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Liu Y, Li T, Lou L, Yang X, Kang C, Ren G, Zhang L, Zheng R, Kang X, Luo H, Liang S, Nie Y, Lv Y, Pan Y. Association between gastrointestinal lesions in individuals undergoing gastroscopy and colonoscopy simultaneously: a retrospective, observational study. BMC Gastroenterol 2025; 25:403. [PMID: 40413402 DOI: 10.1186/s12876-025-04012-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 05/20/2025] [Indexed: 05/27/2025] Open
Abstract
BACKGROUND Due to the common developmental origination and influences by similar unhealthy lifestyle, upper and lower gastrointestinal (GI) diseases may be closely associated. However, the evidence remains elusive. This study aims to determine the prevalence of GI endoscopic lesions and the correlations between endoscopic lesions in individuals undergoing gastroscopy and colonoscopy simultaneously. METHODS A retrospective study was conducted on 18,556 individuals who underwent simultaneous gastroscopy and colonoscopy at the Endoscopy Center of Xijing Hospital of Digestive Diseases from January 2020 to March 2023. Data on sex, age, pathological and endoscopic results were collected. The Pearson chi-square test was used to analyze the occurrence of various GI lesions among age groups and correlations between GI lesions, and logistic regression was used to determine risk factors for common upper and lower GI lesions. RESULTS The mean age was 50.35 ± 12.31 years, and 55.5% of participants were male. At least one endoscopic abnormality was observed in 16,530 cases (89.1%), with 8253 cases (44.5%) showing abnormalities in both the upper and lower GI tract. The most common upper GI endoscopic lesions were chronic atrophic gastritis (CAG, 47.7%), reflux esophagitis (RE, 24.7%), and other gastritis (18.1%). Colorectal polyps (CPs) were the most prevalent lower GI endoscopic condition, observed in 37.5% of cases. The detection of CAG, RE, CPs increased with age, and was higher in man. Moreover, the presence of CAG was associated with the occurrence of CPs (kappa value = 0.135, p < 0.001), which was independent of age and gender. CONCLUSION Most GI diseases are more prevalent in men and the elderly. Additionally, CAG is independently correlated with the occurrence of endoscopic CPs.
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Affiliation(s)
- Yaling Liu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Tongxin Li
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Lijun Lou
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Xintian Yang
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Chenxi Kang
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Gui Ren
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Linhui Zhang
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Rong Zheng
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Xiaoyu Kang
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Hui Luo
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Shuhui Liang
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Yongzhan Nie
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Yong Lv
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China.
| | - Yanglin Pan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China.
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Linhares SM, Schultz KS, Mongiu AK. Computer aided polyp detection has limited clinical efficacy. BMJ 2025; 389:r732. [PMID: 40246307 DOI: 10.1136/bmj.r732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2025]
Affiliation(s)
- Samantha M Linhares
- Division of Colon & Rectal Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT 06519, USA
| | - Kurt S Schultz
- Division of Colon & Rectal Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT 06519, USA
| | - Anne K Mongiu
- Division of Colon & Rectal Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT 06519, USA
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Murray J, Kefayat A, Finlayson M, Seenan JP, Hsu R, Din S. RCPE in association with the American College of Gastroenterology and the Scottish Society of Gastroenterology - Gastroenterology: A global perspective. J R Coll Physicians Edinb 2025:14782715251332318. [PMID: 40219669 DOI: 10.1177/14782715251332318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2025] Open
Abstract
On 6 November 2024, the Royal College of Physicians of Edinburgh (RCPE) hosted its annual gastroenterology symposium, marking the first collaboration with the American College of Gastroenterology (ACG) and the Scottish Society of Gastroenterology (SSG). The event addressed key global challenges in gastroenterology, including obesity, liver disease, inflammatory bowel disease (IBD), the gut microbiome, endoscopy quality and artificial intelligence (AI) applications. Discussions emphasised the growing burden of metabolic dysfunction-associated steatotic liver disease (MASLD), with promising pharmacologic and endoscopic interventions emerging. Advances in microbiome-targeted therapies, including faecal microbiota transplantation (FMT), were explored for recurrent Clostridium difficile infection and IBD. Professor David Rubin delivered the esteemed Sir Stanley Davidson lecture, highlighting the era of disease modification in IBD, emphasising early intervention and personalised treatment strategies. The symposium also addressed the role of AI in improving endoscopic detection rates and optimising resource allocation. This international collaboration underscored the importance of a multidisciplinary approach to tackling global digestive diseases, integrating clinical innovation, policy interventions and technological advancements. The event fostered knowledge exchange among global experts, aiming to advance patient care and improve long-term outcomes in gastroenterology.
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Affiliation(s)
- Jennifer Murray
- Department of Gastroenterology, St. Marks Academic Institute, London, UK
| | - Amirhosein Kefayat
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Edinburgh Inflammatory Bowel Diseases Unit, Western General Hospital, Edinburgh, UK
| | | | - John Paul Seenan
- Department of Gastroenterology, Queen Elizabeth University Hospital, Glasgow, UK
- Treasurer, Scottish Society of Gastroenterology, Edinburgh, UK
| | - Ronald Hsu
- Division of Gastroenterology, University of California Davis School of Medicine, Davis, CA, USA
- Governor for Northern California, American College of Gastroenterology, Bethesda, MD, USA
| | - Shahida Din
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Edinburgh Inflammatory Bowel Diseases Unit, Western General Hospital, Edinburgh, UK
- RCPE Gastroenterology Speciality Symposia Representative, RCPE, Edinburgh, UK
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Chiang AL, Hong H. The Role of Industry to Grow Clinical Artificial Intelligence Applications in Gastroenterology and Endoscopy. Gastrointest Endosc Clin N Am 2025; 35:485-501. [PMID: 40021243 DOI: 10.1016/j.giec.2024.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
The integration of artificial intelligence (AI) in health care has the potential to enhance diagnostics and disease management. In the field of gastroenterology, AI has shown promise in improving diagnostic accuracy and streamlining clinical workflows. Despite its potential, many AI innovations remain in the research phase, with significant hurdles to overcome in the transition from laboratory research to practical clinical tools. The industry is positioned to be a key player in this transition by fostering tight collaboration with clinicians and researchers, advocating for the responsible application of AI, and committing to the extensive process of medical device development and market introduction.
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Affiliation(s)
- Austin L Chiang
- Division of Gastroenterology and Hepatology, Thomas Jefferson University Hospital, 132 South 10th Street, Suite 585, Philadelphia, PA 19102, USA.
| | - Ha Hong
- Endoscopy Operating Unit, Medtronic Plc, Minneapolis, MN, USA
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Kong N, Chang P, Shulman IA, Haq U, Amini M, Nguyen D, Khan F, Narala R, Sharma N, Wang D, Thompson T, Sadik J, Breze C, Whitcomb DC, Buxbaum JL. Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes. Clin Transl Gastroenterol 2025; 16:e00825. [PMID: 39851257 PMCID: PMC12020695 DOI: 10.14309/ctg.0000000000000825] [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: 05/29/2024] [Accepted: 01/13/2025] [Indexed: 01/26/2025] Open
Abstract
INTRODUCTION Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our aim was to assess whether adherence to ADAPT fluid recommendations vs standard management impacted clinical outcomes in a large prospective cohort. METHODS We analyzed patients consecutively admitted to the Los Angeles General Medical Center between June 2015 and November 2022 whose course was richly characterized by capturing more than 100 clinical variables. We inputted these data into the ADAPT system to generate resuscitation fluid recommendations and compared with the actual fluid resuscitation within the first 24 hours from presentation. The primary outcome was the difference in organ failure in those who were over-resuscitated (>500 mL) vs adequately resuscitated (within 500 mL) with respect to the ADAPT fluid recommendation. Additional outcomes included intensive care unit admission, systemic inflammatory response syndrome (SIRS) at 48 hours, local complications, and pancreatitis severity. RESULTS Among the 1,083 patients evaluated using ADAPT, 700 were over-resuscitated, 196 were adequately resuscitated, and 187 were under-resuscitated. Adjusting for pancreatitis etiology, gender, and SIRS at admission, over-resuscitation was associated with increased respiratory failure (odd ratio [OR] 2.73, 95% confidence interval [CI] 1.06-7.03) as well as intensive care unit admission (OR 2.40, 1.41-4.11), more than 48 hours of hospital length of stay (OR 1.87, 95% CI 1.19-2.94), SIRS at 48 hours (OR 1.73, 95% CI 1.08-2.77), and local pancreatitis complications (OR 2.93, 95% CI 1.23-6.96). DISCUSSION Adherence to ADAPT fluid recommendations reduces respiratory failure and other adverse outcomes compared with conventional fluid resuscitation strategies for acute pancreatitis. This validation study demonstrates the potential role of dynamic machine learning tools in acute pancreatitis management.
