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Dinis-Ribeiro M, Libânio D, Uchima H, Spaander MCW, Bornschein J, Matysiak-Budnik T, Tziatzios G, Santos-Antunes J, Areia M, Chapelle N, Esposito G, Fernandez-Esparrach G, Kunovsky L, Garrido M, Tacheci I, Link A, Marcos P, Marcos-Pinto R, Moreira L, Pereira AC, Pimentel-Nunes P, Romanczyk M, Fontes F, Hassan C, Bisschops R, Feakins R, Schulz C, Triantafyllou K, Carneiro F, Kuipers EJ. Management of epithelial precancerous conditions and early neoplasia of the stomach (MAPS III): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG) and European Society of Pathology (ESP) Guideline update 2025. Endoscopy 2025; 57:504-554. [PMID: 40112834 DOI: 10.1055/a-2529-5025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/22/2025]
Abstract
At a population level, the European Society of Gastrointestinal Endoscopy (ESGE), the European Helicobacter and Microbiota Study Group (EHMSG), and the European Society of Pathology (ESP) suggest endoscopic screening for gastric cancer (and precancerous conditions) in high-risk regions (age-standardized rate [ASR] > 20 per 100 000 person-years) every 2 to 3 years or, if cost-effectiveness has been proven, in intermediate risk regions (ASR 10-20 per 100 000 person-years) every 5 years, but not in low-risk regions (ASR < 10).ESGE/EHMSG/ESP recommend that irrespective of country of origin, individual gastric risk assessment and stratification of precancerous conditions is recommended for first-time gastroscopy. ESGE/EHMSG/ESP suggest that gastric cancer screening or surveillance in asymptomatic individuals over 80 should be discontinued or not started, and that patients' comorbidities should be considered when treatment of superficial lesions is planned.ESGE/EHMSG/ESP recommend that a high quality endoscopy including the use of virtual chromoendoscopy (VCE), after proper training, is performed for screening, diagnosis, and staging of precancerous conditions (atrophy and intestinal metaplasia) and lesions (dysplasia or cancer), as well as after endoscopic therapy. VCE should be used to guide the sampling site for biopsies in the case of suspected neoplastic lesions as well as to guide biopsies for diagnosis and staging of gastric precancerous conditions, with random biopsies to be taken in the absence of endoscopically suspected changes. When there is a suspected early gastric neoplastic lesion, it should be properly described (location, size, Paris classification, vascular and mucosal pattern), photodocumented, and two targeted biopsies taken.ESGE/EHMSG/ESP do not recommend routine performance of endoscopic ultrasonography (EUS), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET)-CT prior to endoscopic resection unless there are signs of deep submucosal invasion or if the lesion is not considered suitable for endoscopic resection.ESGE/EHMSG/ESP recommend endoscopic submucosal dissection (ESD) for differentiated gastric lesions clinically staged as dysplastic (low grade and high grade) or as intramucosal carcinoma (of any size if not ulcerated or ≤ 30 mm if ulcerated), with EMR being an alternative for Paris 0-IIa lesions of size ≤ 10 mm with low likelihood of malignancy.ESGE/EHMSG/ESP suggest that a decision about ESD can be considered for malignant lesions clinically staged as having minimal submucosal invasion if differentiated and ≤ 30 mm; or for malignant lesions clinically staged as intramucosal, undifferentiated and ≤ 20 mm; and in both cases with no ulcerative findings.ESGE/EHMSG/ESP recommends patient management based on the following histological risk after endoscopic resection: Curative/very low-risk resection (lymph node metastasis [LNM] risk < 0.5 %-1 %): en bloc R0 resection; dysplastic/pT1a, differentiated lesion, no lymphovascular invasion, independent of size if no ulceration and ≤ 30 mm if ulcerated. No further staging procedure or treatment is recommended.Curative/low-risk resection (LNM risk < 3 %): en bloc R0 resection; lesion with no lymphovascular invasion and: a) pT1b, invasion ≤ 500 µm, differentiated, size ≤ 30 mm; or b) pT1a, undifferentiated, size ≤ 20 mm and no ulceration. Staging should be completed, and further treatment is generally not necessary, but a multidisciplinary discussion is required. Local-risk resection (very low risk of LNM but increased risk of local persistence/recurrence): Piecemeal resection or tumor-positive horizontal margin of a lesion otherwise meeting curative/very low-risk criteria (or meeting low-risk criteria provided that there is no submucosal invasive tumor at the resection margin in the case of piecemeal resection or tumor-positive horizontal margin for pT1b lesions [invasion ≤ 500 µm; well-differentiated; size ≤ 30 mm, and VM0]). Endoscopic surveillance/re-treatment is recommended rather than other additional treatment. High-risk resection (noncurative): Any lesion with any of the following: (a) a positive vertical margin (if carcinoma) or lymphovascular invasion or deep submucosal invasion (> 500 µm from the muscularis mucosae); (b) poorly differentiated lesions if ulceration or size > 20 mm; (c) pT1b differentiated lesions with submucosal invasion ≤ 500 µm with size > 30 mm; or (d) intramucosal ulcerative lesion with size > 30 mm. Complete staging and strong consideration for additional treatments (surgery) in multidisciplinary discussion.ESGE/EHMSG/ESP suggest the use of validated endoscopic classifications of atrophy (e. g. Kimura-Takemoto) or intestinal metaplasia (e. g. endoscopic grading of gastric intestinal metaplasia [EGGIM]) to endoscopically stage precancerous conditions and stratify the risk for gastric cancer.ESGE/EHMSG/ESP recommend that biopsies should be taken from at least two topographic sites (2 biopsies from the antrum/incisura and 2 from the corpus, guided by VCE) in two separate, clearly labeled vials. Additional biopsy from the incisura is optional.ESGE/EHMSG/ESP recommend that patients with extensive endoscopic changes (Kimura C3 + or EGGIM 5 +) or advanced histological stages of atrophic gastritis (severe atrophic changes or intestinal metaplasia, or changes in both antrum and corpus, operative link on gastritis assessment/operative link on gastric intestinal metaplasia [OLGA/OLGIM] III/IV) should be followed up with high quality endoscopy every 3 years, irrespective of the individual's country of origin.ESGE/EHMSG/ESP recommend that no surveillance is proposed for patients with mild to moderate atrophy or intestinal metaplasia restricted to the antrum, in the absence of endoscopic signs of extensive lesions or other risk factors (family history, incomplete intestinal metaplasia, persistent H. pylori infection). This group constitutes most individuals found in clinical practice.ESGE/EHMSG/ESP recommend H. pylori eradication for patients with precancerous conditions and after endoscopic or surgical therapy.ESGE/EHMSG/ESP recommend that patients should be advised to stop smoking and low-dose daily aspirin use may be considered for the prevention of gastric cancer in selected individuals with high risk for cardiovascular events.
