatic Quality Control System and Adenoma Detection Rates During Routine Colonoscopy: A Randomized Clinical Trial. JAMA Netw Open 2025; 8:e2457241. [PMID: 39883463 PMCID: PMC11783196 DOI: 10.1001/jamanetworkopen.2024.57241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 11/25/2024] [Indexed: 01/31/2025] Open
Abstract
Importance High-quality colonoscopy reduces the risks of colorectal cancer by increasing the adenoma detection rate. Routine use of an automatic quality control system (AQCS) to assist in colorectal adenoma detection should be considered. Objective To evaluate the effect of an AQCS on the adenoma detection rate among colonoscopists who were moderate- and low-level detectors during routine colonoscopy. Design, Setting, and Participants This multicenter, single-blind, randomized clinical trial was conducted at 6 centers in China from August 1, 2021, to September 30, 2022. Data were analyzed from March 1 to June 30, 2023. Individuals aged 18 to 80 years were enrolled. Exclusion criteria were a history of inflammatory bowel disease, advanced colorectal cancer, and polyposis syndromes; known colorectal polyps without complete removal previously; a history of colorectal surgery; known stenosis or obstruction with contraindication for biopsy or prior failed colonoscopy; pregnancy or lactation; and refusal to participate. Intention-to-treat and per-protocol analysis was used. Interventions Standard colonoscopy or AQCS-assisted colonoscopy. Main Outcomes and Measures Adenoma detection rate. Results A total of 1254 participants (mean [SD] age, 51.21 [12.10] years; 674 [53.7%] male) were randomized (627 standard colonoscopy, 627 AQCS-assisted colonoscopy). Intention-to-treat analysis showed a significantly higher adenoma detection rate in the AQCS-assisted group vs standard colonoscopy group (32.7% vs 22.6%; relative risk [RR], 1.60; 95% CI, 1.23-2.09; P < .001). The adenoma detection rates were significantly higher in the AQCS group when considering pathology (nonadvanced adenomas, 30.1% vs 21.2%; RR, 1.52; 95% CI, 1.16-1.99; P = .002), and morphology (flat or sessile, 29.3% vs 20.4%, RR, 1.52; 95% CI, 1.16-2.00; P = .003). Use of AQCS significantly increased the adenoma detection rate of both the lower-level detectors (30.0% vs 20.0%; RR, 1.71; 95% CI, 1.24-2.35; P = .001) and the medium-level detectors (38.1% vs 27.7%; RR, 1.61; 95% CI, 1.07-2.43; P = .02). Similar increases were found for adenoma detection rates in the academic and nonacademic centers (academic: 29.3% vs 20.8%; RR, 1.58; 95% CI, 1.10-2.29; P = .01; nonacademic: 36.1% vs 24.5%; RR, 1.74; 95% CI, 1.23-2.46; P = .002). The number of adenomas per colonoscopy was significantly higher in the AQCS-assisted group (0.86 vs 0.48; RR, 1.50; 95% CI, 1.17-1.91; P = .001). The mean withdrawal time without intervention was slightly increased with AQCS assistance (6.78 vs 6.46 minutes; RR, 1.38; 95% CI, 1.26-1.52; P < .001). No serious adverse events were reported. Conclusions and Relevance In this randomized clinical trial, AQCS assistance during routine colonoscopy increased adenoma detection rates and several related polyp parameters compared with standard colonoscopy in the lower- and medium-level detectors in academic and nonacademic settings. Routine use of AQCS to assist in colorectal adenoma detection and quality improvement should be considered. Trial Registration ClinicalTrials.gov Identifier: NCT04901130.
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Affiliation(s)
- Jing Liu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Gastroenterology, Qilu Hospital of Shandong University, Qingdao, Shandong, China
| | - Ruchen Zhou
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chengxia Liu
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Haiyan Liu
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
- Department of Gastroenterology, The First School of Clinical Medicine of Binzhou Medical University, Binzhou, Shandong, China
| | - Zhenqin Cui
- Department of Gastroenterology, Central Hospital of Shengli Oilfield, Dongying, Shandong, China
| | - Zhuang Guo
- Department of Gastroenterology, Central Hospital of Shengli Oilfield, Dongying, Shandong, China
| | - Weidong Zhao
- Department of Gastroenterology, Zibo Municipal Hospital, Zibo, Shandong, China
| | - Xiaoqin Zhong
- Department of Gastroenterology, Zibo Municipal Hospital, Zibo, Shandong, China
| | - Xiaodong Zhang
- Department of Gastroenterology, Linyi People’s Hospital, Dezhou, Shandong, China
| | - Jing Li
- Department of Gastroenterology, Linyi People’s Hospital, Dezhou, Shandong, China
| | - Shihuan Wang
- Department of Gastroenterology, The People’s Hospital of Zhaoyuan City, Yantai, Shandong, China
| | - Li Xing
- Department of Gastroenterology, The People’s Hospital of Zhaoyuan City, Yantai, Shandong, China
| | - Yusha Zhao
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ruiguang Ma
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Jiekun Ni
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Gastroenterology, Qilu Hospital of Shandong University, Qingdao, Shandong, China
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10
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Maida M, Marasco G, Maas MHJ, Ramai D, Spadaccini M, Sinagra E, Facciorusso A, Siersema PD, Hassan C. Effectiveness of artificial intelligence assisted colonoscopy on adenoma and polyp miss rate: A meta-analysis of tandem RCTs. Dig Liver Dis 2025; 57:169-175. [PMID: 39322447 DOI: 10.1016/j.dld.2024.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/20/2024] [Accepted: 09/01/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND AND AIMS One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal cancer (I-CRC). This systematic review and meta-analysis summarizes the efficacy of computer-aided colonoscopy (CAC) compared to white-light colonoscopy (WLC) in reducing lesion miss rates. METHODS Major databases were systematically searched through May 2024 for tandem-design RCTs comparing lesion miss rates in CAC-first followed by WLC vs WLC-first followed by CAC. The primary outcomes were adenoma miss rate (AMR) and polyp miss rate (PMR). The secondary outcomes were advanced AMR (aAMR) and sessile serrated lesion miss rate (SMR). RESULTS Six RCTs (1718 patients) were included. AMR was significantly lower for CAC compared to WLC (RR = 0.46; 95 %CI [0.38-0.55]; P < 0.001). PMR was also lower for CAC compared to WLC (RR = 0.44; 95 %CI [0.33-0.60]; P < 0.001). No significant difference in aAMR (RR = 1.28; 95 %CI [0.34-4.83]; P = 0.71) and SMR (RR = 0.44; 95 %CI [0.15-1.28]; P = 0.13) were observed. Sensitivity analysis including only RCTs performed in CRC screening and surveillance setting confirmed lower AMR (RR = 0.48; 95 %CI [0.39-0.58]; P < 0.001) and PMR (RR = 0.50; 95 %CI [0.37-0.66]; P < 0.001), also showing significantly lower SMR (RR = 0.28; 95 %CI [0.11-0.70]; P = 0.007) for CAC compared to WLC. CONCLUSIONS CAC results in significantly lower AMR and PMR compared to WLC overall, and significantly lower AMR, PMR and SMR in the screening/surveillance setting, potentially reducing the incidence of I-CRC.
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Affiliation(s)
- M Maida
- Department of Medicine and Surgery, University of Enna 'Kore', Enna, Italy; Gastroenterology Unit, Umberto I Hospital, Enna, Italy.
| | - G Marasco
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; IRCCS Azienda Ospedaliera Universitaria di Bologna, Bologna, Italy
| | - M H J Maas
- Department of Gastroenterology & Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - D Ramai
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - M Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy
| | - E Sinagra
- Gastroenterology Unit, Fondazione Istituto San Raffaele Giglio, Cefalù, Italy
| | - A Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - P D Siersema
- Depatment of Gastroenterology and Hepatology, Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - C Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy
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11
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Park JB, Bae JH. Effectiveness of a novel artificial intelligence-assisted colonoscopy system for adenoma detection: a prospective, propensity score-matched, non-randomized controlled study in Korea. Clin Endosc 2025; 58:112-120. [PMID: 39107138 PMCID: PMC11837574 DOI: 10.5946/ce.2024.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 07/18/2024] [Accepted: 07/21/2024] [Indexed: 08/09/2024] Open
Abstract
BACKGROUND/AIMS The real-world effectiveness of computer-aided detection (CADe) systems during colonoscopies remains uncertain. We assessed the effectiveness of the novel CADe system, ENdoscopy as AI-powered Device (ENAD), in enhancing the adenoma detection rate (ADR) and other quality indicators in real-world clinical practice. METHODS We enrolled patients who underwent elective colonoscopies between May 2022 and October 2022 at a tertiary healthcare center. Standard colonoscopy (SC) was compared to ENAD-assisted colonoscopy. Eight experienced endoscopists performed the procedures in randomly assigned CADe- and non-CADe-assisted rooms. The primary outcome was a comparison of ADR between the ENAD and SC groups. RESULTS A total of 1,758 sex- and age-matched patients were included and evenly distributed into two groups. The ENAD group had a significantly higher ADR (45.1% vs. 38.8%, p=0.010), higher sessile serrated lesion detection rate (SSLDR) (5.7% vs. 2.5%, p=0.001), higher mean number of adenomas per colonoscopy (APC) (0.78±1.17 vs. 0.61±0.99; incidence risk ratio, 1.27; 95% confidence interval, 1.13-1.42), and longer withdrawal time (9.0±3.4 vs. 8.3±3.1, p<0.001) than the SC group. However, the mean withdrawal times were not significantly different between the two groups in cases where no polyps were detected (6.9±1.7 vs. 6.7±1.7, p=0.058). CONCLUSIONS ENAD-assisted colonoscopy significantly improved the ADR, APC, and SSLDR in real-world clinical practice, particularly for smaller and nonpolypoid adenomas.