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Affiliation(s)
- Niwen Kong
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Patrick Chang
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Ira A. Shulman
- Department of Pathology, University of Southern California, Los Angeles, California, USA;
| | - Ubayd Haq
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Maziar Amini
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Denis Nguyen
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Farhaad Khan
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Rachan Narala
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Nisha Sharma
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Daniel Wang
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Tiana Thompson
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Jonathan Sadik
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
| | - Cameron Breze
- Ariel Precision Medicine, Pittsburgh, Pennsylvania, USA;
| | - David C. Whitcomb
- Ariel Precision Medicine, Pittsburgh, Pennsylvania, USA;
- Division of Gastroenterology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - James L. Buxbaum
- Division of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA;
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6
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El-Sayed A, Lovat LB, Ahmad OF. Clinical Implementation of Artificial Intelligence in Gastroenterology: Current Landscape, Regulatory Challenges, and Ethical Issues. Gastroenterology 2025:S0016-5085(25)00538-4. [PMID: 40127785 DOI: 10.1053/j.gastro.2025.01.254] [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: 12/03/2024] [Revised: 01/06/2025] [Accepted: 01/10/2025] [Indexed: 03/26/2025]
Abstract
Artificial intelligence (AI) is set to rapidly transform gastroenterology, particularly in the field of endoscopy, where algorithms have demonstrated efficacy in addressing human operator variability. However, implementing AI in clinical practice presents significant challenges. The regulatory landscape for AI as a medical device continues to evolve with areas of uncertainty. More robust studies generating real-world evidence are required to ultimately demonstrate impacts on patient outcomes. Cost-effectiveness data and reimbursement models will be pivotal for widespread adoption. Novel challenges are posed by emerging technologies, such as generative AI. Ethical and medicolegal concerns exist relating to data governance, patient harm, liability, and bias. This review provides an overview for clinical implementation of AI in gastroenterology and offers potential solutions to current barriers.
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Affiliation(s)
- Ahmed El-Sayed
- Division of Surgery and Interventional Sciences, University College London, London, United Kingdom
| | - Laurence B Lovat
- Division of Surgery and Interventional Sciences, University College London, London, United Kingdom
| | - Omer F Ahmad
- Division of Surgery and Interventional Sciences, University College London, London, United Kingdom; Department of Gastrointestinal Services, University College London Hospital, London, United Kingdom.
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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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Kumar A, Aravind N, Gillani T, Kumar D. Artificial intelligence breakthrough in diagnosis, treatment, and prevention of colorectal cancer – A comprehensive review. Biomed Signal Process Control 2025; 101:107205. [DOI: 10.1016/j.bspc.2024.107205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2024]
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Spada C, Salvi D, Ferrari C, Hassan C, Barbaro F, Belluardo N, Grazioli LM, Milluzzo SM, Olivari N, Papparella LG, Pecere S, Pesatori EV, Petruzziello L, Piccirelli S, Quadarella A, Cesaro P, Costamagna G. A comprehensive RCT in screening, surveillance, and diagnostic AI-assisted colonoscopies (ACCENDO-Colo study). Dig Liver Dis 2025; 57:762-769. [PMID: 39814659 DOI: 10.1016/j.dld.2024.12.023] [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: 10/04/2024] [Revised: 12/14/2024] [Accepted: 12/31/2024] [Indexed: 01/18/2025]
Abstract
BACKGROUND AND AIMS Adenoma detection rate (ADR) serves as a primary quality metric in colonoscopy. Various computer-aided detection (CADe) tools have emerged, yielding diverse impacts on ADR across different demographic cohorts. This study aims to evaluate a new CADe system in patients undergoing colonoscopy. METHODS This is an Italian multicenter randomized control trial (RCT) that included patients aged 40-85 scheduled for screening, surveillance or diagnostic colonoscopy randomly assigned to CADe or standard colonoscopy (SC). Patients with a Boston Bowel Preparation Scale < 2 in any segment were excluded. The primary outcome was ADR in both groups. Secondary outcomes included adenoma per colonoscopy (APC), polyp per colonoscopy (PPC) and sessile serrated lesion detection rate (SSLDR). RESULTS 1228 patients were enrolled of whom 70 were excluded for inadequate bowel cleansing or missed cecal intubation. Therefore, 1158 subjects (578 CADe vs 580 SC) were included in the final analysis. ADR was significantly higher in CADe than in the control group (50.2 % vs 40.5 %, p = 0.001). CADe also significantly increased PPC and APC (1.64 ± 2.03 vs 1.23 ± 1.72, p < 0.001; 1.16 ± 1.82 vs 0.80 ± 1.46 p < 0.001; respectively). No significant differences were found in SSLDR between CADe and SC (12.1 % vs 11.0 %, p = 0.631). CONCLUSIONS The results of this RCT indicate that AI-assisted colonoscopy significantly improved ADR in a non-selected population undergoing colonoscopy without causing any significant delay in procedure time or increasing the detection of nonneoplastic lesions. (Ethical committee approval: NCT05862948).
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Affiliation(s)
- C Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - D Salvi
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy.
| | - C Ferrari
- Research and Clinical Trials Office, Fondazione Poliambulanza Istituto Ospedaliero Brescia, Italy
| | - C Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - F Barbaro
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - N Belluardo
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - L Minelli Grazioli
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - S M Milluzzo
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - N Olivari
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - L G Papparella
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - S Pecere
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - E V Pesatori
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - L Petruzziello
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - S Piccirelli
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - A Quadarella
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - P Cesaro
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - G Costamagna
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
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Zhou N, Yuan X, Liu W, Luo Q, Liu R, Hu B. Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions. Chin Med J (Engl) 2025:00029330-990000000-01442. [PMID: 40008787 DOI: 10.1097/cm9.0000000000003490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Indexed: 02/27/2025] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.