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Affiliation(s)
- Mário Dinis-Ribeiro
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), Porto, Portugal
- Gastroenterology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal
| | - Diogo Libânio
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), Porto, Portugal
- Gastroenterology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal
| | - Hugo Uchima
- Endoscopy Unit Gastroenterology Department Hospital Universitari Germans Trias i Pujol, Badalona, Spain
- Endoscopy Unit, Teknon Medical Center, Barcelona, Spain
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jan Bornschein
- Medical Research Council Translational Immune Discovery Unit (MRC TIDU), Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Translational Gastroenterology and Liver Unit, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Tamara Matysiak-Budnik
- Department of Hepato-Gastroenterology & Digestive Oncology, Institut des Maladies de l'Appareil Digestif, Centre Hospitalier Universitaire de Nantes Nantes, France
- INSERM, Center for Research in Transplantation and Translational Immunology, University of Nantes, Nantes, France
| | - Georgios Tziatzios
- Agia Olga General Hospital of Nea Ionia Konstantopouleio, Athens, Greece
| | - João Santos-Antunes
- Gastroenterology Department, Centro Hospitalar S. João, Porto, Portugal
- Faculty of Medicine, University of Porto, Portugal
- University of Porto, Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Instituto de Investigação e Inovação na Saúde (I3S), Porto, Portugal
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra (IPO Coimbra), Coimbra, Portugal
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), RISE@CI-IPO, (Health Research Network), Portuguese Institute of Oncology of Porto (IPO Porto), Porto, Portugal
| | - Nicolas Chapelle
- Department of Hepato-Gastroenterology & Digestive Oncology, Institut des Maladies de l'Appareil Digestif, Centre Hospitalier Universitaire de Nantes Nantes, France
- INSERM, Center for Research in Transplantation and Translational Immunology, University of Nantes, Nantes, France
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Italy
| | - Gloria Fernandez-Esparrach
- Gastroenterology Department, ICMDM, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
- Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
- Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Spain
| | - Lumir Kunovsky
- 2nd Department of Internal Medicine - Gastroenterology and Geriatrics, University Hospital Olomouc, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
- Department of Surgery, University Hospital Brno, Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Gastroenterology and Digestive Endoscopy, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Mónica Garrido
- Gastroenterology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal
| | - Ilja Tacheci
- Gastroenterology, Second Department of Internal Medicine, University Hospital Hradec Kralove, Faculty of Medicine in Hradec Kralove, Charles University of Prague, Czech Republic
| | | | - Pedro Marcos
- Department of Gastroenterology, Pêro da Covilhã Hospital, Covilhã, Portugal
- Department of Medical Sciences, Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal
| | - Ricardo Marcos-Pinto
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), RISE@CI-IPO, (Health Research Network), Portuguese Institute of Oncology of Porto (IPO Porto), Porto, Portugal
- Gastroenterology Department, Centro Hospitalar do Porto, Porto, Portugal
- Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto, Portugal
| | - Leticia Moreira
- Gastroenterology Department, ICMDM, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Spain
| | - Ana Carina Pereira
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), Porto, Portugal
| | - Pedro Pimentel-Nunes
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), RISE@CI-IPO, (Health Research Network), Portuguese Institute of Oncology of Porto (IPO Porto), Porto, Portugal
- Department of Surgery and Physiology, Faculty of Medicine, University of Porto (FMUP), Portugal
- Gastroenterology and Clinical Research, Unilabs Portugal
| | - Marcin Romanczyk
- Department of Gastroenterology, Faculty of Medicine, Academy of Silesia, Katowice, Poland
- Endoterapia, H-T. Centrum Medyczne, Tychy, Poland
| | - Filipa Fontes
- Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), Porto, Portugal
- Public Health and Forensic Sciences, and Medical Education Department, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Department of Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium
| | - Roger Feakins
- Department of Cellular Pathology, Royal Free London NHS Foundation Trust, London, United Kingdom
- University College London, London, United Kingdom
| | - Christian Schulz
- Department of Medicine II, University Hospital, LMU Munich, Germany
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Internal Medicine-Propaedeutic, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece
| | - Fatima Carneiro
- Institute of Molecular Pathology and Immunology at the University of Porto (IPATIMUP), Porto, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal
- Pathology Department, Centro Hospitalar de São João and Faculty of Medicine, Porto, Portugal
| | - Ernst J Kuipers
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Gao Y, Wen P, Liu Y, Sun Y, Qian H, Zhang X, Peng H, Gao Y, Li C, Gu Z, Zeng H, Hong Z, Wang W, Yan R, Hu Z, Fu H. Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology. J Transl Med 2025; 23:412. [PMID: 40205603 PMCID: PMC11983949 DOI: 10.1186/s12967-025-06428-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2024] [Accepted: 03/25/2025] [Indexed: 04/11/2025] Open
Abstract
BACKGROUND Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical early symptoms, causing patients to miss optimal treatment opportunities. Traditional endoscopic and pathological diagnostic processes are highly dependent on expert experience, facing problems such as high misdiagnosis rates and significant inter-observer variations. With the development of artificial intelligence (AI) technologies such as deep learning, real-time lesion detection with endoscopic assistance and automated pathological image analysis have shown potential in improving diagnostic accuracy and efficiency. However, relevant applications still face challenges including insufficient data standardization, inadequate interpretability, and weak clinical validation. OBJECTIVE This study aims to systematically review the current applications of artificial intelligence in diagnosing malignant digestive tract tumors, focusing on the progress and bottlenecks in two key areas: endoscopic examination and pathological diagnosis, and to provide feasible ideas and suggestions for subsequent research and clinical translation. METHODS A systematic literature search strategy was adopted to screen relevant studies published between 2017 and 2024 from databases including PubMed, Web of Science, Scopus, and IEEE Xplore, supplemented with searches of early classical literature. Inclusion criteria included studies on malignant digestive tract tumors such as esophageal cancer, gastric cancer, or colorectal cancer, involving the application of artificial intelligence technology in endoscopic diagnosis or pathological analysis. The effects and main limitations