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Affiliation(s)
- Jung-Bin Park
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jung Ho Bae
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
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12
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Makar J, Abdelmalak J, Con D, Hafeez B, Garg M. Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis. Gastrointest Endosc 2025; 101:68-81.e8. [PMID: 39216648 DOI: 10.1016/j.gie.2024.08.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 08/17/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI) is increasingly used to improve adenoma detection during colonoscopy. This meta-analysis aimed to provide an updated evaluation of computer-aided detection (CADe) systems and their impact on key colonoscopy quality indicators. METHODS We searched the EMBASE, PubMed, and MEDLINE databases from inception until February 15, 2024, for randomized control trials (RCTs) comparing the performance of CADe systems with routine unassisted colonoscopy in the detection of colorectal adenomas. RESULTS Twenty-eight RCTs were selected for inclusion involving 23,861 participants. Random-effects meta-analysis demonstrated a 20% increase in adenoma detection rate (risk ratio [RR], 1.20; 95% confidence interval [CI], 1.14-1.27; P < .01) and 55% decrease in adenoma miss rate (RR, 0.45; 95% CI, 0.37-0.54; P < .01) with AI-assisted colonoscopy. Subgroup analyses involving only expert endoscopists demonstrated a similar effect size (RR, 1.19; 95% CI, 1.11-1.27; P < .001), with similar findings seen in analysis of differing CADe systems and healthcare settings. CADe use also significantly increased adenomas per colonoscopy (weighted mean difference, 0.21; 95% CI, 0.14-0.29; P < .01), primarily because of increased diminutive lesion detection, with no significant difference seen in detection of advanced adenomas. Sessile serrated lesion detection (RR, 1.10; 95% CI, 0.93-1.30; P = .27) and miss rates (RR, 0.44; 95% CI, 0.16-1.19; P = .11) were similar. There was an average 0.15-minute prolongation of withdrawal time with AI-assisted colonoscopy (weighted mean difference, 0.15; 95% CI, 0.04-0.25; P = .01) and a 39% increase in the rate of non-neoplastic resection (RR, 1.39; 95% CI, 1.23-1.57; P < .001). CONCLUSIONS AI-assisted colonoscopy significantly improved adenoma detection but not sessile serrated lesion detection irrespective of endoscopist experience, system type, or healthcare setting.
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Affiliation(s)
- Jonathan Makar
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jonathan Abdelmalak
- Department of Gastroenterology, Austin Hospital, Heidelberg, Victoria, Australia; Department of Gastroenterology, Alfred Hospital, Melbourne, Victoria, Australia; Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Danny Con
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia; Department of Gastroenterology, Austin Hospital, Heidelberg, Victoria, Australia
| | - Bilal Hafeez
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mayur Garg
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia; Department of Gastroenterology, Northern Health, Epping, Victoria, Australia
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13
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Khalaf K, Rizkala T, Repici A. The use of artificial intelligence in colonoscopic evaluations. Curr Opin Gastroenterol 2025; 41:3-8. [PMID: 39480883 DOI: 10.1097/mog.0000000000001063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/02/2024]
Abstract
PURPOSE OF REVIEW This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, particularly in the detection and characterization of colorectal polyps. RECENT FINDINGS Over the past decade, artificial intelligence has significantly advanced the medical industry, including gastrointestinal endoscopy. Computer aided diagnosis - detection (CADe) systems have shown notable success in increasing ADR. Recent meta-analyses of RCTs have demonstrated that patients undergoing colonoscopy with CADe assistance had a higher ADR compared with conventional methods. Similarly, computer aided diagnosis - characterization (CADx) systems have proven effective in distinguishing between adenomatous and nonadenomatous polyps, enhancing diagnostic confidence and supporting cost-saving measures like the resect-and-discard strategy. Despite the high performance of these systems, the variability in real-world adoption highlights the importance of integrating artificial intelligence as an assistive tool rather than a replacement for human expertise. SUMMARY Artificial intelligence integration in colonoscopy, through CADe and CADx systems, marks a significant advancement in gastroenterology. These systems enhance lesion detection and characterization, leading to improved diagnostic accuracy, training outcomes, and clinical workflow efficiency. While artificial intelligence offers substantial benefits, the optimal approach involves using artificial intelligence to augment the expertise of endoscopists, ensuring that clinical decisions remain under human oversight.
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Affiliation(s)
- Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Tommy Rizkala
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele
- Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Milan, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele
- Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Milan, Italy
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14
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Misawa M, Kudo SE. Current Status of Artificial Intelligence Use in Colonoscopy. Digestion 2024; 106:138-145. [PMID: 39724867 DOI: 10.1159/000543345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 12/24/2024] [Indexed: 12/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures. SUMMARY Colonoscopy is essential for colorectal cancer screening but often misses a significant percentage of adenomas. AI-assisted systems employing deep learning offer improved detection and differentiation of colorectal polyps, potentially increasing adenoma detection rates by 8%-10%. The main benefit of CADe is in detecting small adenomas, whereas it has a limited impact on advanced neoplasm detection. Recent advancements include real-time CADe systems and CADx for histopathological predictions, aiding in the differentiation of neoplastic and nonneoplastic lesions. Biases such as the Hawthorne effect and potential overdiagnosis necessitate large-scale clinical trials to validate the long-term benefits of AI. Additionally, novel concepts such as computer-aided quality improvement systems are emerging to address limitations facing current CADe systems. KEY MESSAGES Despite the potential of AI for enhancing colonoscopy outcomes, its effectiveness in reducing colorectal cancer incidence and mortality remains unproven. Further prospective studies are essential to establish the overall utility and clinical benefits of AI in colonoscopy.
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Affiliation(s)
- Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Tsuzuki, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Tsuzuki, Yokohama, Japan
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15
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Lee H, Chung JW, Kim KO, Kwon KA, Kim JH, Yun SC, Jung SW, Sheeraz A, Yoon YJ, Kim JH, Kayasseh MA. Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy. Diagnostics (Basel) 2024; 14:2762. [PMID: 39682670 DOI: 10.3390/diagnostics14232762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 12/02/2024] [Accepted: 12/04/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND/OBJECTIVES Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. METHODS We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®. RESULTS The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®. CONCLUSIONS The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance.
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Affiliation(s)
- Hannah Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Kyoung Oh Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Kwang An Kwon
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Jung Ho Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Sung-Cheol Yun
- Division of Biostatistics, Center for Medical Research and Information, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
| | - Sung Woo Jung
- Division of Gastroenterology, Department of Internal Medicine, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | | | | | - Ji Hee Kim
- CAIMI Co., Ltd., Incheon 22004, Republic of Korea
| | - Mohd Azzam Kayasseh
- Division of Gastroenterology, Dr. Sulaiman AI Habib Medical Group, Dubai Healthcare City, Dubai 51431, United Arab Emirates
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Li S, Xu M, Meng Y, Sun H, Zhang T, Yang H, Li Y, Ma X. The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors. MEDCOMM – ONCOLOGY 2024; 3. [DOI: 10.1002/mog2.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 09/03/2024] [Indexed: 01/04/2025]
Abstract
AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.
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Affiliation(s)
- Shen Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Maosen Xu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, West China Hospital, National Clinical Research, Sichuan University Chengdu Sichuan China
| | - Yuanling Meng
- West China School of Stomatology Sichuan University Chengdu Sichuan China
| | - Haozhen Sun
- College of Life Sciences Sichuan University Chengdu Sichuan China
| | - Tao Zhang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Hanle Yang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Yueyi Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Xuelei Ma
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
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17
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Labaki C, Uche-Anya EN, Berzin TM. Artificial Intelligence in Gastrointestinal Endoscopy. Gastroenterol Clin North Am 2024; 53:773-786. [PMID: 39489586 DOI: 10.1016/j.gtc.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2024]
Abstract
Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.
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Affiliation(s)
- Chris Labaki
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 300 Brookline Avenue, Boston, MA, USA
| | - Eugenia N Uche-Anya
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA.