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Affiliation(s)
- Nuoya Zhou
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xianglei Yuan
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Med-X Center for Materials, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wei Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qi Luo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Ruide Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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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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12
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Debellotte O, Dookie RL, Rinkoo F, Kar A, Salazar González JF, Saraf P, Aflahe Iqbal M, Ghazaryan L, Mukunde AC, Khalid A, Olumuyiwa T. Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review. Cureus 2025; 17:e79199. [PMID: 40125138 PMCID: PMC11926462 DOI: 10.7759/cureus.79199] [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] [Accepted: 02/18/2025] [Indexed: 03/25/2025] Open
Abstract
Artificial intelligence (AI) is revolutionizing early cancer detection by enhancing the sensitivity, efficiency, and precision of screening programs for breast, colorectal, and lung cancers. Deep learning algorithms, such as convolutional neural networks, are pivotal in improving diagnostic accuracy by identifying patterns in imaging data that may elude human radiologists. AI has shown remarkable advancements in breast cancer detection, including risk stratification and treatment planning, with models achieving high specificity and precision in identifying invasive ductal carcinoma. In colorectal cancer screening, AI-powered systems significantly enhance polyp detection rates during colonoscopies, optimizing the adenoma detection rate and improving diagnostic workflows. Similarly, low-dose CT scans integrated with AI algorithms are transforming lung cancer screening by increasing the sensitivity and specificity of early-stage cancer detection, while aiding in accurate lesion segmentation and classification. This review highlights the potential of AI to streamline cancer diagnosis and treatment by analyzing vast datasets and reducing diagnostic variability. Despite these advancements, challenges such as data standardization, model generalization, and integration into clinical workflows remain. Addressing these issues through collaborative research, enhanced dataset diversity, and improved explainability of AI models will be critical for widespread adoption. The findings underscore AI's potential to significantly impact patient outcomes and reduce cancer-related mortality, emphasizing the need for further validation and optimization in diverse healthcare settings.
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Affiliation(s)
- Omofolarin Debellotte
- Internal Medicine, Brookdale Hospital Medical Center, One Brooklyn Health, Brooklyn, USA
| | | | - Fnu Rinkoo
- Medicine and Surgery, Ghulam Muhammad Mahar Medical College, Sukkur, PAK
| | - Akankshya Kar
- Internal Medicine, SRM Medical College Hospital and Research Centre, Chennai, IND
| | | | - Pranav Saraf
- Internal Medicine, SRM Medical College and Hospital, Chennai, IND
| | - Muhammed Aflahe Iqbal
- Internal Medicine, Muslim Educational Society (MES) Medical College Hospital, Perinthalmanna, IND
- General Practice, Naseem Medical Center, Doha, QAT
| | | | - Annie-Cheilla Mukunde
- Internal Medicine, Escuela de Medicina de la Universidad de Montemorelos, Montemorelos, MEX
| | - Areeba Khalid
- Respiratory Medicine, Sikkim Manipal Institute of Medical Sciences, Gangtok, IND
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Morimoto S, Tanaka H, Takehara Y, Yamamoto N, Tanino F, Kamigaichi Y, Yamashita K, Takigawa H, Urabe Y, Kuwai T, Oka S. Efficiency of Real-time Computer-aided Polyp Detection during Surveillance Colonoscopy: A Pilot Study. J Anus Rectum Colon 2025; 9:127-133. [PMID: 39882234 PMCID: PMC11772792 DOI: 10.23922/jarc.2024-055] [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: 05/28/2024] [Accepted: 10/26/2024] [Indexed: 01/31/2025] Open
Abstract
Objectives Studies have suggested that computer-aided polyp detection using artificial intelligence improves adenoma identification during colonoscopy. However, its real-world effectiveness remains unclear. Therefore, this study evaluated the usefulness of computer-aided detection during regular surveillance colonoscopy. Methods Consecutive patients who underwent surveillance colonoscopy with computer-aided detection between January and March 2023 and had undergone colonoscopy at least twice during the past 3 years were recruited. The clinicopathological findings of lesions identified using computer-aided detection were evaluated. The detection ability was sub-analyzed based on the expertise of the endoscopist and the presence of diminutive adenomas (size ≤5 mm). Results A total of 78 patients were included. Computer-aided detection identified 46 adenomas in 28 patients; however, no carcinomas were identified. The mean withdrawal time was 824 ± 353 s, and the mean tumor diameter was 3.3 mm (range, 2-8 mm). The most common gross type was 0-Is (70%), followed by 0-Isp (17%) and 0-IIa (13%). The most common tumor locations were the ascending colon and sigmoid colon (28%), followed by the transverse colon (26%), cecum (7%), descending colon (7%), and rectum (4%). Overall, 34.1% and 38.2% of patients with untreated diminutive adenomas and those with no adenomas, respectively, had newly detected adenomas. Endoscopist expertise did not affect the results. Conclusions Computer-aided detection may help identify adenomas during surveillance colonoscopy for patients with untreated diminutive adenomas and those with a history of endoscopic resection.
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Affiliation(s)
- Shin Morimoto
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Hidenori Tanaka
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Yudai Takehara
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Noriko Yamamoto
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Fumiaki Tanino
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Yuki Kamigaichi
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Ken Yamashita
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Hidehiko Takigawa
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Yuji Urabe
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Toshio Kuwai
- Gastrointestinal Endoscopy and Medicine, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
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14
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Alali AA, Alhashmi A, Alotaibi N, Ali N, Alali M, Alfadhli A. Artificial Intelligence for Adenoma and Polyp Detection During Screening and Surveillance Colonoscopy: A Randomized-Controlled Trial. J Clin Med 2025; 14:581. [PMID: 39860586 PMCID: PMC11766411 DOI: 10.3390/jcm14020581] [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: 12/20/2024] [Revised: 12/31/2024] [Accepted: 01/15/2025] [Indexed: 01/27/2025] Open
Abstract
Background: Colorectal cancer (CRC) is the second leading cause of cancer death in Kuwait. The effectiveness of colonoscopy in preventing CRC is dependent on a high adenoma detection rate (ADR). Computer-aided detection can identify (CADe) and characterize polyps in real time and differentiate benign from neoplastic polyps, but its role remains unclear in screening colonoscopy. Methods: This was a randomized-controlled trial (RCT) enrolling patients 45 years of age or older presenting for outpatient screening or surveillance colonoscopy (Kuwait clinical trial registration number 2047/2022). Patients with a history of inflammatory bowel disease, alarm symptoms, familial polyposis syndrome, colon resection, or poor bowel preparation were excluded. Patients were randomly assigned to either high-definition white-light (HD-WL) colonoscopy (standard of care) or HD-WL colonoscopy with the CADe system. The primary outcome was ADR. The secondary outcomes included polyp detection rate (PDR), adenoma per colonoscopy (APC), polyp per colonoscopy (PPC), and accuracy of polyp characterization. Results: From 1 September 2022 to 1 March 2023, 102 patients were included and allocated to either the HD-WL colonoscopy group (n = 51) or CADe group (n = 51). The mean age was 52.8 years (SD 8.2), and males represented 50% of the cohort. Screening for CRC accounted for 94.1% of all examinations, while the remaining patients underwent surveillance colonoscopy. A total of 121 polyps were detected with an average size of 4.18 mm (SD 5.1), the majority being tubular adenomas with low-grade dysplasia (47.1%) and hyperplastic polyps (46.3%). There was no difference in the overall bowel preparation, insertion and withdrawal times, and adverse events between the two arms. ADR (primary outcome) was non-significantly higher in the CADe group compared to the HD colonoscopy group (47.1% vs. 37.3%, p = 0.3). Among the secondary outcomes, PDR (78.4% vs. 56.8%, p = 0.02) and PPC (1.35 vs. 0.96, p = 0.04) were significantly higher in the CADe group, but APC was not (0.75 vs. 0.51, p = 0.09). Accuracy in characterizing polyp histology was similar in both groups. Conclusions: In this RCT, the artificial intelligence system showed a non-significant trend towards improving ADR among Kuwaiti patients undergoing screening or surveillance colonoscopy compared to HD-WL colonoscopy alone, while it significantly improved the detection of diminutive polyps. A larger multicenter study is required to detect the true effect of CADe on the detection of adenomas.