of AI diagnosis were summarized through comprehensive analysis of research design, algorithmic methods, and experimental results from relevant literature. RESULTS In the field of endoscopy, multiple deep learning models have significantly improved detection rates in real-time polyp detection, early gastric cancer, and esophageal cancer screening, with some commercialized systems successfully entering clinical trials. However, the scale and quality of data across different studies vary widely, and the generalizability of models to multi-center, multi-device environments remains to be verified. In pathological analysis, using convolutional neural networks, multimodal pre-training models, etc., automatic tissue segmentation, tumor grading, and assisted diagnosis can be achieved, showing good scalability in interactive question-answering. Nevertheless, clinical implementation still faces obstacles such as non-uniform data standards, lack of large-scale prospective validation, and insufficient model interpretability and continuous learning mechanisms. CONCLUSION Artificial intelligence provides new technological opportunities for endoscopic and pathological diagnosis of malignant digestive tract tumors, achieving positive results in early lesion identification and assisted decision-making. However, to achieve the transition from research to widespread clinical application, data standardization, model reliability, and interpretability still need to be improved through multi-center joint research, and a complete regulatory and ethical system needs to be established. In the future, artificial intelligence will play a more important role in the standardization and precision management of diagnosis and treatment of digestive tract tumors.
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Affiliation(s)
- Yinhu Gao
- Department of Gastroenterology, Shaanxi Province Rehabilitation Hospital, Xi'an, Shaanxi, China
| | - Peizhen Wen
- Department of General Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China.
| | - Yuan Liu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yahuang Sun
- Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China
| | - Hui Qian
- Department of Gastroenterology, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China
| | - Xin Zhang
- Department of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China
| | - Huan Peng
- Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China
| | - Yanli Gao
- Infection Control Office, Shaanxi Province Rehabilitation Hospital, Xi'an, Shaanxi, China
| | - Cuiyu Li
- Department of Radiology, The First Hospital of Nanchang, the Third Affiliated Hospital of Nanchang University, Nanchang, 330008, Jiangxi, China
| | - Zhangyuan Gu
- Tongji University School of Medicine, Tongji University, Shanghai, 200092, People's Republic of China
| | - Huajin Zeng
- Department of General Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China
| | - Zhijun Hong
- Tongji University School of Medicine, Tongji University, Shanghai, 200092, People's Republic of China
| | - Weijun Wang
- Department of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China.
| | - Ronglin Yan
- Department of Gastroenterology, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China.
| | - Zunqi Hu
- Department of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China.
| | - Hongbing Fu
- Department of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical University, 415 Fengyang Road, Shanghai, 200003, China.
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Tao J, Zhang Z, Meng L, Zhang L, Wang J, Li Z. Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features. Front Oncol 2025; 15:1554523. [PMID: 40255428 PMCID: PMC12006015 DOI: 10.3389/fonc.2025.1554523] [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: 01/02/2025] [Accepted: 03/13/2025] [Indexed: 04/22/2025] Open
Abstract
Background This study aimed to construct and validate diagnostic models for the Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) staging systems using three different methodologies based on magnifying endoscopy with narrow-band imaging (ME-NBI) features, to evaluate model performance, and to analyse risk factors for high-risk OLGA/OLGIM stages. Methods We enrolled 356 patients who underwent white-light endoscopy and ME-NBI at the Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, between January 2022 and September 2023. Clinical data were recorded. Chi-square or Fisher's exact tests were used to analyse differences in endoscopic features between OLGA/OLGIM stages. Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. Model accuracy, area under the ROC curve (AUC), sensitivity, and specificity were calculated for comprehensive validation. Results All three models demonstrated excellent diagnostic performance, with random forest and XGBoost models showing marginally superior accuracy, AUC values, and sensitivity compared with the Bayesian stepwise discrimination model. For OLGA staging, the AUC values were 0.928, 0.958, and 0.966, with accuracies of 0.854, 0.902, and 0.918 for Bayesian, random forest, and XGBoost models, respectively. For OLGIM staging, the corresponding AUC values were 0.924, 0.975, and 0.979, with accuracies of 0.910, 0.938, and 0.927. Risk factors for high-risk OLGA included lesion location (subcardial and lower body greater curvature), intestinal metaplasia patches, lesion size, demarcation line (DL), and margin regularity of micro-capillary demarcation line (MCDL). Risk factors for high-risk OLGIM included Helicobacter pylori infection status, mucosal condition, lesion location (lesser curvature and lower body greater curvature), erosion, lesion size, DL, vessel and epithelial classification (VEC), white globe appearance (WGA), and MCDL margin regularity. Conclusions All three models demonstrated robust accuracy and predictive capability, confirming that conventional white-light endoscopy combined with ME-NBI features provides valuable diagnostic reference for clinical risk assessment of precancerous gastric lesions.
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Affiliation(s)
- Jingna Tao
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Zhongmian Zhang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Linghan Meng
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Liju Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Jiaqi Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Zhihong Li
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
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Enslin S, Kaul V. Past, Present, and Future: A History Lesson in Artificial Intelligence. Gastrointest Endosc Clin N Am 2025; 35:265-278. [PMID: 40021228 DOI: 10.1016/j.giec.2024.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning systems, the impact of AI on various fields, including medicine, has been profound. In gastroenterology, AI-driven computer-aided detection and computer-aided diagnosis systems have revolutionized endoscopy, imaging, and pathology detection. The future promises further advancements in diagnostic precision, personalized treatment, and clinical research. However, challenges such as transparency, liability, and ethical concerns must be addressed. By fostering collaboration, robust governance and development of quality metrics, AI can be leveraged to enhance patient care and advance scientific knowledge.