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18
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Soleymanjahi S, Huebner J, Elmansy L, Rajashekar N, Lüdtke N, Paracha R, Thompson R, Grimshaw AA, Foroutan F, Sultan S, Shung DL. Artificial Intelligence-Assisted Colonoscopy for Polyp Detection : A Systematic Review and Meta-analysis. Ann Intern Med 2024; 177:1652-1663. [PMID: 39531400 DOI: 10.7326/annals-24-00981] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Randomized clinical trials (RCTs) of computer-aided detection (CADe) system-enhanced colonoscopy compared with conventional colonoscopy suggest increased adenoma detection rate (ADR) and decreased adenoma miss rate (AMR), but the effect on detection of advanced colorectal neoplasia (ACN) is unclear. PURPOSE To conduct a systematic review to compare performance of CADe-enhanced and conventional colonoscopy. DATA SOURCES Cochrane Library, Google Scholar, Ovid EMBASE, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases were searched through February 2024. STUDY SELECTION Published RCTs comparing CADe-enhanced and conventional colonoscopy. DATA EXTRACTION Average adenoma per colonoscopy (APC) and ACN per colonoscopy were primary outcomes. Adenoma detection rate, AMR, and ACN detection rate (ACN DR) were secondary outcomes. Balancing outcomes included withdrawal time and resection of nonneoplastic polyps (NNPs). Subgroup analyses were done by neural network architecture. DATA SYNTHESIS Forty-four RCTs with 36 201 cases were included. Computer-aided detection-enhanced colonoscopies have higher average APC (12 090 of 12 279 [0.98] vs. 9690 of 12 292 [0.78], incidence rate difference [IRD] = 0.22 [95% CI, 0.16 to 0.28]) and higher ADR (7098 of 16 253 [44.7%] vs. 5825 of 15 855 [36.7%], rate ratio [RR] = 1.21 [CI, 1.15 to 1.28]). Average ACN per colonoscopy was similar (1512 of 9296 [0.16] vs. 1392 of 9121 [0.15], IRD = 0.01 [CI, -0.01 to 0.02]), but ACN DR was higher with CADe system use (1260 of 9899 [12.7%] vs. 1119 of 9746 [11.5%], RR = 1.16 [CI, 1.02 to 1.32]). Using CADe systems resulted in resection of almost 2 extra NNPs per 10 colonoscopies and longer total withdrawal time (0.53 minutes [CI, 0.30 to 0.77]). LIMITATION Statistically significant heterogeneity in quality and sample size and inability to blind endoscopists to the intervention in included studies may affect the performance estimates. CONCLUSION Computer-aided detection-enhanced colonoscopies have increased APC and detection rate but no difference in ACN per colonoscopy and a small increase in ACN DR. There is minimal increase in procedure time and no difference in performance across neural network architectures. PRIMARY FUNDING SOURCE None. (PROSPERO: CRD42023422835).
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Affiliation(s)
- Saeed Soleymanjahi
- Division of Gastroenterology, Mass General Brigham, Harvard School of Medicine, Boston, Massachusetts (S.Soleymanjahi)
| | - Jack Huebner
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Lina Elmansy
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Niroop Rajashekar
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Nando Lüdtke
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut (N.L.)
| | - Rumzah Paracha
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Rachel Thompson
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Alyssa A Grimshaw
- Cushing/Whitney Medical Library, Yale University, New Haven, Connecticut (A.A.G.)
| | | | - Shahnaz Sultan
- Division of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota (S.Sultan)
| | - Dennis L Shung
- Section of Digestive Diseases, Clinical and Translational Research Accelerator, and Department of Biomedical Informatics and Data Science, Department of Medicine, Yale School of Medicine, New Haven, Connecticut (D.L.S.)
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19
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Park DK, Kim EJ, Im JP, Lim H, Lim YJ, Byeon JS, Kim KO, Chung JW, Kim YJ. A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection. Sci Rep 2024; 14:25453. [PMID: 39455850 PMCID: PMC11512038 DOI: 10.1038/s41598-024-77079-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 10/18/2024] [Indexed: 10/28/2024] Open
Abstract
Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. To develop the AI-assisted program, the dataset was fully anonymized and divided into 10 folds for 10-fold cross-validation. Each fold consisted of 9,639 training images and 1,070 validation images. Video data from 56 patients were used for model training, and transfer learning was performed using the developed still image-based model. The final model was developed as a real-time polyp-detection program for endoscopy. To evaluate the model's performance, a prospective randomized controlled trial was conducted at six institutions to compare the polyp detection rates (PDR). A total of 805 patients were included. The group that utilized the AI model showed significantly higher PDR and adenoma detection rate (ADR) than the group that underwent colonoscopy without AI assistance. Multivariate analysis revealed an OR of 1.50 for cases where polyps were detected. The AI-assisted polyp-detection program is clinically beneficial for detecting polyps during colonoscopy. By utilizing this AI-assisted program, clinicians can improve adenoma detection rates, ultimately leading to enhanced cancer prevention.
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Affiliation(s)
- Dong Kyun Park
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
- Health IT Research Center, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Eui Joo Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Jong Pil Im
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyun Lim
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyoung Oh Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Yoon Jae Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
- Health IT Research Center, Gachon University Gil Medical Center, Incheon, Republic of Korea.
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20
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Tada N, Tamai N, Sumiyama K. Screening Colonoscopy to Reduce the Incidence and Mortality of Colorectal Cancer. Digestion 2024; 106:100-106. [PMID: 39437753 DOI: 10.1159/000542113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 10/12/2024] [Indexed: 10/25/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) is a major concern because of its increasing incidence and mortality worldwide. Therefore, effective screening strategies are necessary to reduce its incidence. SUMMARY In addition to fecal immunochemical tests and computed tomography colonography, screening colonoscopy is expected to significantly contribute to the reduction of CRC. However, the timing of colonoscopy for CRC screening is not well-defined because of the lack of sufficient data. Additionally, the effectiveness of colonoscopy is affected by various factors known as quality indicators (QIs), such as the performance of the endoscopist; therefore, there are concerns regarding quality assurance. The adenoma detection rate (ADR) is a well-known QI of colonoscopy. Substantial evidence has suggested that improving the ADR could reduce the incidence and mortality of postcolonoscopy CRC. KEY MESSAGES Recent technological advancements have led to the development of image-enhanced endoscopy and the incorporation of artificial intelligence, and their ability to improve the ADR has been assessed. This review focused on screening colonoscopies and QIs and their ability to improve the ADR and incidence and mortality of CRC.
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Affiliation(s)
- Naoya Tada
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Naoto Tamai
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Kazuki Sumiyama
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
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21
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Mota J, Almeida MJ, Martins M, Mendes F, Cardoso P, Afonso J, Ribeiro T, Ferreira J, Fonseca F, Limbert M, Lopes S, Macedo G, Castro Poças F, Mascarenhas M. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. J Clin Med 2024; 13:5842. [PMID: 39407902 PMCID: PMC11477032 DOI: 10.3390/jcm13195842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.° 455/461, 4200-135 Porto, Portugal
| | - Filipa Fonseca
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
| | - Manuel Limbert
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
| | - Susana Lopes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Fernando Castro Poças
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Department of Gastroenterology, Santo António University Hospital, 4099-001 Porto, Portugal
- Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
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22
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Lee J, Cho WS, Kim BS, Yoon D, Kim J, Song JH, Yang SY, Lim SH, Chung GE, Choi JM, Han YM, Kong HJ, Lee JC, Kim S, Bae JH. Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection. Gut Liver 2024; 18:857-866. [PMID: 39054913 PMCID: PMC11391145 DOI: 10.5009/gnl240068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 05/03/2024] [Accepted: 05/09/2024] [Indexed: 07/27/2024] Open
Abstract
Background/Aims We investigated how interactions between humans and computer-aided detection (CADe) systems are influenced by the user's experience and polyp characteristics. Methods We developed a CADe system using YOLOv4, trained on 16,996 polyp images from 1,914 patients and 1,800 synthesized sessile serrated lesion (SSL) images. The performance of polyp detection with CADe assistance was evaluated using a computerized test module. Eighteen participants were grouped by colonoscopy experience (nurses, fellows, and experts). The value added by CADe based on the histopathology and detection difficulty of polyps were analyzed. Results The area under the curve for CADe was 0.87 (95% confidence interval [CI], 0.83 to 0.91). CADe assistance increased overall polyp detection accuracy from 69.7% to 77.7% (odds ratio [OR], 1.88; 95% CI, 1.69 to 2.09). However, accuracy decreased when CADe inaccurately detected a polyp (OR, 0.72; 95% CI, 0.58 to 0.87). The impact of CADe assistance was most and least prominent in the nurses (OR, 1.97; 95% CI, 1.71 to 2.27) and the experts (OR, 1.42; 95% CI, 1.15 to 1.74), respectively. Participants demonstrated better sensitivity with CADe assistance, achieving 81.7% for adenomas and 92.4% for easy-to-detect polyps, surpassing the standalone CADe performance of 79.7% and 89.8%, respectively. For SSLs and difficult-to-detect polyps, participants' sensitivities with CADe assistance (66.5% and 71.5%, respectively) were below those of standalone CADe (81.1% and 74.4%). Compared to the other two groups (56.1% and 61.7%), the expert group showed sensitivity closest to that of standalone CADe in detecting SSLs (79.7% vs 81.1%, respectively). Conclusions CADe assistance boosts polyp detection significantly, but its effectiveness depends on the user's experience, particularly for challenging lesions.