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Affiliation(s)
- Ali A. Alali
- Department of Medicine, Faculty of Medicine, Kuwait University, Jabriyah 13110, Kuwait
- Thunayan Alghanim Gastroenterology Center, Amiri Hospital, Sharq 15300, Kuwait
| | - Ahmad Alhashmi
- Department of Medicine, Faculty of Medicine, Kuwait University, Jabriyah 13110, Kuwait
| | - Nawal Alotaibi
- Department of Medicine, Jaber Alahmad Hospital, Zahra 47761, Kuwait
| | - Nargess Ali
- Department of Medicine, Faculty of Medicine, Kuwait University, Jabriyah 13110, Kuwait
| | - Maryam Alali
- Haya Al-Habeeb Gastroenterology Center, Mubarak Alkabeer Hospital, Jabriyah 13110, Kuwait
| | - Ahmad Alfadhli
- Haya Al-Habeeb Gastroenterology Center, Mubarak Alkabeer Hospital, Jabriyah 13110, Kuwait
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Mascarenhas M, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Ferreira J, Fonseca J, Macedo G. Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy. J Clin Med 2025; 14:549. [PMID: 39860554 PMCID: PMC11765989 DOI: 10.3390/jcm14020549] [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: 12/07/2024] [Revised: 12/29/2024] [Accepted: 01/02/2025] [Indexed: 01/27/2025] Open
Abstract
An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician's trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached. This review first defined the concepts underlying XAI, establishing the tools available and how they can benefit digestive healthcare. Examples of the application of XAI in digestive healthcare were provided, and potential future uses were proposed. In addition, aspects of the regulatory frameworks that must be established and the ethical concerns that must be borne in mind during the development of these tools were discussed. Finally, we considered the challenges that this technology faces to ensure that optimal benefits are reaped, highlighting the need for more research into the use of XAI in this field.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4099-002 Porto, Portugal
- Digestive Artificial Intelligence Development, 4200-135 Porto, Portugal
| | - João Fonseca
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (M.M.); (T.R.); (J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
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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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Park K, Lim J, Shin SH, Ryu M, Shin H, Lee M, Hong SW, Hwang SW, Park SH, Yang DH, Ye BD, Myung SJ, Yang SK, Kim N, Byeon JS. Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis. J Gastroenterol Hepatol 2025; 40:115-122. [PMID: 39496468 DOI: 10.1111/jgh.16788] [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: 06/04/2024] [Revised: 08/26/2024] [Accepted: 10/10/2024] [Indexed: 11/06/2024]
Abstract
BACKGROUND AND AIM Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose. METHODS The AI model was developed and assessed using an internal dataset comprising 1,132 colonoscopy images of CD and 1,045 colonoscopy images of GITB at a tertiary referral center. Its stand-alone performance was further evaluated in an external dataset comprising 67 colonoscopy images of 17 CD patients and 63 colonoscopy images of 14 GITB patients from other institutions. Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI-assisted and unassisted human interpretations. RESULTS In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (P = 0.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI vs 95.6% without, P = 0.360), it significantly improved accuracy for the trainees (81.0% vs 76.7%, P = 0.002). CONCLUSIONS This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.
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Affiliation(s)
- Kwangbeom Park
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, South Korea
| | - Jisup Lim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Seung Hwan Shin
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Minkyeong Ryu
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, South Korea
| | - Hyungeun Shin
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, South Korea
| | - Minyoung Lee
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, South Korea
| | - Seung Wook Hong
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Sung Wook Hwang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Sang Hyoung Park
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Dong-Hoon Yang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Byong Duk Ye
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Seung-Jae Myung
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Suk-Kyun Yang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
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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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19
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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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20
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Chung GE, Lee J, Lim SH, Kang HY, Kim J, Song JH, Yang SY, Choi JM, Seo JY, Bae JH. A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy. NPJ Digit Med 2024; 7:366. [PMID: 39702474 DOI: 10.1038/s41746-024-01334-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 11/08/2024] [Indexed: 12/21/2024] Open
Abstract
This study evaluated the impact of differing false positive (FP) rates in two computer-aided detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted colonoscopy. The primary outcomes were adenoma detection rate (ADR) and adenomas per colonoscopy (APC). The ADR in the control, system A (3.2% FP rate), and system B (0.6% FP rate) groups were 44.3%, 43.4%, and 50.4%, respectively, with system B showing a significantly higher ADR than the control group. The APC for the control, A, and B groups were 0.75, 0.83, and 0.90, respectively, with system B also showing a higher APC than the control. The non-true lesion resection rates were 23.8%, 29.2%, and 21.3%, with system B having the lowest. The system with lower FP rates demonstrated improved ADR and APC without increasing the resection of non-neoplastic lesions. These findings suggest that higher FP rates negatively affect the clinical performance of AI-assisted colonoscopy.
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Affiliation(s)
- Goh Eun Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Jooyoung Lee
- 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
| | - Hae Yeon Kang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 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
| | - Ji Min Choi
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Ji Yeon Seo
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 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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21
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Shaukat A, Lichtenstein DR, Chung DC, Seidl C, Wang Y, Navajas EE, Colucci DR, Baxi S, Brugge WR. Patient and procedural factors associated with true histology rates in patients undergoing colonoscopy with computer-aided detection of polyps. Gastrointest Endosc 2024; 100:1090-1094.e3. [PMID: 38964478 DOI: 10.1016/j.gie.2024.06.040] [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: 04/20/2024] [Revised: 06/14/2024] [Accepted: 06/30/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND AND AIMS Computer-aided detection (CADe) devices have been shown to increase adenoma detection rates and adenomas per colonoscopy compared to standard colonoscopies. Questions remain about whether CADe colonoscopies are mainly increasing the detection of small, nonneoplastic lesions or if they are detecting more pathologically meaningful polyps. In this analysis, we compare the true histology rate (defined as polyps with confirmation of clinically relevant histopathology) of CADe-identified polyps with polyps identified during standard colonoscopies. METHODS Using data from the SKOUT trial, we compared the true histology rate (THR) between CADe and standard colonoscopies. We also conducted a subgroup analysis by patient, procedural, and endoscopist factors. To account for multiple testing of comparisons, we used the false discovery rate. RESULTS A total of 1423 participants were included (CADe, n = 714; standard, n = 709). Overall, THR was similar between the CADe and standard colonoscopy arms for adenomas, sessile serrated lesions, and large hyperplastic polyps. Higher THR with CADe colonoscopy was observed in some subgroups for adenomas. Endoscopists with 11 to 20 years of experience and procedures occurring after 12 pm had significantly higher adenoma THRs in the CADe cohort. Patients younger than 65 years, male patients, and procedures with a withdrawal time of ≥8 minutes had borderline significance in the CADe device adenoma THR subgroup. CONCLUSIONS CADe colonoscopies may hold the key to improving endoscopic quality measures, provided that the polyps identified by the CADe device are those of clinical relevance. Although the benefit and significance in the CADe group were demonstrated in this analysis, further research is warranted to ensure that the true histology is maintained when applied in real-world applications.
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Affiliation(s)
- Aasma Shaukat
- Division of Gastroenterology and Hepatology, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA.