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Affiliation(s)
- Sarah Enslin
- Division of Gastroenterology and Hepatology, Center for Advanced Therapeutic Endoscopy, University of Rochester Medical Center, 601 Elmwood Avenue, Box 646, Rochester, NY 14642, USA
| | - Vivek Kaul
- Division of Gastroenterology and Hepatology, Center for Advanced Therapeutic Endoscopy, University of Rochester Medical Center, 601 Elmwood Avenue, Box 646, Rochester, NY 14642, USA.
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Nathani P, Sharma P. Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach. Gastrointest Endosc Clin N Am 2025; 35:319-353. [PMID: 40021232 DOI: 10.1016/j.giec.2024.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.
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Affiliation(s)
- Piyush Nathani
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA.
| | - Prateek Sharma
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA; Kansas City Veteran Affairs Medical Center, Kansas City, MO, USA
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Ebigbo A, Messmann H, Lee SH. Artificial Intelligence Applications in Image-Based Diagnosis of Early Esophageal and Gastric Neoplasms. Gastroenterology 2025:S0016-5085(25)00471-8. [PMID: 40043857 DOI: 10.1053/j.gastro.2025.01.253] [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: 11/15/2024] [Revised: 01/14/2025] [Accepted: 01/22/2025] [Indexed: 04/03/2025]
Abstract
Artificial intelligence (AI) holds the potential to transform the management of upper gastrointestinal (GI) conditions, such as Barrett's esophagus, esophageal squamous cell cancer, and early gastric cancer. Advancements in deep learning and convolutional neural networks offer improved diagnostic accuracy and reduced diagnostic variability across different clinical settings, particularly where human error or fatigue may impair diagnostic precision. Deep learning models have shown the potential to improve early cancer detection and lesion characterization, predict invasion depth, and delineate lesion margins with remarkable accuracy, all contributing to effective treatment planning. Several challenges, however, limit the broad application of AI in GI endoscopy, particularly in the upper GI tract. Subtle lesion morphology and restricted diversity in training datasets, which are often sourced from specialized centers, may constrain the generalizability of AI models in various clinical settings. Furthermore, the "black box" nature of some AI systems can impede explainability and clinician trust. To address these issues, efforts are underway to incorporate multimodal data, such as combining endoscopic and histopathologic imaging, to bolster model robustness and transparency. In the future, AI promises substantial advancements in automated real-time endoscopic guidance, personalized risk assessment, and optimized biopsy decision making. As it evolves, it would substantially impact not only early diagnosis and prognosis, but also the cost-effectiveness of managing upper GI diseases, ultimately leading to improved patient outcomes and more efficient health care delivery.
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Affiliation(s)
- Alanna Ebigbo
- Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany.
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany.
| | - Sung Hak Lee
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Seoul St. Mary's Hospital, Seoul, South Korea.
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Okamoto H, Cap QH, Nomura T, Nabeshima K, Hashimoto J, Iyatomi H. Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training. Artif Intell Med 2025; 161:103075. [PMID: 39919469 DOI: 10.1016/j.artmed.2025.103075] [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: 10/02/2023] [Revised: 12/16/2024] [Accepted: 01/27/2025] [Indexed: 02/09/2025]
Abstract
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thus allowing a much larger number of patients to undergo imaging. However, the diagnosis of X-ray images relies heavily on the expertise and experience of physicians, and few machine learning methods have been developed to assist in this process. We propose a novel and practical gastric cancer diagnostic support system for gastric X-ray images that will enable more people to be screened. The system is based on a general deep learning-based object detection model and incorporates two novel techniques: refined probabilistic stomach image augmentation (R-sGAIA) and hard boundary box training (HBBT). R-sGAIA enhances the probabilistic gastric fold region and provides more learning patterns for cancer detection models. HBBT is an efficient training method that improves model performance by allowing the use of unannotated negative (i.e., healthy control) samples, which are typically unusable in conventional detection models. The proposed system achieved a sensitivity (SE) for gastric cancer of 90.2%, higher than that of an expert (85.5%). Under these conditions, two out of five candidate boxes identified by the system were cancerous (precision = 42.5%), with an image processing speed of 0.51 s per image. The system also outperformed methods using the same object detection model and state-of-the-art data augmentation by showing a 5.9-point improvement in the F1 score. In summary, this system efficiently identifies areas for radiologists to examine within a practical time frame, thus significantly reducing their workload.
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Affiliation(s)
- Hideaki Okamoto
- Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajino, Koganei, 184-8584, Tokyo, Japan
| | - Quan Huu Cap
- Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajino, Koganei, 184-8584, Tokyo, Japan; AI Development Department, Aillis Inc., 2-2-1 Yaesu, Chuo, 104-0028, Tokyo, Japan
| | - Takakiyo Nomura
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Kanagawa, Japan
| | - Kazuhito Nabeshima
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Kanagawa, Japan
| | - Jun Hashimoto
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Kanagawa, Japan
| | - Hitoshi Iyatomi
- Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajino, Koganei, 184-8584, Tokyo, Japan.
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8
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Yin K, Liang H, Guo W, Chen YX, Cui ML, Zhang MX. Artificial intelligence and early cancer of the digestive tract: New challenges and new futures. Shijie Huaren Xiaohua Zazhi 2025; 33:1-10. [DOI: 10.11569/wcjd.v33.i1.1] [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: 10/14/2024] [Revised: 11/06/2024] [Accepted: 11/21/2024] [Indexed: 01/22/2025] Open
Abstract
Early gastrointestinal tumors have a good prognosis, but they have insidious onset and no specific manifestations, making their diagnosis difficult. With the rapid development of artificial intelligence technology in the medical field, it has shown great potential in clinical work such as diagnosis and prognosis prediction of early gastrointestinal cancer. In this paper, we systematically review the relevant studies on AI in early esophageal cancer, early gastric cancer, early colon cancer, and hepatobiliary pancreatic cancer, and discuss the challenges and futures of AI application in early gastrointestinal cancer.