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Affiliation(s)
- Jooyoung Lee
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Woo Sang Cho
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Byeong Soo Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Dan Yoon
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Jung Kim
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Ji Hyun Song
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Sun Young Yang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Seon Hee Lim
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Goh Eun Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Ji Min Choi
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Yoo Min Han
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Hyoun-Joong Kong
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, Korea
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Korea
| | - Jung Chan Lee
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea
- Institute of Bioengineering, Seoul National University, Seoul, Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea
- Institute of Bioengineering, Seoul National University, Seoul, Korea
| | - Jung Ho Bae
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
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23
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Xu Q, He Z. Effect of different working periods on missed diagnosis of colorectal polyps in colonoscopy. BMC Gastroenterol 2024; 24:286. [PMID: 39187774 PMCID: PMC11346284 DOI: 10.1186/s12876-024-03365-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 08/08/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND To investigate the effect of different working periods on missed diagnoses in patients with colorectal polyps in colonoscopy. METHODS We conducted a retrospective analysis of patients who were diagnosed with colorectal polyps during colonoscopy in an outpatient department between July and December 2022. These patients were subsequently hospitalized for resection during this period. Patients with missed diagnoses were those who had newly discovered polyps in a second colonoscopy. The working periods were categorized as work, near the end of work, and delayed work, respectively, in the morning and afternoon. RESULTS A total of 482 patients were included, and the miss rate of diagnosis was 48.1% (232/482), mainly in the transverse colon (25%), and the ascending colon (23%). Patient age was a risk factor for the miss rate of diagnosis (OR = 1.025, 95%CI: 1.009-1.042, P = 0.003) and was also associated with the number of polyps detected for the first colonoscopy (χ2 = 18.196, P = 0.001). The different working periods had no statistical effect on the missed rate of diagnosis (χ2 = 1.998, P = 0.849). However, there was an increasing trend in miss rates towards the end of work and delayed work periods, both in the morning and afternoon. The highest miss rate (60.0%) was observed during delayed work in the afternoon. Additionally, poor bowel preparation was significantly more common during delayed work in the afternoon. CONCLUSIONS The increasing trend in miss rates towards the end of work and delayed work periods deserves clinical attention. Endoscopists cannot always stay in good condition under heavy workloads.
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Affiliation(s)
- Qing Xu
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Zhi He
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
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24
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Kikuchi R, Okamoto K, Ozawa T, Shibata J, Ishihara S, Tada T. Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms. Digestion 2024; 105:419-435. [PMID: 39068926 DOI: 10.1159/000540251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated. SUMMARY In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation. KEY MESSAGES Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.
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Affiliation(s)
- Ryosuke Kikuchi
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuaki Okamoto
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Ozawa
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
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25
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Introzzi L, Zonca J, Cabitza F, Cherubini P, Reverberi C. Enhancing human-AI collaboration: The case of colonoscopy. Dig Liver Dis 2024; 56:1131-1139. [PMID: 37940501 DOI: 10.1016/j.dld.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023]
Abstract
Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.
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Affiliation(s)
- Luca Introzzi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy
| | - Joshua Zonca
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy
| | - Federico Cabitza
- Department of Informatics, Systems and Communication, Università Milano - Bicocca, Milano, Italy; IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Paolo Cherubini
- Department of Brain and Behavioral Sciences, Università Statale di Pavia, Pavia, Italy
| | - Carlo Reverberi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy.
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Lui TKL, Lam CPM, To EWP, Ko MKL, Tsui VWM, Liu KSH, Hui CKY, Cheung MKS, Mak LLY, Hui RWH, Wong SY, Seto WK, Leung WK. Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial. Am J Gastroenterol 2024; 119:1318-1325. [PMID: 38305278 PMCID: PMC11208055 DOI: 10.14309/ajg.0000000000002684] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/19/2023] [Indexed: 02/03/2024]
Abstract
INTRODUCTION Both artificial intelligence (AI) and distal attachment devices have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. We studied the combined effect of Endocuff and AI on enhancing detection rates of various colonic lesions. METHODS This was a 3-arm prospective randomized colonoscopy study involving patients aged 40 years or older. Participants were randomly assigned in a 1:1:1 ratio to undergo Endocuff with AI, AI alone, or standard high-definition (HD) colonoscopy. The primary outcome was adenoma detection rate (ADR) between the Endocuff-AI and AI groups while secondary outcomes included detection rates of polyp (PDR), sessile serrated lesion (sessile detection rate [SDR]), and advanced adenoma (advanced adenoma detection rate) between the 2 groups. RESULTS A total of 682 patients were included (mean age 65.4 years, 52.3% male), with 53.7% undergoing diagnostic colonoscopy. The ADR for the Endocuff-AI, AI, and HD groups was 58.7%, 53.8%, and 46.3%, respectively, while the corresponding PDR was 77.0%, 74.0%, and 61.2%. A significant increase in ADR, PDR, and SDR was observed between the Endocuff-AI and AI groups (ADR difference: 4.9%, 95% CI: 1.4%-8.2%, P = 0.03; PDR difference: 3.0%, 95% CI: 0.4%-5.8%, P = 0.04; SDR difference: 6.4%, 95% CI: 3.4%-9.7%, P < 0.01). Both Endocuff-AI and AI groups had a higher ADR, PDR, SDR, and advanced adenoma detection rate than the HD group (all P < 0.01). DISCUSSION Endocuff in combination with AI further improves various colonic lesion detection rates when compared with AI alone.
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Affiliation(s)
- Thomas Ka-Luen Lui
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
| | | | - Elvis Wai-Pan To
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
- Department of Medicine, Tung Wah Hospital, Hong Kong, China.
| | | | | | | | | | - Michael Ka-Shing Cheung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
| | - Loey Lung-Yi Mak
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
| | - Rex Wan-Hin Hui
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
| | - Siu-Yin Wong
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
| | - Wai Kay Seto
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
| | - Wai K. Leung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Medicine, Queen Mary Hospital, Hong Kong, China;
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Mandarino FV, Danese S, Uraoka T, Parra-Blanco A, Maeda Y, Saito Y, Kudo SE, Bourke MJ, Iacucci M. Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy. Dig Endosc 2024; 36:761-777. [PMID: 37988279 DOI: 10.1111/den.14727] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/19/2023] [Indexed: 11/23/2023]
Abstract
Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.
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Affiliation(s)
- Francesco Vito Mandarino
- Department of Gastroenterology and Gastrointestinal Endoscopy, San Raffaele Hospital IRCSS, Milan, Italy
- Department of Gastrointestinal Endoscopy, Westmead Hospital, Sydney, NSW, Australia
| | - Silvio Danese
- Department of Gastroenterology and Gastrointestinal Endoscopy, San Raffaele Hospital IRCSS, Milan, Italy
| | - Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Gumma, Japan
| | - Adolfo Parra-Blanco
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Michael J Bourke
- Department of Gastrointestinal Endoscopy, Westmead Hospital, Sydney, NSW, Australia
| | - Marietta Iacucci
- Department of Gastroenterology, University College Cork, Cork, Ireland
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Oswald W, Browning C, Yasmin R, Deal J, Rich TC, Leavesley SJ, Gong N. Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection. Sci Rep 2024; 14:14790. [PMID: 38926431 PMCID: PMC11208566 DOI: 10.1038/s41598-024-64917-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.
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Affiliation(s)
- Willaim Oswald
- Department of Electrical and Computer Engineering, University of South Alabama, Mobile Alabama, 36688, USA
- Department of Systems Engineering, University of South Alabama, Mobile, AL, 36688, USA
| | - Craig Browning
- Department of Systems Engineering, University of South Alabama, Mobile, AL, 36688, USA
- Department of Chemical and Biomolecular Engineering, University of South Alabama, Mobile, AL, 36688, USA
| | - Ruthba Yasmin
- Department of Electrical and Computer Engineering, University of South Alabama, Mobile Alabama, 36688, USA
| | | | - Thomas C Rich
- Department of Pharmacology, University of South Alabama, Mobile, AL, 36688, USA
- Center for Lung Biology, University of South Alabama, Mobile, AL, 36688, USA
| | - Silas J Leavesley
- Department of Systems Engineering, University of South Alabama, Mobile, AL, 36688, USA.
- Department of Chemical and Biomolecular Engineering, University of South Alabama, Mobile, AL, 36688, USA.
- Department of Pharmacology, University of South Alabama, Mobile, AL, 36688, USA.
- Center for Lung Biology, University of South Alabama, Mobile, AL, 36688, USA.
| | - Na Gong
- Department of Electrical and Computer Engineering, University of South Alabama, Mobile Alabama, 36688, USA.
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Savino A, Rondonotti E, Rocchetto S, Piagnani A, Bina N, Di Domenico P, Segatta F, Radaelli F. GI genius endoscopy module: a clinical profile. Expert Rev Med Devices 2024; 21:359-372. [PMID: 38618982 DOI: 10.1080/17434440.2024.2342508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. AREAS COVERED This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. EXPERT OPINION Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.
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Affiliation(s)
- Alberto Savino
- Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy
| | | | - Simone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Pasquale Di Domenico
- Gastrointestinal Unit, Department of Medicine, Surgery & Dentistry Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Francesco Segatta
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
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Jin XF, Ma HY, Shi JW, Cai JT. Efficacy of artificial intelligence in reducing miss rates of GI adenomas, polyps, and sessile serrated lesions: a meta-analysis of randomized controlled trials. Gastrointest Endosc 2024; 99:667-675.e1. [PMID: 38184117 DOI: 10.1016/j.gie.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/20/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
BACKGROUND AND AIMS The aim of this study was to determine if utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and the polyp miss rate (PMR) compared with standard endoscopy. METHODS We performed an extensive search of various databases, encompassing PubMed, Embase, Cochrane Library, Web of Science, and Scopus, until June 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks, gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. The main study aim was to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate. RESULTS A total of 7 randomized controlled trials were included in this meta-analysis. Pooled AMR was markedly lower in the AI group versus the non-AI group (pooled relative risk [RR], .46; 95% confidence interval [CI], .36-.59; P < .001). PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR, .43; 95% CI, .27-.69; P < .001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, .43; 95% CI, .20 to .92; P < .05) and diminutive adenomas (pooled RR, .49; 95% CI, .26-.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, .48; 95% CI, .17-1.37; P = .17). The average number of polyps (Hedges' g = -.486; 95% CI, -.697 to -.274; P = .000) and adenomas (Hedges' g = -.312; 95% CI, -.551 to -.074; P = .01) detected during the second procedure also favored AI. However, AI implementation did not lead to a prolonged withdrawal time (P > .05). CONCLUSIONS This meta-analysis suggests that AI technology leads to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions during endoscopic surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopic procedures.