| | - David R Lichtenstein
- Division of Gastroenterology, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Daniel C Chung
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Yeli Wang
- Iterative Health Inc, Cambridge, Massachusetts, USA
| | | | | | - Shrujal Baxi
- Iterative Health Inc, Cambridge, Massachusetts, USA
| | - William R Brugge
- Division of Gastroenterology, Department of Medicine, Mount Auburn Hospital, Harvard Medical School, Boston, Massachusetts, USA
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22
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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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23
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Sinonquel P, Eelbode T, Pech O, De Wulf D, Dewint P, Neumann H, Antonelli G, Iacopini F, Tate D, Lemmers A, Pilonis ND, Kaminski MF, Roelandt P, Hassan C, Ingrid D, Maes F, Bisschops R. Clinical consequences of computer-aided colorectal polyp detection. Gut 2024; 73:1974-1983. [PMID: 38876773 DOI: 10.1136/gutjnl-2024-331943] [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: 01/12/2024] [Accepted: 06/02/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND AND AIM Randomised trials show improved polyp detection with computer-aided detection (CADe), mostly of small lesions. However, operator and selection bias may affect CADe's true benefit. Clinical outcomes of increased detection have not yet been fully elucidated. METHODS In this multicentre trial, CADe combining convolutional and recurrent neural networks was used for polyp detection. Blinded endoscopists were monitored in real time by a second observer with CADe access. CADe detections prompted reinspection. Adenoma detection rates (ADR) and polyp detection rates were measured prestudy and poststudy. Histological assessments were done by independent histopathologists. The primary outcome compared polyp detection between endoscopists and CADe. RESULTS In 946 patients (51.9% male, mean age 64), a total of 2141 polyps were identified, including 989 adenomas. CADe was not superior to human polyp detection (sensitivity 94.6% vs 96.0%) but outperformed them when restricted to adenomas. Unblinding led to an additional yield of 86 true positive polyp detections (1.1% ADR increase per patient; 73.8% were <5 mm). CADe also increased non-neoplastic polyp detection by an absolute value of 4.9% of the cases (1.8% increase of entire polyp load). Procedure time increased with 6.6±6.5 min (+42.6%). In 22/946 patients, the additional detection of adenomas changed surveillance intervals (2.3%), mostly by increasing the number of small adenomas beyond the cut-off. CONCLUSION Even if CADe appears to be slightly more sensitive than human endoscopists, the additional gain in ADR was minimal and follow-up intervals rarely changed. Additional inspection of non-neoplastic lesions was increased, adding to the inspection and/or polypectomy workload.
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Affiliation(s)
- Pieter Sinonquel
- Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven Biomedical Sciences Group, Leuven, Belgium
| | - Tom Eelbode
- Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Oliver Pech
- Gastroenterology and Hepatology, Krankenhaus Barmherzige Bruder Regensburg, Regensburg, Germany
| | - Dominiek De Wulf
- Gastroenterology and Hepatology, AZ Delta vzw, Roeselare, Belgium
| | - Pieter Dewint
- Gastroenterology and Hepatology, AZ Maria Middelares vzw, Gent, Belgium
| | - Helmut Neumann
- Gastroenterology and Hepatology, Gastrozentrum Lippe, Bad Salzuflen, Germany
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale Nuovo Regina Margherita, Roma, Italy
| | - Federico Iacopini
- Gastroenterology and Digestive endoscopy, Ospedale dei Castelli, Ariccia, Italy
| | - David Tate
- Gastroenterology and Hepatology, UZ Gent, Gent, Belgium
| | - Arnaud Lemmers
- Gastroenterology and Hepatology, ULB Erasme, Bruxelles, Belgium
| | | | - Michal Filip Kaminski
- Department of Gastroenterology, Hepatology and Oncology, Medical Centre fo Postgraduate Education, Warsaw, Poland
- Department of Gastroenterological Oncology, The Maria Sklodowska-Curie Memorial Cancer Centre, Instytute of Oncology, Warsaw, Poland
| | - Philip Roelandt
- Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven Biomedical Sciences Group, Leuven, Belgium
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
| | - Demedts Ingrid
- Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven Biomedical Sciences Group, Leuven, Belgium
| | - Frederik Maes
- Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Raf Bisschops
- Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven Biomedical Sciences Group, Leuven, Belgium
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24
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Thiruvengadam NR, Solaimani P, Shrestha M, Buller S, Carson R, Reyes-Garcia B, Gnass RD, Wang B, Albasha N, Leonor P, Saumoy M, Coimbra R, Tabuenca A, Srikureja W, Serrao S. The Efficacy of Real-time Computer-aided Detection of Colonic Neoplasia in Community Practice: A Pragmatic Randomized Controlled Trial. Clin Gastroenterol Hepatol 2024; 22:2221-2230.e15. [PMID: 38437999 DOI: 10.1016/j.cgh.2024.02.021] [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: 10/27/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND & AIMS The use of computer-aided detection (CADe) has increased the adenoma detection rates (ADRs) during colorectal cancer (CRC) screening/surveillance in randomized controlled trials (RCTs) but has not shown benefit in real-world implementation studies. We performed a single-center pragmatic RCT to evaluate the impact of real-time CADe on ADRs in colonoscopy performed by community gastroenterologists. METHODS We enrolled 1100 patients undergoing colonoscopy for CRC screening, surveillance, positive fecal-immunohistochemical tests, and diagnostic indications at one community-based center from September 2022 to March 2023. Patients were randomly assigned (1:1) to traditional colonoscopy or real-time CADe. Blinded pathologists analyzed histopathologic findings. The primary outcome was ADR (the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy (APC), sessile-serrated lesion detection rate, and non-neoplastic resection rate. RESULTS The median age was 55.5 years (interquartile range, 50-62 years), 61% were female, 72.7% were of Hispanic ethnicity, and 9.1% had inadequate bowel preparation. The ADR for the CADe group was significantly higher than the traditional colonoscopy group (42.5% vs 34.4%; P = .005). The mean APC was significantly higher in the CADe group compared with the traditional colonoscopy group (0.89 ± 1.46 vs 0.60 ± 1.12; P < .001). The improvement in adenoma detection was driven by increased detection of <5 mm adenomas. CADe had a higher sessile-serrated lesion detection rate than traditional colonoscopy (4.7% vs 2.0%; P = .01). The improvement in ADR with CADe was significantly higher in the first half of the study (47.2% vs 33.7%; P = .002) compared with the second half (38.7% vs 34.9%; P = .33). CONCLUSIONS In a single-center pragmatic RCT, real-time CADe modestly improved ADR and APC in average-detector community endoscopists. (ClinicalTrials.gov number, NCT05963724).
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Affiliation(s)
- Nikhil R Thiruvengadam
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California.
| | - Pejman Solaimani
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Manish Shrestha
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Seth Buller
- Loma Linda University School of Medicine, Loma Linda, California
| | - Rachel Carson
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Breanna Reyes-Garcia
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Ronaldo D Gnass
- Department of Pathology, Riverside University Health System, Moreno Valley, California
| | - Bing Wang
- Department of Pathology, Loma Linda University School of Medicine, Loma Linda, California
| | - Natalie Albasha
- University of California Riverside School of Medicine, Riverside, California; Department of Medicine, Scripps Green Hospital, La Jolla, California
| | - Paul Leonor
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Monica Saumoy
- Center for Digestive Health, Penn Medicine Princeton Medical Center, Plainsboro, New Jersey
| | - Raul Coimbra
- Comparative Effectiveness and Clinical Outcomes Research Center, Riverside University Health System, Moreno Valley, California; Department of Surgery, Riverside University Health System, Moreno Valley, California
| | - Arnold Tabuenca
- Department of Surgery, Riverside University Health System, Moreno Valley, California; Department of Surgery, University of California Riverside School of Medicine, Riverside, California
| | - Wichit Srikureja
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Steve Serrao
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
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25
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Maas MHJ, Rath T, Spada C, Soons E, Forbes N, Kashin S, Cesaro P, Eickhoff A, Vanbiervliet G, Salvi D, Belletrutti PJ, Siersema PD, for the Discovery study team . A computer-aided detection system in the everyday setting of diagnostic, screening, and surveillance colonoscopy: an international, randomized trial. Endoscopy 2024; 56:843-850. [PMID: 38749482 PMCID: PMC11524745 DOI: 10.1055/a-2328-2844] [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: 02/10/2024] [Accepted: 05/15/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Computer-aided detection (CADe) has been developed to improve detection during colonoscopy. After initial reports of high efficacy, there has been an increasing recognition of variability in the effectiveness of CADe systems. The aim of this study was to evaluate a CADe system in a varied colonoscopy population. METHODS A multicenter, randomized trial was conducted at seven hospitals (both university and non-university) in Europe and Canada. Participants referred for diagnostic, non-immunochemical fecal occult blood test (iFOBT) screening, or surveillance colonoscopy were randomized (1:1) to undergo CADe-assisted or conventional colonoscopy by experienced endoscopists. Participants with insufficient bowel preparation were excluded from the analysis. The primary outcome was adenoma detection rate (ADR). Secondary outcomes included adenomas per colonoscopy (APC) and sessile serrated lesions (SSLs) per colonoscopy. RESULTS 581 participants were enrolled, of whom 497 were included in the final analysis: 250 in the CADe arm and 247 in the conventional colonoscopy arm. The indication was surveillance in 202/497 colonoscopies (40.6 %), diagnostic in 199/497 (40.0 %), and non-iFOBT screening in 96/497 (19.3 %). Overall, ADR (38.4 % vs. 37.7 %; P = 0.43) and APC (0.66 vs. 0.66; P = 0.97) were similar between CADe and conventional colonoscopy. SSLs per colonoscopy was increased (0.30 vs. 0.19; P = 0.049) in the CADe arm vs. the conventional colonoscopy arm. CONCLUSIONS In this study conducted by experienced endoscopists, CADe did not result in a statistically significant increase in ADR. However, the ADR of our control group substantially surpassed our sample size assumptions, increasing the risk of an underpowered trial.