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Affiliation(s)
- Kun Yin
- Xi'an Medical College, Xi'an 710021, Shaanxi Province, China
| | - Hao Liang
- Xi'an Medical College, Xi'an 710021, Shaanxi Province, China
| | - Wen Guo
- Xi'an Medical College, Xi'an 710021, Shaanxi Province, China
| | - Ya-Xin Chen
- Xi'an Medical College, Xi'an 710021, Shaanxi Province, China
| | - Man-Li Cui
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Medical College, Xi'an 710077, Shaanxi Province, China
| | - Ming-Xin Zhang
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Medical College, Xi'an 710077, Shaanxi Province, China
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9
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Li S, Xu M, Meng Y, Sun H, Zhang T, Yang H, Li Y, Ma X. The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors. MEDCOMM – ONCOLOGY 2024; 3. [DOI: 10.1002/mog2.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 09/03/2024] [Indexed: 01/04/2025]
Abstract
AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.
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Affiliation(s)
- Shen Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Maosen Xu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, West China Hospital, National Clinical Research, Sichuan University Chengdu Sichuan China
| | - Yuanling Meng
- West China School of Stomatology Sichuan University Chengdu Sichuan China
| | - Haozhen Sun
- College of Life Sciences Sichuan University Chengdu Sichuan China
| | - Tao Zhang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Hanle Yang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Yueyi Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Xuelei Ma
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
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10
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Labaki C, Uche-Anya EN, Berzin TM. Artificial Intelligence in Gastrointestinal Endoscopy. Gastroenterol Clin North Am 2024; 53:773-786. [PMID: 39489586 DOI: 10.1016/j.gtc.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2024]
Abstract
Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.
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Affiliation(s)
- Chris Labaki
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 300 Brookline Avenue, Boston, MA, USA
| | - Eugenia N Uche-Anya
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA.
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11
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Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. Artif Intell Cancer 2024; 5. [DOI: 10.35713/aic.v5.i2.97317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 09/05/2024] Open
Abstract
Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.
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Affiliation(s)
- Ankit Shukla
- Department of Surgery, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| | - Rajesh Chaudhary
- Department of Renal Transplantation, Dr Rajendra Prasad Government Medical College, Kangra 176001, India
| | - Nishant Nayyar
- Department of Radiology, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
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12
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Turtoi DC, Brata VD, Incze V, Ismaiel A, Dumitrascu DI, Militaru V, Munteanu MA, Botan A, Toc DA, Duse TA, Popa SL. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. J Clin Med 2024; 13:4818. [PMID: 39200959 PMCID: PMC11355427 DOI: 10.3390/jcm13164818] [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: 07/23/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 09/02/2024] Open
Abstract
Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.
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Affiliation(s)
- Daria Claudia Turtoi
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Vlad Dumitru Brata
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Victor Incze
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (A.I.); (S.L.P.)
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Valentin Militaru
- Department of Internal Medicine, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania;
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania;
| | - Alexandru Botan
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Dan Alexandru Toc
- Department of Microbiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Traian Adrian Duse
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (A.I.); (S.L.P.)
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13
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Khayatian D, Maleki A, Nasiri H, Dorrigiv M. Histopathology image analysis for gastric cancer detection: a hybrid deep learning and catboost approach. MULTIMEDIA TOOLS AND APPLICATIONS 2024. [DOI: 10.1007/s11042-024-19816-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 03/04/2024] [Accepted: 06/25/2024] [Indexed: 01/03/2025]
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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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15
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Matsubayashi CO, Cheng S, Hulchafo I, Zhang Y, Tada T, Buxbaum JL, Ochiai K. Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market. Dig Liver Dis 2024; 56:1156-1163. [PMID: 38763796 DOI: 10.1016/j.dld.2024.04.019] [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: 10/19/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024]
Abstract
Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.
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Affiliation(s)
- Carolina Ogawa Matsubayashi
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo, Brasil; AI Medical Service Inc., Tokyo, Japan.
| | - Shuyan Cheng
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Ismael Hulchafo
- Columbia University School of Nursing, New York, NY 10032, USA
| | - Yifan Zhang
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan; Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - James L Buxbaum
- Division of Gastrointestinal and Liver Diseases, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Colon and Rectal Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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16
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Nijjar GS, Aulakh SK, Singh R, Chandi SK. Emerging Technologies in Endoscopy for Gastrointestinal Neoplasms: A Comprehensive Overview. Cureus 2024; 16:e62946. [PMID: 39044885 PMCID: PMC11265259 DOI: 10.7759/cureus.62946] [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: 06/22/2024] [Indexed: 07/25/2024] Open
Abstract
Gastrointestinal neoplasms are a growing global health concern, requiring prompt identification and treatment. Endoscopic procedures have revolutionized the detection and treatment of gastrointestinal tumors by providing accurate, minimally invasive methods. Early-stage malignancies can be treated with endoscopic excision, leading to improved outcomes and increased survival rates. Precancerous lesions, like adenomatous polyps, can be prevented by removing them, reducing cancer occurrence and death rates. Advanced techniques like chromoendoscopy, narrow-band imaging, and confocal laser endomicroscopy improve the ability to see the mucosa surface and diagnose conditions. Artificial Intelligence (AI) applications in endoscopy can enhance diagnostic accuracy and predict histology outcomes. However, challenges remain in accurately defining lesions and ensuring precise diagnosis and treatment selection. Molecular imaging approaches and therapeutic modalities like photodynamic therapy and endoscopic ultrasonography-guided therapies hold potential but require further study and clinical confirmation. This study examines the future prospects and obstacles in endoscopic procedures for the timely identification and treatment of gastrointestinal cancers. The focus is on developing technology, limits, and prospective effects on clinical practice.