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Affiliation(s)
- Xi-Feng Jin
- Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
| | - Hong-Yan Ma
- Tengzhou Central People's Hospital, Shandong Province, Zaozhuang, China
| | - Jun-Wen Shi
- Tengzhou Central People's Hospital, Shandong Province, Zaozhuang, China
| | - Jian-Ting Cai
- Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
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Lee MCM, Parker CH, Liu LWC, Farahvash A, Jeyalingam T. Impact of study design on adenoma detection in the evaluation of artificial intelligence-aided colonoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2024; 99:676-687.e16. [PMID: 38272274 DOI: 10.1016/j.gie.2024.01.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/19/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND AIMS Randomized controlled trials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Different methodologies-namely, parallel and tandem designs-have been used to evaluate the efficacy of AI-assisted colonoscopy in RCTs. Systematic reviews and meta-analyses have reported a pooled effect that includes both study designs. However, it is unclear whether there are inconsistencies in the reported results of these 2 designs. Here, we aimed to determine whether study characteristics moderate between-trial differences in outcomes when evaluating the effectiveness of AI-assisted polyp detection. METHODS A systematic search of Ovid MEDLINE, Embase, Cochrane Central, Web of Science, and IEEE Xplore was performed through March 1, 2023, for RCTs comparing AI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection. The primary outcome of interest was the impact of study type on the adenoma detection rate (ADR). Secondary outcomes included the impact of the study type on adenomas per colonoscopy and withdrawal time, as well as the impact of geographic location, AI system, and endoscopist experience on ADR. Pooled event analysis was performed using a random-effects model. RESULTS Twenty-four RCTs involving 17,413 colonoscopies (AI assisted: 8680; non-AI assisted: 8733) were included. AI-assisted colonoscopy improved overall ADR (risk ratio [RR], 1.24; 95% confidence interval [CI], 1.17-1.31; I2 = 53%; P < .001). Tandem studies collectively demonstrated improved ADR in AI-aided colonoscopies (RR, 1.18; 95% CI, 1.08-1.30; I2 = 0%; P < .001), as did parallel studies (RR, 1.26; 95% CI, 1.17-1.35; I2 = 62%; P < .001), with no statistical subgroup difference between study design. Both tandem and parallel study designs revealed improvement in adenomas per colonoscopy in AI-aided colonoscopies, but this improvement was more marked among tandem studies (P < .001). AI assistance significantly increased withdrawal times for parallel (P = .002), but not tandem, studies. ADR improvement was more marked among studies conducted in Asia compared to Europe and North America in a subgroup analysis (P = .007). Type of AI system used or endoscopist experience did not affect overall improvement in ADR. CONCLUSIONS Either parallel or tandem study design can capture the improvement in ADR resulting from the use of AI-assisted polyp detection systems. Tandem studies powered to detect differences in endoscopic performance through paired comparison may be a resource-efficient method of evaluating new AI-assisted technologies.
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Affiliation(s)
- Michelle C M Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Colleen H Parker
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Louis W C Liu
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Armin Farahvash
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Thurarshen Jeyalingam
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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Yuan L, Zhou H, Xiao X, Zhang X, Chen F, Liu L, Liu J, Bao S, Tao K. Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study. Front Oncol 2024; 14:1365364. [PMID: 38725622 PMCID: PMC11079287 DOI: 10.3389/fonc.2024.1365364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
Background The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. Method In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Results Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Conclusion Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.
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Affiliation(s)
- Liuhong Yuan
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Henghua Zhou
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | | | - Xiuqin Zhang
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Feier Chen
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Lin Liu
- Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | | | - Shisan Bao
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Kun Tao
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Lau LHS, Ho JCL, Lai JCT, Ho AHY, Wu CWK, Lo VWH, Lai CMS, Scheppach MW, Sia F, Ho KHK, Xiao X, Yip TCF, Lam TYT, Kwok HYH, Chan HCH, Lui RN, Chan TT, Wong MTL, Ho MF, Ko RCW, Hon SF, Chu S, Futaba K, Ng SSM, Yip HC, Tang RSY, Wong VWS, Chan FKL, Chiu PWY. Effect of Real-Time Computer-Aided Polyp Detection System (ENDO-AID) on Adenoma Detection in Endoscopists-in-Training: A Randomized Trial. Clin Gastroenterol Hepatol 2024; 22:630-641.e4. [PMID: 37918685 DOI: 10.1016/j.cgh.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND The effect of computer-aided polyp detection (CADe) on adenoma detection rate (ADR) among endoscopists-in-training remains unknown. METHODS We performed a single-blind, parallel-group, randomized controlled trial in Hong Kong between April 2021 and July 2022 (NCT04838951). Eligible subjects undergoing screening/surveillance/diagnostic colonoscopies were randomized 1:1 to receive colonoscopies with CADe (ENDO-AID[OIP-1]) or not (control) during withdrawal. Procedures were performed by endoscopists-in-training with <500 procedures and <3 years' experience. Randomization was stratified by patient age, sex, and endoscopist experience (beginner vs intermediate level, <200 vs 200-500 procedures). Image enhancement and distal attachment devices were disallowed. Subjects with incomplete colonoscopies or inadequate bowel preparation were excluded. Treatment allocation was blinded to outcome assessors. The primary outcome was ADR. Secondary outcomes were ADR for different adenoma sizes and locations, mean number of adenomas, and non-neoplastic resection rate. RESULTS A total of 386 and 380 subjects were randomized to CADe and control groups, respectively. The overall ADR was significantly higher in the CADe group than in the control group (57.5% vs 44.5%; adjusted relative risk, 1.41; 95% CI, 1.17-1.72; P < .001). The ADRs for <5 mm (40.4% vs 25.0%) and 5- to 10-mm adenomas (36.8% vs 29.2%) were higher in the CADe group. The ADRs were higher in the CADe group in both the right colon (42.0% vs 30.8%) and left colon (34.5% vs 27.6%), but there was no significant difference in advanced ADR. The ADRs were higher in the CADe group among beginner (60.0% vs 41.9%) and intermediate-level (56.5% vs 45.5%) endoscopists. Mean number of adenomas (1.48 vs 0.86) and non-neoplastic resection rate (52.1% vs 35.0%) were higher in the CADe group. CONCLUSIONS Among endoscopists-in-training, the use of CADe during colonoscopies was associated with increased overall ADR. (ClinicalTrials.gov, Number: NCT04838951).
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Affiliation(s)
- Louis H S Lau
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Jacky C L Ho
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Jimmy C T Lai
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Agnes H Y Ho
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Claudia W K Wu
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Vincent W H Lo
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Carol M S Lai
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Markus W Scheppach
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Gastroenterology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Felix Sia
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR
| | - Kyle H K Ho
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR
| | - Xiang Xiao
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Medical Data Analytic Centre, The Chinese University of Hong Kong, Hong Kong SAR
| | - Terry C F Yip
- Medical Data Analytic Centre, The Chinese University of Hong Kong, Hong Kong SAR
| | - Thomas Y T Lam
- Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong SAR
| | - Hanson Y H Kwok
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Heyson C H Chan
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Rashid N Lui
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Ting-Ting Chan
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Marc T L Wong
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Man-Fung Ho
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Rachel C W Ko
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Sok-Fei Hon
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Simon Chu
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Koari Futaba
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Simon S M Ng
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Hon-Chi Yip
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Raymond S Y Tang
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Vincent W S Wong
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Francis K L Chan
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Philip W Y Chiu
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR.
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Tian S, Shi H, Chen W, Li S, Han C, Du F, Wang W, Wen H, Lei Y, Deng L, Tang J, Zhang J, Lin J, Shi L, Ning B, Zhao K, Miao J, Wang G, Hou H, Huang X, Kong W, Jin X, Ding Z, Lin R. Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study. Int J Surg 2024; 110:1637-1644. [PMID: 38079604 PMCID: PMC10942157 DOI: 10.1097/js9.0000000000000995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/27/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images. METHODS The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared. RESULTS In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98). CONCLUSIONS The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.