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Affiliation(s)
- Michiel H. J. Maas
- Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Timo Rath
- Department of Medicine I, Division of Gastroenterology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Cristiano Spada
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elsa Soons
- Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nauzer Forbes
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Sergey Kashin
- Department of Endoscopy, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russia
| | - Paola Cesaro
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Axel Eickhoff
- Gastroenterology, Diabetology, Infectiology, Klinikum Hanau, Hanau, Germany
| | | | - Daniele Salvi
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Peter D. Siersema
- Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
- ErasmusMC – University Medical Center, Rotterdam, the Netherlands
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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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Khouri A, Dickson C, Green A, Hanjar A, Sonnier W. Effect of computer aided detection device on the adenoma detection rate and serrated detection rate among trainee fellows. JGH Open 2024; 8:e70018. [PMID: 39253018 PMCID: PMC11382257 DOI: 10.1002/jgh3.70018] [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: 04/30/2024] [Revised: 07/13/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Abstract
Background and Aims The utilization of artificial intelligence (AI) with computer-aided detection (CADe) has the potential to increase the adenoma detection rate (ADR) by up to 30% in expert settings and specialized centers. The impact of CADe on serrated polyp detection rates (SDR) and academic trainees ADR & SDR remains underexplored. We aim to investigate the effect of CADe on ADR and SDR at an academic center with various levels of providers' experience. Methods A single-center retrospective analysis was conducted on asymptomatic patients between the ages of 45 and 75 who underwent screening colonoscopy. Colonoscopy reports were reviewed for 3 months prior to the introduction of GI Genius™ (Medtronic, USA) and 3 months after its implementation. The primary outcome was ADR and SDR with and without CADe. Results Totally 658 colonoscopies were eligible for analysis. CADe resulted in statistically significant improvement in SDR from 8.92% to 14.1% (P = 0.037). The (ADR + SDR) with CADe and without CADe was 58% and 55.1%, respectively (P = 0.46). Average colonoscopy (CSC) withdrawal time was 17.33 min (SD 10) with the device compared with 17.35 min (SD 9) without the device (P = 0.98). Conclusion In this study, GI Genius™ was associated with a statistically significant increase in SDR alone, but not in ADR or (ADR + SDR), likely secondary to the more elusive nature of serrated polyps compared to adenomatous polyps. The use of CADe did not affect withdrawal time.
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Affiliation(s)
- Anas Khouri
- Department of Internal Medicine and Division of Gastroenterology University of South Alabama Frederick P. Whiddon College of Medicine Mobile Alabama USA
| | - Chance Dickson
- Department of Internal Medicine and Division of Gastroenterology University of South Alabama Frederick P. Whiddon College of Medicine Mobile Alabama USA
| | - Alvin Green
- Department of Internal Medicine and Division of Gastroenterology University of South Alabama Frederick P. Whiddon College of Medicine Mobile Alabama USA
| | - Abrahim Hanjar
- Department of Internal Medicine and Division of Gastroenterology University of South Alabama Frederick P. Whiddon College of Medicine Mobile Alabama USA
| | - William Sonnier
- Department of Internal Medicine and Division of Gastroenterology University of South Alabama Frederick P. Whiddon College of Medicine Mobile Alabama USA
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Bou Jaoude J, Al Bacha R, Abboud B. Will artificial intelligence reach any limit in gastroenterology? Artif Intell Gastroenterol 2024; 5:91336. [DOI: 10.35712/aig.v5.i2.91336] [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: 12/27/2023] [Revised: 04/25/2024] [Accepted: 06/07/2024] [Indexed: 08/08/2024] Open
Abstract
Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.
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Affiliation(s)
- Joseph Bou Jaoude
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Rose Al Bacha
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Bassam Abboud
- Department of General Surgery, Geitaoui Hospital, Faculty of Medicine, Lebanese University, Lebanon, Beirut 166830, Lebanon
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Li J, Peng Z, Wang X, Zhang S, Sun J, Li Y, Zhang Q, Shi L, Li H, Tian Z, Feng Y, Mu J, Tang N, Wang X, Li W, Pei Z. Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5. J Gastroenterol Hepatol 2024; 39:1613-1622. [PMID: 38710592 DOI: 10.1111/jgh.16596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/02/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND AND AIM The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT). METHODS Colonoscopy video data from 472 patients across two hospitals were retrospectively analyzed. WTS was computed through a combination of artificial intelligence (AI) and manual verification. The results obtained through WTS were compared with those generated by the AI system. Patients were categorized into four groups based on the presence of polyps and whether resections or biopsies were performed. Bland Altman plots were utilized to compare AI-computed WTS with manually verified WTS. Scatterplots were used to illustrate WTS within the four groups, among different hospitals, and across various physicians. A parallel box plot was employed to depict the proportions of WTS relative to WT within each of the four groups. RESULTS The study included 472 patients, with a median age of 55 years, and 57.8% were male. A significant correlation with manually verified WTS (r = 0.918) was observed in AI-computed WTS. Significant differences in WTS/WT among the four groups were revealed by the parallel box plot (P < 0.001). The group with no detected polyps had the highest WTS/WT, with a median of 0.69 (interquartile range: 0.40, 0.97). WTS patterns were found to be varied between the two hospitals and among senior and junior physicians. CONCLUSIONS A promising alternative to traditional WT for quality control and training assessment in colonoscopy is offered by AI-assisted computation of WTS.