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Affiliation(s)
| | - Smriti Kaur Aulakh
- Internal Medicine, Sri Guru Ram Das University of Health Science and Research, Amritsar, IND
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17
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Shimada Y, Ojima T, Takaoka Y, Sugano A, Someya Y, Hirabayashi K, Homma T, Kitamura N, Akemoto Y, Tanabe K, Sato F, Yoshimura N, Tsuchiya T. Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning. Surg Today 2024; 54:540-550. [PMID: 37864054 DOI: 10.1007/s00595-023-02756-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 09/13/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically. METHODS Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients. RESULTS The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons. CONCLUSION The deep learning model systems can be utilized in clinical applications via data expansion.
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Affiliation(s)
- Yoshifumi Shimada
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Toshihiro Ojima
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yutaka Takaoka
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Aki Sugano
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Clinical Research, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Yoshiaki Someya
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Kenichi Hirabayashi
- Department of Diagnostic Pathology, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Takahiro Homma
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoya Kitamura
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yushi Akemoto
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Keitaro Tanabe
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Fumitaka Sato
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoki Yoshimura
- Department of Cardiovascular Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Tomoshi Tsuchiya
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
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Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
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Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
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19
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Waheed Z, Gui J. An optimized ensemble model bfased on cuckoo search with Levy Flight for automated gastrointestinal disease detection. MULTIMEDIA TOOLS AND APPLICATIONS 2024; 83:89695-89722. [DOI: 10.1007/s11042-024-18937-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 01/15/2025]
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20
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Horiuchi Y, Hirasawa T, Fujisaki J. Endoscopic Features of Undifferentiated-Type Early Gastric Cancer in Patients with Helicobacter pylori-Uninfected or -Eradicated Stomachs: A Comprehensive Review. Gut Liver 2024; 18:209-217. [PMID: 37855088 PMCID: PMC10938157 DOI: 10.5009/gnl230106] [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: 03/22/2023] [Revised: 05/10/2023] [Accepted: 05/23/2023] [Indexed: 03/16/2024] Open
Abstract
Since the indications for endoscopic submucosal dissection have been expanded to include undifferentiated-type early gastric cancers, improvements in preoperative diagnostic ability have been an area of research. There are also concerns about the impact on the diagnosis of Helicobacter pylori infection. Based on our previous studies, in undifferentiated-type early gastric cancers, magnifying endoscopy with narrow-band imaging is useful for delineating the demarcation regardless of the tumor size. Additionally, inflammatory cell infiltration appears to be a cause of misdiagnosis, suggesting that the resolution of inflammation could contribute to the accurate diagnosis of demarcations. As such, the accuracy of demarcation in eradicated and uninfected cases is higher than that in non-eradicated cases. The common features of the endoscopic findings were discoloration under white-light imaging and a predominance of sites in the lower and middle regions. The uninfected group was characterized by smaller tumor size, flat type, more extended intervening parts in magnifying endoscopy with narrow-band imaging, and pure signet ring cell carcinoma. In contrast, the eradication and non-eradication groups were characterized by larger tumor size, depressed type, and wavy microvessels in magnifying endoscopy with narrow-band imaging. In this comprehensive review, as described above, we discuss the diagnosis of demarcation of undifferentiated-type early gastric cancers, undifferentiated-type early gastric cancers that developed following H. pylori eradication, and H. pylori-uninfected undifferentiated-type early gastric cancers, with a focus on studies with self-examination and endoscopic findings and describe the future direction.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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21
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Sierra-Jerez F, Martinez F. A non-aligned translation with a neoplastic classifier regularization to include vascular NBI patterns in standard colonoscopies. Comput Biol Med 2024; 170:108008. [PMID: 38277922 DOI: 10.1016/j.compbiomed.2024.108008] [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: 08/25/2023] [Revised: 12/21/2023] [Accepted: 01/13/2024] [Indexed: 01/28/2024]
Abstract
Polyp vascular patterns are key to categorizing colorectal cancer malignancy. These patterns are typically observed in situ from specialized narrow-band images (NBI). Nonetheless, such vascular characterization is lost from standard colonoscopies (the primary attention mechanism). Besides, even for NBI observations, the categorization remains biased for expert observations, reporting errors in classification from 59.5% to 84.2%. This work introduces an end-to-end computational strategy to enhance in situ standard colonoscopy observations, including vascular patterns typically observed from NBI mechanisms. These retrieved synthetic images are achieved by adjusting a deep representation under a non-aligned translation task from optical colonoscopy (OC) to NBI. The introduced scheme includes an architecture to discriminate enhanced neoplastic patterns achieving a remarkable separation into the embedding representation. The proposed approach was validated in a public dataset with a total of 76 sequences, including standard optical sequences and the respective NBI observations. The enhanced optical sequences were automatically classified among adenomas and hyperplastic samples achieving an F1-score of 0.86%. To measure the sensibility capability of the proposed approach, serrated samples were projected to the trained architecture. In this experiment, statistical differences from three classes with a ρ-value <0.05 were reported, following a Mann-Whitney U test. This work showed remarkable polyp discrimination results in enhancing OC sequences regarding typical NBI patterns. This method also learns polyp class distributions under the unpaired criteria (close to real practice), with the capability to separate serrated samples from adenomas and hyperplastic ones.
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Affiliation(s)
- Franklin Sierra-Jerez
- Biomedical Imaging, Vision and Learning Laboratory (BIVL(2)ab), Universidad Industrial de Santander (UIS), Colombia
| | - Fabio Martinez
- Biomedical Imaging, Vision and Learning Laboratory (BIVL(2)ab), Universidad Industrial de Santander (UIS), Colombia.
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22
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Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
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Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
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23
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Shi Y, Fan H, Li L, Hou Y, Qian F, Zhuang M, Miao B, Fei S. The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis. World J Surg Oncol 2024; 22:40. [PMID: 38297303 PMCID: PMC10832162 DOI: 10.1186/s12957-024-03321-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis. METHODS We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed. RESULTS Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64). CONCLUSION ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.
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Affiliation(s)
- Yiheng Shi
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Haohan Fan
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Li Li
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Yaqi Hou
- College of Nursing, Yangzhou University, Yangzhou, 225009, China
| | - Feifei Qian
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Mengting Zhuang
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Bei Miao
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Institute of Digestive Diseases, Xuzhou Medical University, 84 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
| | - Sujuan Fei
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China.