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Affiliation(s)
- Shuxin Tian
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
- Department of Gastroenterology, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi
- National Health Commission Key Laboratory of Central Asia High Incidence Disease Prevention and Control, Shihezi
| | - Huiying Shi
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Weigang Chen
- Department of Gastroenterology, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi
- National Health Commission Key Laboratory of Central Asia High Incidence Disease Prevention and Control, Shihezi
| | - Shijie Li
- National Health Commission Key Laboratory of Central Asia High Incidence Disease Prevention and Control, Shihezi
- Department of Endoscopy Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing
| | - Chaoqun Han
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Fan Du
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Weijun Wang
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Hongxu Wen
- Department of Gastroenterology, Lanzhou Second People’s Hospital, Lanzhou
| | - Yali Lei
- Department of Gastroenterology, Weinan Central Hospital, Weinan
| | - Liang Deng
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing
| | - Jing Tang
- Department of Gastroenterology, Fuling Hospital Affiliated to Chongqing University, Chongqing
| | - Jinjie Zhang
- Department of Gastroenterology, The Second Affiliated Hospital of Baotou Medical College, Baotou
| | - Jianjiao Lin
- Department of Gastroenterology, Longgang District People’s Hospital, Shenzhen
| | - Lei Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou
| | - Bo Ning
- Department of Gastroenterology, The Second Affiliated Hospital Chongqing Medical University, Chongqing
| | - Kui Zhao
- Department of Gastroenterology, The First Affiliated Hospital of Chendu Medical College, Chengdu
| | - Jiarong Miao
- Department of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming
- Yunnan Province Clinical Research Center for Digestive Diseases, Kunming
| | - Guobao Wang
- Department of endoscopy, Sun Yat-sen University Cancer Center,Guangzhou
| | - Hui Hou
- Department of Gastroenterology, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi
| | - Xiaoxi Huang
- Department of Gastroenterology, Haikou People’s Hospital, Haikou
| | - Wenjie Kong
- Department of Gastroenterology, People’s Hospital of Xinjiang Autonomous Region, Urumqi
| | - Xiaojuan Jin
- Department of Gastroenterology, Suining Central Hospital, Suining, People’s Republic of China
| | - Zhen Ding
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
- Department of Endoscopy Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Rong Lin
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
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Maas MHJ, Neumann H, Shirin H, Katz LH, Benson AA, Kahloon A, Soons E, Hazzan R, Landsman MJ, Lebwohl B, Lewis SK, Sivanathan V, Ngamruengphong S, Jacob H, Siersema PD. A computer-aided polyp detection system in screening and surveillance colonoscopy: an international, multicentre, randomised, tandem trial. Lancet Digit Health 2024; 6:e157-e165. [PMID: 38395537 DOI: 10.1016/s2589-7500(23)00242-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/01/2023] [Accepted: 11/16/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND Studies on the effect of computer-aided detection (CAD) in a daily clinical screening and surveillance colonoscopy population practice are scarce. The aim of this study was to evaluate a novel CAD system in a screening and surveillance colonoscopy population. METHODS This multicentre, randomised, controlled trial was done in ten hospitals in Europe, the USA, and Israel by 31 endoscopists. Patients referred for non-immunochemical faecal occult blood test (iFOBT) screening or surveillance colonoscopy were included. Patients were randomomly assigned to CAD-assisted colonoscopy or conventional colonoscopy; a subset was further randomly assigned to undergo tandem colonoscopy: CAD followed by conventional colonoscopy or conventional colonoscopy followed by CAD. Primary objectives included adenoma per colonoscopy (APC) and adenoma per extraction (APE). Secondary objectives included adenoma miss rate (AMR) in the tandem colonoscopies. The study was registered at ClinicalTrials.gov, NCT04640792. FINDINGS A total of 916 patients were included in the modified intention-to-treat analysis: 449 in the CAD group and 467 in the conventional colonoscopy group. APC was higher with CAD compared with conventional colonoscopy (0·70 vs 0·51, p=0·015; 314 adenomas per 449 colonoscopies vs 238 adenomas per 467 colonoscopies; poisson effect ratio 1·372 [95% CI 1·068-1·769]), while showing non-inferiority of APE compared with conventional colonoscopy (0·59 vs 0·66; p<0·001 for non-inferiority; 314 of 536 extractions vs 238 of 360 extractions). AMR in the 127 (61 with CAD first, 66 with conventional colonoscopy first) patients completing tandem colonoscopy was 19% (11 of 59 detected during the second pass) in the CAD first group and 36% (16 of 45 detected during the second pass) in the conventional colonoscopy first group (p=0·024). INTERPRETATION CAD increased adenoma detection in non-iFOBT screening and surveillance colonoscopies and reduced adenoma miss rates compared with conventional colonoscopy, without an increase in the resection of non-adenomatous lesions. FUNDING Magentiq Eye.
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Affiliation(s)
- Michiel H J Maas
- Department of Gastroenterology & Hepatology, Radboud University Medical Center, Nijmegen, Netherlands.
| | - Helmut Neumann
- University Medical Center Mainz, Interventional Endoscopy Center, I Medizinische Klinik und Poliklinik, Mainz, Germany
| | - Haim Shirin
- Institute of Gastroenterology and Liver Diseases, Shamir (Assaf Harofeh) Medical Center, Zerifin, Israel
| | - Lior H Katz
- Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Institute of Gastroenterology and Liver Diseases, Jerusalem, Israel
| | - Ariel A Benson
- Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Institute of Gastroenterology and Liver Diseases, Jerusalem, Israel
| | - Arslan Kahloon
- College of Medicine, Division of Gastroenterology, University of Tennessee, Chattanooga, TN, USA
| | - Elsa Soons
- Department of Gastroenterology & Hepatology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Rawi Hazzan
- Assuta Centers, Haifa Gastroenterology Institute, Haifa, Israel
| | - Marc J Landsman
- Department of Gastroenterology, MetroHealth Medical Center, Cleveland, OH, USA
| | - Benjamin Lebwohl
- Department of Gastroenterology, Columbia University Irving Medical Center, New York, NY, USA
| | - Suzanne K Lewis
- Department of Gastroenterology, Columbia University Irving Medical Center, New York, NY, USA
| | - Visvakanth Sivanathan
- University Medical Center Mainz, Interventional Endoscopy Center, I Medizinische Klinik und Poliklinik, Mainz, Germany
| | | | - Harold Jacob
- Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Institute of Gastroenterology and Liver Diseases, Jerusalem, Israel
| | - Peter D Siersema
- Department of Gastroenterology & Hepatology, Radboud University Medical Center, Nijmegen, Netherlands; Department of Gastroenterology & Hepatology, Erasmus MC, University Medical Center, Rotterdam, Netherlands
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Zhang J, Sun H, Xiong F, Lei S, Zhou G, Xiao X, Liu L, Wang P. The absolute number of small and diminutive adenomas with high-grade dysplasia is substantially higher compared with large adenomas: a retrospective pooled study. Front Oncol 2024; 14:1294745. [PMID: 38410098 PMCID: PMC10896556 DOI: 10.3389/fonc.2024.1294745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/22/2024] [Indexed: 02/28/2024] Open
Abstract
Introduction The risk that a large polyp (≥10 mm) evolves into high-grade dysplasia (HGD) is relatively high compared with that of a small/diminutive polyp (<10 mm). Recently, the detection of small and diminutive polyps has been substantially improved with the advancement of endoscopy. However, further research is needed on the role of the incidence of HGD caused by the co-occurrence of small and diminutive polyps in the progression of HGD. In this study, we aim to investigate whether and how the small and diminutive polyps correlate with the incidence of HGD in the population. Methods The pooled data were deeply analyzed from four published randomized controlled trials (RCTs) regarding colon polyp detection. All polyps detected were examined and confirmed by pathologists. The primary outcome was the composition ratio of the HGD polyps in each polyp size category. Results Among a total of 3,179 patients with 2,730 polyps identified, there were 83 HGD polyps confirmed, and 68 patients had at least one polyp with HGD. The risk of development of HGD was lower for a single small and diminutive polyp than for one large polyp (2.18% vs. 22.22%, P < 0.0001). On the contrary, the composition ratio for HGD from small and diminutive polyps was significantly higher than that from the large ones (68.67% vs. 31.33%, P < 0.0001). The combined number of HGD presented a trend negatively correlated to size. Conclusions Our data demonstrated that the absolute number of HGD significantly derives more from small and diminutive polyps than from the large ones, and the collective number of small and diminutive polyps per patient is indicative of his/her HGD exposure. These findings positively provide novel perspectives on the management of polyps and may further optimize the prevention of colorectal cancer. Systematic Review Registration http://www.chictr.org.cn, identifier ChiCTR1900025235, ChiCTR1800017675, ChiCTR1800018058, and ChiCTR1900023086.
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Affiliation(s)
- Jiancheng Zhang
- Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Huajun Sun
- Department of Pathology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Xiong
- Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Shan Lei
- Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Guanyu Zhou
- Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xun Xiao
- Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Liu
- Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China and Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Pu Wang
- Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Zhang H, Wu Q, Sun J, Wang J, Zhou L, Cai W, Zou D. A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial. Front Med (Lausanne) 2024; 10:1341259. [PMID: 38327275 PMCID: PMC10847558 DOI: 10.3389/fmed.2023.1341259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Background Up to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided detection (CADe) techniques based on deep learning might improve endoscopists' performance in detecting polyps. We aimed to evaluate the effectiveness of the CADe system in assisting endoscopists in a real-world clinical setting. Methods The CADe system was trained to detect colorectal polyps, recognize the ileocecal region, and monitor the speed of withdrawal during colonoscopy in real-time. Between 17 January 2021 and 16 July 2021. We recruited consecutive patients aged 18-75 years from three centers in China. We randomized patients in 1:1 groups to either colonoscopy with the CADe system or unassisted (control). The primary outcomes were the sensitivity and specificity of the endoscopists. We used subgroup analysis to examine the polyp detection rate (PDR) and the miss detection rate of endoscopists. Results A total of 1293 patients were included. The sensitivity of the endoscopists in the experimental group was significantly higher than that of the control group (84.97 vs. 72.07%, p < 0.001), and the specificity of the endoscopists in these two groups was comparable (100.00 vs. 100.00%). In a subgroup analysis, the CADe system improved the PDR of the 6-9 mm polyps (18.04 vs. 13.85%, p < 0.05) and reduced the miss detection rate, especially at 10:00-12:00 am (12.5 vs. 39.81%, p < 0.001). Conclusion The CADe system can potentially improve the sensitivity of endoscopists in detecting polyps, reduce the missed detection of polyps in colonoscopy, and reduce the risk of CRC. Registration This clinical trial was registered with the Chinese Clinical Trial Registry (Trial Registration Number: ChiCTR2100041988). Clinical trial registration website www.chictr.org.cn, identifier ChiCTR2100041988.