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Affiliation(s)
- Jiaxin Li
- Medical School, Tianjin University, Tianjin, China
| | - Ziye Peng
- Medical School, Tianjin University, Tianjin, China
| | - Xiangyu Wang
- Medical School, Tianjin University, Tianjin, China
| | - Shuyi Zhang
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Jiayi Sun
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Yanru Li
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Qi Zhang
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Lei Shi
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Hongzhou Li
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Zhenggang Tian
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Yue Feng
- TEDA Yujin Digestive Health Industry Research Institute. Ltd., Tianjin, China
| | - Jinbao Mu
- TEDA Yujin Digestive Health Industry Research Institute. Ltd., Tianjin, China
| | - Nan Tang
- Tianjin Center for Medical Devices Evaluation and Inspection, Tianjin, China
| | - Ximo Wang
- Tianjin Third Central Hospital, Tianjin, China
| | - Wen Li
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Zhengcun Pei
- Medical School, Tianjin University, Tianjin, China
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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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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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Wittbrodt M, Klug M, Etemadi M, Yang A, Pandolfino JE, Keswani RN. Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation. Endosc Int Open 2024; 12:E849-E853. [PMID: 38966321 PMCID: PMC11221895 DOI: 10.1055/a-2333-8138] [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: 04/22/2024] [Accepted: 05/14/2024] [Indexed: 07/06/2024] Open
Abstract
Background and study aims Low-quality colonoscopy increases cancer risk but measuring quality remains challenging. We developed an automated, interactive assessment of colonoscopy quality (AI-CQ) using machine learning (ML). Methods Based on quality guidelines, metrics selected for AI development included insertion time (IT), withdrawal time (WT), polyp detection rate (PDR), and polyps per colonoscopy (PPC). Two novel metrics were also developed: HQ-WT (time during withdrawal with clear image) and WT-PT (withdrawal time subtracting polypectomy time). The model was pre-trained using a self-supervised vision transformer on unlabeled colonoscopy images and then finetuned for multi-label classification on another mutually exclusive colonoscopy image dataset. A timeline of video predictions and metric calculations were presented to clinicians in addition to the raw video using a web-based application. The model was externally validated using 50 colonoscopies at a second hospital. Results The AI-CQ accuracy to identify cecal intubation was 88%. IT ( P = 0.99) and WT ( P = 0.99) were highly correlated between manual and AI-CQ measurements with a median difference of 1.5 seconds and 4.5 seconds, respectively. AI-CQ PDR did not significantly differ from manual PDR (47.6% versus 45.5%, P = 0.66). Retroflexion was correctly identified in 95.2% and number of right colon evaluations in 100% of colonoscopies. HQ-WT was 45.9% of, and significantly correlated with ( P = 0.85) WT time. Conclusions An interactive AI assessment of colonoscopy skill can automatically assess quality. We propose that this tool can be utilized to rapidly identify and train providers in need of remediation.
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Affiliation(s)
| | - Matthew Klug
- Information Services, Northwestern Medicine, Chicago, United States
| | - Mozziyar Etemadi
- Information Services, Northwestern Medicine, Chicago, United States
- Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, United States
| | - Anthony Yang
- Surgery, Indiana University School of Medicine, Indianapolis, United States
| | - John E. Pandolfino
- Medicine, Northwestern University Feinberg School of Medicine, Chicago, United States
| | - Rajesh N. Keswani
- Medicine, Northwestern University Feinberg School of Medicine, Chicago, United States
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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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Desai M, Ausk K, Brannan D, Chhabra R, Chan W, Chiorean M, Gross SA, Girotra M, Haber G, Hogan RB, Jacob B, Jonnalagadda S, Iles-Shih L, Kumar N, Law J, Lee L, Lin O, Mizrahi M, Pacheco P, Parasa S, Phan J, Reeves V, Sethi A, Snell D, Underwood J, Venu N, Visrodia K, Wong A, Winn J, Wright CH, Sharma P. Use of a Novel Artificial Intelligence System Leads to the Detection of Significantly Higher Number of Adenomas During Screening and Surveillance Colonoscopy: Results From a Large, Prospective, US Multicenter, Randomized Clinical Trial. Am J Gastroenterol 2024; 119:1383-1391. [PMID: 38235741 DOI: 10.14309/ajg.0000000000002664] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/14/2023] [Indexed: 01/19/2024]
Abstract
INTRODUCTION Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel artificial intelligence (AI) system, compared with standard high-definition colonoscopy, for APC measurement. METHODS This was a US-based, multicenter, prospective randomized trial examining a novel AI detection system (EW10-EC02) that enables a real-time colorectal polyp detection enabled with the colonoscope (CAD-EYE). Eligible average-risk subjects (45 years or older) undergoing screening or surveillance colonoscopy were randomized to undergo either CAD-EYE-assisted colonoscopy (CAC) or conventional colonoscopy (CC). Modified intention-to-treat analysis was performed for all patients who completed colonoscopy with the primary outcome of APC. Secondary outcomes included positive predictive value (total number of adenomas divided by total polyps removed) and adenoma detection rate. RESULTS In modified intention-to-treat analysis, of 1,031 subjects (age: 59.1 ± 9.8 years; 49.9% male), 510 underwent CAC vs 523 underwent CC with no significant differences in age, gender, ethnicity, or colonoscopy indication between the 2 groups. CAC led to a significantly higher APC compared with CC: 0.99 ± 1.6 vs 0.85 ± 1.5, P = 0.02, incidence rate ratio 1.17 (1.03-1.33, P = 0.02) with no significant difference in the withdrawal time: 11.28 ± 4.59 minutes vs 10.8 ± 4.81 minutes; P = 0.11 between the 2 groups. Difference in positive predictive value of a polyp being an adenoma among CAC and CC was less than 10% threshold established: 48.6% vs 54%, 95% CI -9.56% to -1.48%. There were no significant differences in adenoma detection rate (46.9% vs 42.8%), advanced adenoma (6.5% vs 6.3%), sessile serrated lesion detection rate (12.9% vs 10.1%), and polyp detection rate (63.9% vs 59.3%) between the 2 groups. There was a higher polyp per colonoscopy with CAC compared with CC: 1.68 ± 2.1 vs 1.33 ± 1.8 (incidence rate ratio 1.27; 1.15-1.4; P < 0.01). DISCUSSION Use of a novel AI detection system showed to a significantly higher number of adenomas per colonoscopy compared with conventional high-definition colonoscopy without any increase in colonoscopy withdrawal time, thus supporting the use of AI-assisted colonoscopy to improve colonoscopy quality ( ClinicalTrials.gov NCT04979962).
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Affiliation(s)
- Madhav Desai
- Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, Missouri, USA
| | - Karlee Ausk
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Donald Brannan
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Rajiv Chhabra
- Department of Gastroenterology, Saint Luke's Hospital of Kansas City, Kansas City, Missouri, USA
| | - Walter Chan
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Michael Chiorean
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Seth A Gross
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - Mohit Girotra
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Gregory Haber
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - Reed B Hogan
- GI Associates and Endoscopy Center, Jackson, Mississippi, USA
| | - Bobby Jacob
- Gastroenterology, Largo Medical Center, Largo, Florida, USA
| | - Sreeni Jonnalagadda
- Department of Gastroenterology, Saint Luke's Hospital of Kansas City, Kansas City, Missouri, USA
| | - Lulu Iles-Shih
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Navin Kumar
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joanna Law
- Gastroenterology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Linda Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Otto Lin
- Gastroenterology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Meir Mizrahi
- Gastroenterology, Largo Medical Center, Largo, Florida, USA
| | - Paulo Pacheco
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - Sravanthi Parasa
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Jennifer Phan
- Departement of Gastroenterology, Keck Medicine University of Southern California, Los Angeles, California, USA
| | - Vonda Reeves
- GI Associates and Endoscopy Center, Jackson, Mississippi, USA
| | - Amrita Sethi
- Department of Gastroenterology, Columbia University Irving Medical Center, New York, New York, USA
| | - David Snell
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - James Underwood
- GI Associates and Endoscopy Center, Jackson, Mississippi, USA
| | - Nanda Venu
- Gastroenterology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Kavel Visrodia
- Department of Gastroenterology, Columbia University Irving Medical Center, New York, New York, USA
| | - Alina Wong
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Jessica Winn
- Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, Missouri, USA
| | | | - Prateek Sharma
- Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, Missouri, USA
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Coban S, Zahid KS, Brugge WR. The future of EUS. ENDOSCOPIC ULTRASONOGRAPHY 2024:287-293. [DOI: 10.1002/9781119697893.ch31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]
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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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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Rey JF. As how artificial intelligence is revolutionizing endoscopy. Clin Endosc 2024; 57:302-308. [PMID: 38454543 PMCID: PMC11133999 DOI: 10.5946/ce.2023.230] [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: 09/13/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 03/09/2024] Open
Abstract
With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.