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Horiuchi Y, Hirasawa T, Fujisaki J. Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. Clin Endosc 2024; 57:11-17. [PMID: 38178327 PMCID: PMC10834286 DOI: 10.5946/ce.2023.173] [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: 07/08/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 01/06/2024] Open
Abstract
Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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25
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics (Basel) 2023; 13:3613. [PMID: 38132197 PMCID: PMC10742887 DOI: 10.3390/diagnostics13243613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- ARC Innovation Center, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
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28
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Wang L, Yang Y, Yang A, Li T. Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images. BIOMEDICAL OPTICS EXPRESS 2023; 14:4677-4695. [PMID: 37791283 PMCID: PMC10545198 DOI: 10.1364/boe.487456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/11/2023] [Accepted: 06/29/2023] [Indexed: 10/05/2023]
Abstract
Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.
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Affiliation(s)
- Lingxiao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Yingyun Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Aiming Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
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29
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Cheema HI, Tharian B, Inamdar S, Garcia-Saenz-de-Sicilia M, Cengiz C. Recent advances in endoscopic management of gastric neoplasms. World J Gastrointest Endosc 2023; 15:319-337. [PMID: 37274561 PMCID: PMC10236974 DOI: 10.4253/wjge.v15.i5.319] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/12/2023] [Accepted: 04/06/2023] [Indexed: 05/16/2023] Open
Abstract
The development and clinical application of new diagnostic endoscopic technologies such as endoscopic ultrasonography with biopsy, magnification endoscopy, and narrow-band imaging, more recently supplemented by artificial intelligence, have enabled wider recognition and detection of various gastric neoplasms including early gastric cancer (EGC) and subepithelial tumors, such as gastrointestinal stromal tumors and neuroendocrine tumors. Over the last decade, the evolution of novel advanced therapeutic endoscopic techniques, such as endoscopic mucosal resection, endoscopic submucosal dissection, endoscopic full-thickness resection, and submucosal tunneling endoscopic resection, along with the advent of a broad array of endoscopic accessories, has provided a promising and yet less invasive strategy for treating gastric neoplasms with the advantage of a reduced need for gastric surgery. Thus, the management algorithms of various gastric tumors in a defined subset of the patient population at low risk of lymph node metastasis and amenable to endoscopic resection, may require revision considering upcoming data given the high success rate of en bloc resection by experienced endoscopists. Moreover, endoscopic surveillance protocols for precancerous gastric lesions will continue to be refined by systematic reviews and meta-analyses of further research. However, the lack of familiarity with subtle endoscopic changes associated with EGC, as well as longer procedural time, evolving resection techniques and tools, a steep learning curve of such high-risk procedures, and lack of coding are issues that do not appeal to many gastroenterologists in the field. This review summarizes recent advances in the endoscopic management of gastric neoplasms, with special emphasis on diagnostic and therapeutic methods and their future prospects.
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Affiliation(s)
- Hira Imad Cheema
- Department of Internal Medicine, Baptist Health Medical Center, Little Rock, AR 72205, United States
| | - Benjamin Tharian
- Department of Interventional Endoscopy/Gastroenterology, Bayfront Health, Digestive Health Institute, St. Petersberg, FL 33701, United States
| | - Sumant Inamdar
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Mauricio Garcia-Saenz-de-Sicilia
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Cem Cengiz
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, John L. McClellan Memorial Veterans Hospital, Little Rock, AR 72205, United States
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, TOBB University of Economics and Technology, Ankara 06510, Turkey
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30
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Yin M, Liu L, Gao J, Lin J, Qu S, Xu W, Liu X, Xu C, Zhu J. Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review. Int J Med Inform 2023; 174:105044. [PMID: 36948061 DOI: 10.1016/j.ijmedinf.2023.105044] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Shuting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Wei Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
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Yacob YM, Alquran H, Mustafa WA, Alsalatie M, Sakim HAM, Lola MS. H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner. Diagnostics (Basel) 2023; 13:diagnostics13030336. [PMID: 36766441 PMCID: PMC9914156 DOI: 10.3390/diagnostics13030336] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
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Affiliation(s)
- Yasmin Mohd Yacob
- Faculty of Electronic Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Wan Azani Mustafa
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Correspondence:
| | - Mohammed Alsalatie
- King Hussein Medical Center, Royal Jordanian Medical Service, The Institute of Biomedical Technology, Amman 11855, Jordan
| | - Harsa Amylia Mat Sakim
- School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 11800, Penang, Malaysia
| | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Terengganu, Malaysia
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Panarese A, Saito Y, Zagari RM. Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need? Artif Intell Gastrointest Endosc 2023; 4:1-11. [DOI: 10.37126/aige.v4.i1.1] [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: 09/14/2022] [Revised: 10/18/2022] [Accepted: 01/04/2023] [Indexed: 01/06/2023] Open
Abstract
Chronic gastritis (CG) is a widespread and frequent disease, mainly caused by Helicobacter pylori infection, which is associated with an increased risk of gastric cancer. Virtual chromoendoscopy improves the endoscopic diagnostic efficacy, which is essential to establish the most appropriate therapy and to enable cancer prevention. Artificial intelligence provides algorithms for the diagnosis of gastritis and, in particular, early gastric cancer, but it is not yet used in practice. Thus, technological innovation, through image resolution and processing, optimizes the diagnosis and management of CG and gastric cancer. The endoscopic Kyoto classification of gastritis improves the diagnosis and management of this disease, but through the analysis of the most recent literature, new algorithms can be proposed.
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Affiliation(s)
- Alba Panarese
- Division of Gastroenterology and Digestive Endoscopy, Department of Medical Sciences, Central Hospital - Azienda Ospedaliera, Taranto 74123, Italy
| | - Yutaka Saito
- Division of Endoscopy, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Rocco Maurizio Zagari
- Gastroenterology Unit and Department of Surgical and Medical Sciences, IRCCS Azienda Ospedaliero-Universitaria and University of Bologna, Bologna 40121, Italy
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Zhang G, Song J, Feng Z, Zhao W, Huang P, Liu L, Zhang Y, Su X, Wu Y, Cao Y, Li Z, Jie Z. Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace. Front Oncol 2023; 12:1075974. [PMID: 36686778 PMCID: PMC9846739 DOI: 10.3389/fonc.2022.1075974] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023] Open
Abstract
Objective This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis. Methods The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field. Results A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC. Conclusions Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.