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Affiliation(s)
- Heng Zhang
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wu
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jing Sun
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Wang
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Lei Zhou
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Cai
- Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Troya J, Sudarevic B, Krenzer A, Banck M, Brand M, Walter BM, Puppe F, Zoller WG, Meining A, Hann A. Direct comparison of multiple computer-aided polyp detection systems. Endoscopy 2024; 56:63-69. [PMID: 37532115 PMCID: PMC10736101 DOI: 10.1055/a-2147-0571] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/01/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND AND STUDY AIMS Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset. METHODS 101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics. RESULTS Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind. CONCLUSIONS This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.
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Affiliation(s)
- Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
- Bavarian Cancer Research Center, Würzburg, Germany
| | - Boban Sudarevic
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
- Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
| | - Adrian Krenzer
- Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany
| | - Michael Banck
- Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany
| | - Markus Brand
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Benjamin M. Walter
- Department of Internal Medicine I, University Hospital Ulm, Ulm, Germany
| | - Frank Puppe
- Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany
| | - Wolfram G. Zoller
- Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
- Bavarian Cancer Research Center, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
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Yao L, Li X, Wu Z, Wang J, Luo C, Chen B, Luo R, Zhang L, Zhang C, Tan X, Lu Z, Zhu C, Huang Y, Tan T, Liu Z, Li Y, Li S, Yu H. Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study. Gastrointest Endosc 2024; 99:91-99.e9. [PMID: 37536635 DOI: 10.1016/j.gie.2023.07.044] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND AND AIMS The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts. METHODS This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR). RESULTS A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]). CONCLUSIONS AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).
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Affiliation(s)
- Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhifeng Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chaijie Luo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Boru Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Renquan Luo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chenxia Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xia Tan
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zihua Lu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ci Zhu
- Digestive Endoscopy Center, Wuhan Eighth Hospital, Wuhan, China
| | - Yuan Huang
- Digestive Endoscopy Center, Wuhan Eighth Hospital, Wuhan, China
| | - Tao Tan
- Department of Endoscopy, The Third People's Hospital of Hubei Province, Wuhan, China
| | - Zhifeng Liu
- Department of Endoscopy, The Third People's Hospital of Hubei Province, Wuhan, China
| | - Ying Li
- Digestive Endoscopy Center, Wuhan Eighth Hospital, Wuhan, China
| | - Shuyu Li
- Department of Endoscopy, The Third People's Hospital of Hubei Province, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
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Yuan XL, Liu W, Lin YX, Deng QY, Gao YP, Wan L, Zhang B, Zhang T, Zhang WH, Bi XG, Yang GD, Zhu BH, Zhang F, Qin XB, Pan F, Zeng XH, Chaudhry H, Pang MY, Yang J, Zhang JY, Hu B. Effect of an artificial intelligence-assisted system on endoscopic diagnosis of superficial oesophageal squamous cell carcinoma and precancerous lesions: a multicentre, tandem, double-blind, randomised controlled trial. Lancet Gastroenterol Hepatol 2024; 9:34-44. [PMID: 37952555 DOI: 10.1016/s2468-1253(23)00276-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/08/2023] [Accepted: 08/08/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Despite the usefulness of white light endoscopy (WLE) and non-magnified narrow-band imaging (NBI) for screening for superficial oesophageal squamous cell carcinoma and precancerous lesions, these lesions might be missed due to their subtle features and interpretation variations among endoscopists. Our team has developed an artificial intelligence (AI) system to detect superficial oesophageal squamous cell carcinoma and precancerous lesions using WLE and non-magnified NBI. We aimed to evaluate the auxiliary diagnostic performance of the AI system in a real clinical setting. METHODS We did a multicentre, tandem, double-blind, randomised controlled trial at 12 hospitals in China. Eligible patients were aged 18 years or older and underwent sedated upper gastrointestinal endoscopy for screening, investigation of gastrointestinal symptoms, or surveillance. Patients were randomly assigned (1:1) to either the AI-first group or the routine-first group using a computerised random number generator. Patients, pathologists, and statistical analysts were masked to group assignment, whereas endoscopists and research assistants were not. The same endoscopist at each centre did tandem upper gastrointestinal endoscopy for each eligible patient on the same day. In the AI-first group, the endoscopist did the first examination with the assistance of the AI system and the second examination without it. In the routine-first group, the order of examinations was reversed. The primary outcome was the miss rate of superficial oesophageal squamous cell carcinoma and precancerous lesions, calculated on a per-lesion and per-patient basis. All analyses were done on a per-protocol basis. This trial is registered with the Chinese Clinical Trial Registry (ChiCTR2100052116) and is completed. FINDINGS Between Oct 19, 2021, and June 8, 2022, 5934 patients were randomly assigned to the AI-first group and 5912 to the routine-first group, of whom 5865 and 5850 were eligible for analysis. Per-lesion miss rates were 1·7% (2/118; 95% CI 0·0-4·0) in the AI-first group versus 6·7% (6/90; 1·5-11·8) in the routine-first group (risk ratio 0·25, 95% CI 0·06-1·08; p=0·079). Per-patient miss rates were 1·9% (2/106; 0·0-4·5) in AI-first group versus 5·1% (4/79; 0·2-9·9) in the routine-first group (0·37, 0·08-1·71; p=0·40). Bleeding after biopsy of oesophageal lesions was observed in 13 (0·2%) patients in the AI-first group and 11 (0·2%) patients in the routine-first group. No serious adverse events were reported by patients in either group. INTERPRETATION The observed effect of AI-assisted endoscopy on the per-lesion and per-patient miss rates of superficial oesophageal squamous cell carcinoma and precancerous lesions under WLE and non-magnified NBI was consistent with substantial benefit through to a neutral or small negative effect. The effectiveness and cost-benefit of this AI system in real-world clinical settings remain to be further assessed. FUNDING National Natural Science Foundation of China, 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University, and Chengdu Science and Technology Project. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Xiang-Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi-Xiu Lin
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Qian-Yi Deng
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan-Ping Gao
- Department of Gastroenterology, Meishan People's Hospital, Meishan, China
| | - Ling Wan
- Department of Gastroenterology, Shimian People's Hospital, Ya'an, China
| | - Bin Zhang
- Department of Gastroenterology, Nanbu People's Hospital, Nanchong, China
| | - Tao Zhang
- Department of Gastroenterology, Nanchong Central Hospital, Nanchong, China
| | - Wan-Hong Zhang
- Department of Gastroenterology, Cangxi People's Hospital, Guangyuan, China
| | - Xiao-Gang Bi
- Department of Gastroenterology, Zigong Fourth People's Hospital, Zigong, China
| | - Guo-Dong Yang
- Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Bi-Hui Zhu
- Department of Gastroenterology, Zizhong People's Hospital, Neijiang, China
| | - Fan Zhang
- Department of Gastroenterology, The Third People's Hospital of Yunnan Province, Kunming, China
| | - Xiao-Bo Qin
- Department of Gastroenterology, The First Veterans Hospital of Sichuan Province, Chengdu, China
| | - Feng Pan
- Department of Gastroenterology, Huai'an First People's Hospital, Huai'an, China
| | - Xian-Hui Zeng
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Hunza Chaudhry
- Department of Internal Medicine, University of California San Francisco-Fresno, CA, USA
| | - Mao-Yin Pang
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL, USA
| | - Juliana Yang
- Department of Gastroenterology and Hepatology, The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Jing-Yu Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Gao W, Gao X, Zhu L, Gao S, Sun R, Feng Z, Wu D, Liu Z, Zhu R, Jiao N. Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer. Gut Microbes 2023; 15:2245562. [PMID: 37635357 PMCID: PMC10464540 DOI: 10.1080/19490976.2023.2245562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/29/2023] Open
Abstract
Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.
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Affiliation(s)
- Wenxing Gao
- Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Xiang Gao
- Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Lixin Zhu
- Guangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-Sen University, Guangzhou, P. R. China
- Department of General Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, P. R. China
| | - Sheng Gao
- Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Ruicong Sun
- Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Zhongsheng Feng
- Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Dingfeng Wu
- National Clinical Research Center for Child Health, the Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, P. R. China
| | - Zhanju Liu
- Center for IBD Research, Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, P. R. China
| | - Ruixin Zhu
- Department of Gastroenterology, the Shanghai Tenth People’s Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
- Research Institute, GloriousMed Clinical Laboratory Co, Ltd, Shanghai, P. R. China
| | - Na Jiao
- National Clinical Research Center for Child Health, the Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, P. R. China
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Lou S, Du F, Song W, Xia Y, Yue X, Yang D, Cui B, Liu Y, Han P. Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials. EClinicalMedicine 2023; 66:102341. [PMID: 38078195 PMCID: PMC10698672 DOI: 10.1016/j.eclinm.2023.102341] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 05/11/2024] Open
Abstract
BACKGROUND The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy. METHODS Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658). FINDINGS This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%). INTERPRETATION AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence. FUNDING This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).