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Affiliation(s)
- Jean-Francois Rey
- Institut Arnaut Tzanck Gastrointestinal Unt, Saint Laurent du Var, France
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Sullivan BA, Lieberman DA. Colon Polyp Surveillance: Separating the Wheat From the Chaff. Gastroenterology 2024; 166:743-757. [PMID: 38224860 DOI: 10.1053/j.gastro.2023.11.305] [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: 01/30/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 01/17/2024]
Abstract
One goal of colorectal cancer (CRC) screening is to prevent CRC incidence by removing precancerous colonic polyps, which are detected in up to 50% of screening examinations. Yet, the lifetime risk of CRC is 3.9%-4.3%, so it is clear that most of these individuals with polyps would not develop CRC in their lifetime. It is, therefore, a challenge to determine which individuals with polyps will benefit from follow-up, and at what intervals. There is some evidence that individuals with advanced polyps, based on size and histology, benefit from intensive surveillance. However, a large proportion of individuals will have small polyps without advanced histologic features (ie, "nonadvanced"), where the benefits of surveillance are uncertain and controversial. Demand for surveillance will further increase as more polyps are detected due to increased screening uptake, recent United States recommendations to expand screening to younger individuals, and emergence of polyp detection technology. We review the current understanding and clinical implications of the natural history, biology, and outcomes associated with various categories of colon polyps based on size, histology, and number. Our aims are to highlight key knowledge gaps, specifically focusing on certain categories of polyps that may not be associated with future CRC risk, and to provide insights to inform research priorities and potential management strategies. Optimization of CRC prevention programs based on updated knowledge about the future risks associated with various colon polyps is essential to ensure cost-effective screening and surveillance, wise use of resources, and inform efforts to personalize recommendations.
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Affiliation(s)
- Brian A Sullivan
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, North Carolina; Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina.
| | - David A Lieberman
- Portland Veteran Affairs Medical Center, Portland, Oregon; Division of Gastroenterology and Hepatology, School of Medicine, Oregon Health and Science University, Portland, Oregon
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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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Gangwani MK, Haghbin H, Ishtiaq R, Hasan F, Dillard J, Jaber F, Dahiya DS, Ali H, Salim S, Lee-Smith W, Sohail AH, Inamdar S, Aziz M, Hart B. Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis. Dig Dis Sci 2024; 69:1380-1388. [PMID: 38436866 PMCID: PMC11026252 DOI: 10.1007/s10620-024-08341-9] [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: 01/23/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND AND AIMS Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR. METHODS We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant. RESULTS We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer. CONCLUSION Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.
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Affiliation(s)
| | - Hossein Haghbin
- Department of Gastroenterology and Hepatology, Ascension Providence Hospital, Southfield, MI, USA
| | - Rizwan Ishtiaq
- Department of Medicine, St Francis Hospital and Medical Center, Hartford, CT, USA
| | - Fariha Hasan
- Department of Internal Medicine, Cooper University Hospital, Camden, NJ, USA
| | - Julia Dillard
- Department of Medicine, University of Toledo Medical Center, Toledo, OH, USA
| | - Fouad Jaber
- Department of Internal Medicine, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Dushyant Singh Dahiya
- Department of Medicine, Central Michigan University College of Medicine, Mount Pleasant, MI, USA
| | - Hassam Ali
- Department of Gastroenterology and Hepatology, East Carolina University Health, Greenville, NC, USA
| | - Shaharyar Salim
- Department of Internal Medicine, Aga Khan University Hospital, Karachi, Pakistan
| | - Wade Lee-Smith
- University of Toledo Libraries, University of Toledo, Toledo, OH, USA
| | - Amir Humza Sohail
- Department of General Surgery, New York University Langone Health, Long Island, NY, USA
| | - Sumant Inamdar
- Department of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, Toledo, OH, USA
| | - Benjamin Hart
- Depertment of Hepatology and Gastroenterology, University of Michigan, Ann Arbor, MI, USA
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Keswani RN, Thakkar U, Sals A, Pandolfino JE. A Computer-Aided Detection (CADe) System Significantly Improves Polyp Detection in Routine Practice. Clin Gastroenterol Hepatol 2024; 22:893-895.e1. [PMID: 37741303 DOI: 10.1016/j.cgh.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023]
Affiliation(s)
- Rajesh N Keswani
- Division of Gastroenterology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Digestive Health Center, Northwestern Medicine, Chicago, Illinois.
| | - Urvi Thakkar
- Digestive Health Center, Northwestern Medicine, Chicago, Illinois
| | - Alexandra Sals
- Digestive Health Center, Northwestern Medicine, Chicago, Illinois
| | - John E Pandolfino
- Division of Gastroenterology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Digestive Health Center, Northwestern Medicine, Chicago, Illinois
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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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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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Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, di Ruffano LF, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Rivera SC, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat Commun 2024; 15:1619. [PMID: 38388497 PMCID: PMC10883966 DOI: 10.1038/s41467-024-45355-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Affiliation(s)
| | - Carrie D Llewellyn
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Richard O de Visser
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Benjamin Ng
- Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK
- Christ Church, University of Oxford, Oxford, UK
| | - Victoria Ngai
- University College London Medical School, London, UK
| | - Aditya U Kale
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
| | | | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Gary S Collins
- Centre for Statistics in Medicine//UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottowa, Canada
| | - Melissa D McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Centre for Research & Learning, Toronto, Canada
- Division of Clinical and Public Health, Dalla Lana School of Public Health, Toronto, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Melanie Calvert
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | | | | | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Hospital. University of Toronto, Toronto, Canada
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard. T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alastair K Denniston
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
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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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Abstract
PURPOSE OF REVIEW Colon polyps are potential precursors to colorectal cancer (CRC), which remains one of the most common causes of cancer-associated death. The proper identification and management of these colorectal polyps is an important quality measure for colonoscopy outcomes. Here, we review colon polyp epidemiology, their natural history, and updates in endoscopic classification and management. RECENT FINDINGS Colon polyps that form from not only the adenoma, but also the serrated polyp pathway have significant risk for future progression to CRC. Therefore, correct identification and management of sessile serrated lesions can improve the quality of screening colonoscopy. Malignant polyp recognition continues to be heavily reliant on well established endoscopic classification systems and plays an important role in intraprocedural management decisions. Hot snare remains the gold standard for pedunculated polyp resection. Nonpedunculated noninvasive lesions can be effectively removed by large forceps if diminutive, but cold snare is preferred for colon polyps 3-20 mm in diameter. Larger lesions at least 20 mm require endoscopic mucosal resection. Polyps with the endoscopic appearance of submucosal invasion require surgical referral or advanced endoscopic resection in select cases. Advances in artificial intelligence may revolutionize endoscopic polyp classification and improve both patient and cost-related outcomes of colonoscopy. SUMMARY Clinicians should be aware of the most recent updates in colon polyp classification and management to provide the best care to their patients initiating screening colonoscopy.
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Affiliation(s)
- David Dornblaser
- Division of Gastroenterology, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
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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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50
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Samarasena J, Yang D, Berzin TM. AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary. Gastroenterology 2023; 165:1568-1573. [PMID: 37855759 DOI: 10.1053/j.gastro.2023.07.010] [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: 01/23/2023] [Revised: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 10/20/2023]
Abstract
DESCRIPTION The purpose of this American Gastroenterological Association (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide expert commentary on the current landscape of artificial intelligence in the evaluation and management of colorectal polyps. METHODS This CPU was commissioned and approved by the AGA Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. This Expert Commentary incorporates important as well as recently published studies in this field, and it reflects the experiences of the authors who are experienced endoscopists with expertise in the field of artificial intelligence and colorectal polyps.
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Affiliation(s)
- Jason Samarasena
- Division of Gastroenterology, University of California Irvine, Orange, California
| | - Dennis Yang
- Center for Interventional Endoscopy, AdventHealth, Orlando, Florida.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
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