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Affiliation(s)
- Shenghan Lou
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Fenqi Du
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wenjie Song
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yixiu Xia
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Xinyu Yue
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Da Yang
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Binbin Cui
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yanlong Liu
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Peng Han
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
- Key Laboratory of Tumor Immunology in Heilongjiang, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
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Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc 2023; 30:2050-2063. [PMID: 37647865 PMCID: PMC10654852 DOI: 10.1093/jamia/ocad180] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings. MATERIALS AND METHODS We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized. RESULTS Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed. CONCLUSION ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.
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Affiliation(s)
- Anindya Pradipta Susanto
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
| | - David Lyell
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Bambang Widyantoro
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
- National Cardiovascular Center Harapan Kita Hospital, Jakarta, DKI Jakarta 11420, Indonesia
| | - Shlomo Berkovsky
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
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Sekiguchi M, Igarashi A, Toyoshima N, Takamaru H, Yamada M, Esaki M, Kobayashi N, Saito Y. Cost-effectiveness analysis of computer-aided detection systems for colonoscopy in Japan. Dig Endosc 2023; 35:891-899. [PMID: 36752676 DOI: 10.1111/den.14532] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/05/2023] [Indexed: 02/09/2023]
Abstract
OBJECTIVES The usefulness of computer-aided detection systems (CADe) for colonoscopy has been increasingly reported. In many countries, however, data on the cost-effectiveness of their use are lacking; consequently, CADe for colonoscopy has not been covered by health insurance. We aimed to evaluate the cost-effectiveness of colonoscopy using CADe in Japan. METHODS We conducted a simulation model analysis using Japanese data to examine the cost-effectiveness of colonoscopy with and without CADe for a population aged 40-74 years who received colorectal cancer (CRC) screening with a fecal immunochemical test (FIT). The rates of receiving FIT screening and colonoscopy following a positive FIT were set as 40% and 70%, respectively. The sensitivities of FIT for advanced adenomas and CRC Dukes' A-D were 26.5% and 52.8-78.3%, respectively. CADe colonoscopy was judged to be cost-effective when its incremental cost-effectiveness ratio (ICER) was below JPY 5,000,000 per quality-adjusted life-years (QALYs) gained. RESULTS Compared to conventional colonoscopy, CADe colonoscopy showed a higher QALY (20.4098 vs. 20.4088) and lower CRC incidence (2373 vs. 2415 per 100,000) and mortality (561 vs. 569 per 100,000). When the CADe cost was set at JPY 1000-6000, the ICER per QALY gained for CADe colonoscopy was lower than JPY 5,000,000 (JPY 796,328-4,971,274). The CADe cost threshold at which the ICER for CADe colonoscopy exceeded JPY 5,000,000 was JPY 6040. CONCLUSIONS Computer-aided detection systems for colonoscopy has the potential to be cost-effective when the CADe cost is up to JPY 6000. These results suggest that the insurance reimbursement of CADe for colonoscopy is reasonable.
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Affiliation(s)
- Masau Sekiguchi
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
- Division of Screening Technology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Ataru Igarashi
- Department of Health Economics and Outcomes Research, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Department of Public Health, Yokohama City University School of Medicine, Kanagawa, Japan
| | - Naoya Toyoshima
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Minoru Esaki
- Hepatobiliary and Pancreatic Surgery Division, National Cancer Center Hospital, Tokyo, Japan
| | - Nozomu Kobayashi
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
- Division of Screening Technology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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Leśniewska M, Patryn R, Kopystecka A, Kozioł I, Budzyńska J. Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms. J Clin Med 2023; 12:6721. [PMID: 37959187 PMCID: PMC10650785 DOI: 10.3390/jcm12216721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions.
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Affiliation(s)
- Magdalena Leśniewska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Rafał Patryn
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
| | - Agnieszka Kopystecka
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Ilona Kozioł
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Julia Budzyńska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
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Okada N, Arimoto J, Nishiguchi T, Kobayashi M, Niikura T, Kuwabara H, Nakaoka M, Nakajima A, Chiba H. Effectiveness of switching endoscopists for repeat surveillance colonoscopy: a retrospective study. BMC Gastroenterol 2023; 23:347. [PMID: 37803276 PMCID: PMC10557195 DOI: 10.1186/s12876-023-02981-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/27/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Surveillance colonoscopy decreases colorectal cancer mortality; however, lesions are occasionally missed. Although an appropriate surveillance interval is indicated, variations may occur in the methods used, such as scope manipulation or observation. Therefore, individual endoscopists may miss certain areas. This study aimed to verify the effectiveness of performing repeat colonoscopies with a different endoscopist from the initial procedure. METHODS We retrospectively reviewed a database of 8093 consecutive colonoscopies performed in the Omori Red Cross Hospital from January 1st 2018 to June 30th 2021. Data from repeat total colonoscopies performed within three months were collected to assess missed lesions. The patients were divided into two groups according to whether the two examinations were performed by different endoscopists (group D) or the same endoscopist (group S). The primary outcome in both groups was the missed lesion detection rate (MLDR). RESULTS Overall, 205 eligible patients were analyzed. In total, 102 and 103 patients were enrolled in groups D and S, respectively. The MLDR was significantly higher in group D (61.8% vs. 31.1%, P < 0.0001). Multivariate logistic regression analysis for the detection of missed lesions identified performance by the different endoscopists (odds ratio, 3.38; 95% CI, 1.81-6.30), and sufficient withdrawal time (> 6 min) (odds ratio, 3.10; 95% CI, 1.12-8.61) as significant variables. CONCLUSIONS Overall, our study showed a significant improvement in the detection of missed lesions when performed by different endoscopists. When performing repeat colonoscopy, it is desirable that a different endoscopist perform the second colonoscopy. TRIAL REGISTRATION This study was approved by the Institutional Review Board of the Omori Red Cross Hospital on November 28, 2022 (approval number:22-43).
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Affiliation(s)
- Naoya Okada
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
- Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Japan
| | - Jun Arimoto
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
| | - Takanori Nishiguchi
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
| | - Mikio Kobayashi
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
| | - Toshihiro Niikura
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
| | - Hiroki Kuwabara
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
| | - Michiko Nakaoka
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan
| | - Atsushi Nakajima
- Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Japan
| | - Hideyuki Chiba
- Department of Gastroenterology, Omori Red Cross Hospital, 4‑30‑1, Chuo, Ota-Ku, Tokyo, 143‑8527, Japan.
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Mizukami K, Fushimi E, Sagami R, Abe T, Sato T, Terashi S, Fukuda M, Nishikiori H, Nagai T, Kodama M, Murakami K. Usefulness of AI-Equipped Endoscopy for Detecting Colorectal Adenoma during Colonoscopy Screening: Confirm That Colon Neoplasm Finely Can Be Identified by AI without Overlooking Study (Confidential Study). J Clin Med 2023; 12:6332. [PMID: 37834976 PMCID: PMC10573595 DOI: 10.3390/jcm12196332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/28/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
In the present prospective case series study, we investigated the lesion-detection ability of an AI-equipped colonoscopy as an addition to colonoscopy (CS) screening. Participants were 100 patients aged ≥20 years who had not undergone CS at the study site in the last 3 years and passed the exclusion criteria. CS procedures were conducted using conventional white light imaging and computer-aided detection (CADe). Adenoma detection rate (ADR; number of individuals with at least one adenoma detected) was compared between the conventional group and the CADe group. Of the 170 lesions identified, the ADR of the CADe group was significantly higher than the ADR of the conventional group (69% vs. 61%, p = 0.008). For the expert endoscopists, although ADR did not differ significantly, the mean number of detected adenomas per procedure (MAP) was significantly higher in the CADe group than in the conventional group (1.7 vs. 1.45, p = 0.034). For non-expert endoscopists, ADR and MAP were significantly higher in the CADe group than in the conventional group (ADR 69.5% vs. 56.6%, p = 0.016; MAP 1.66 vs. 1.11, p < 0.001). These results indicate that the CADe function in CS screening has a positive effect on adenoma detection, especially for non-experts.
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Affiliation(s)
- Kazuhiro Mizukami
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Erina Fushimi
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Ryota Sagami
- Department of Gastroenterology, Oita San-ai Medical Center, 1213 Ichi, Oita 870-1151, Japan
| | - Takashi Abe
- Department of Gastroenterology, Oita Koseiren Tsurumi Hospital, 4333, Tsurumi, Beppu, Oita 874-8585, Japan
| | - Takao Sato
- Department of Gastroenterology, Oita San-ai Medical Center, 1213 Ichi, Oita 870-1151, Japan
| | - Shohei Terashi
- Department of Gastroenterology, Oita Koseiren Tsurumi Hospital, 4333, Tsurumi, Beppu, Oita 874-8585, Japan
| | - Masahide Fukuda
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Hidefumi Nishikiori
- Department of Gastroenterology, Oita San-ai Medical Center, 1213 Ichi, Oita 870-1151, Japan
| | - Takayuki Nagai
- Department of Gastroenterology, Oita Koseiren Tsurumi Hospital, 4333, Tsurumi, Beppu, Oita 874-8585, Japan
| | - Masaaki Kodama
- Department of Advanced Medical Sciences, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Kazunari Murakami
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
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