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Affiliation(s)
- Guoyang Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jingjing Song
- Jiangxi Med College of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wentao Zhao
- The Third Clinical Department of China Medical University, Shenyang, China
| | - Pan Huang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li Liu
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yang Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xufeng Su
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yukang Wu
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Cao
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhengrong Li
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Zhigang Jie, ; Zhengrong Li,
| | - Zhigang Jie
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Zhigang Jie, ; Zhengrong Li,
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [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: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Ochiai K, Ozawa T, Shibata J, Ishihara S, Tada T. Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy. Diagnostics (Basel) 2022; 12:diagnostics12123153. [PMID: 36553160 PMCID: PMC9777622 DOI: 10.3390/diagnostics12123153] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/07/2022] [Accepted: 12/10/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.
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Affiliation(s)
- Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tsuyoshi Ozawa
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
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Luo Q, Yang H, Hu B. Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection. J Dig Dis 2022; 23:666-674. [PMID: 36661411 DOI: 10.1111/1751-2980.13154] [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: 08/12/2022] [Revised: 01/04/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.
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Affiliation(s)
- Qi Luo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Gong L, Wang M, Shu L, He J, Qin B, Xu J, Su W, Dong D, Hu H, Tian J, Zhou P. Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging. Gastrointest Endosc 2022; 96:929-942.e6. [PMID: 35917877 DOI: 10.1016/j.gie.2022.07.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/03/2022] [Accepted: 07/13/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS The detection rate for early gastric cancer (EGC) is unsatisfactory, and mastering the diagnostic skills of magnifying endoscopy with narrow-band imaging (ME-NBI) requires rich expertise and experience. We aimed to develop an EGC captioning model (EGCCap) to automatically describe the visual characteristics of ME-NBI images for endoscopists. METHODS ME-NBI images (n = 1886) from 294 cases were enrolled from multiple centers, and corresponding 5658 text data were designed following the simple EGC diagnostic algorithm. An EGCCap was developed using the multiscale meshed-memory transformer. We conducted comprehensive evaluations for EGCCap including the quantitative and quality of performance, generalization, robustness, interpretability, and assistant value analyses. The commonly used metrics were BLEUs, CIDEr, METEOR, ROUGE, SPICE, accuracy, sensitivity, and specificity. Two-sided statistical tests were conducted, and statistical significance was determined when P < .05. RESULTS EGCCap acquired satisfying captioning performance by outputting correctly and coherently clinically meaningful sentences in the internal test cohort (BLEU1 = 52.434, CIDEr = 36.734, METEOR = 27.823, ROUGE = 49.949, SPICE = 35.548) and maintained over 80% performance when applied to other centers or corrupted data. The diagnostic ability of endoscopists improved with the assistance of EGCCap, which was especially significant (P < .05) for junior endoscopists. Endoscopists gave EGCCap an average remarkable score of 7.182, showing acceptance of EGCCap. CONCLUSIONS EGCCap exhibited promising captioning performance and was proven with satisfying generalization, robustness, and interpretability. Our study showed potential value in aiding and improving the diagnosis of EGC and facilitating the development of automated reporting in the future.
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Affiliation(s)
- Lixin Gong
- College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Min Wang
- Department of Gastroenterology, Hepatology and Nutrition, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Lei Shu
- Department of Gastroenterology, No. 1 Hospital of Wuhan, Wuhan, China
| | - Jie He
- Endoscopy Center, Zhongshan Hospital (Xiamen Branch), Fudan University, Xiamen, China; Department of Gastroenterology, The Affiliated Dongnan Hospital of Xiamen University, Zhangzhou, China
| | - Bin Qin
- Department of Gastroenterology, the Second Affiliated Hospital of Xi'an Jiaotong University, Zhangzhou, China
| | - Jiacheng Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Wei Su
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Hu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China; Department of Gastroenterology, Shigatse People's Hospital, Shigatse, China
| | - Jie Tian
- College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Pinghong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
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Pan J, Lei LM. Value of serum pepsinogen, gastrin, and cadherin-17 detection combined with narrowband imaging magnifying endoscopy in distinguishing early gastric cancer and precancerous lesions. Shijie Huaren Xiaohua Zazhi 2022; 30:964-970. [DOI: 10.11569/wcjd.v30.i21.964] [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] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The early screening of cancer has always been the focus of cancer research. As a common cancer in the world, gastric cancer has had a high incidence rate and mortality for many years, and canceration is difficult to detect. Current detection methods are not ideal for early detection of gastric cancer and precancerous lesions.
AIM To assess the value of detection of serum pepsinogen (PG), gastrin (G-17), and hepato-intestinal cadherin-17 (CDH-17) combined with narrowband imaging magnifying endoscopy in the identification of early gastric cancer and precancerous lesions.
METHODS The clinical data of patients with early gastric cancer (106 cases) and precancerous lesions (134 cases) at our hospital from January 2018 to January 2021 were retrospectively analyzed. All patients underwent narrow-band imaging magnifying endoscopy and detection of serum PG (including PGⅠ and PGⅡ), G-17, and CDH-17 levels. The sensitivity, specificity, and accuracy of PG, G-17, CDH-17, and narrow-band imaging magnifying endoscopy, alone and in combination, in the diagnosis of early gastric cancer and precancerous lesions were calculated.
RESULTS In the early gastric cancer group, the incidence of lesions with border limits, mucosal microvascular irregularities, irregular surface ducts, and increased glandular spacing as detected by narrow-band imaging magnifying endoscopy was higher than that in the precancerous lesion group (P < 0.05). The diagnostic coincidence rates of magnifying endoscopy for early gastric cancer and precancerous lesions were 83.02% and 85.07%, respectively, and the difference was not statistically significant (P > 0.05). Serum PGⅠ level in the early gastric cancer group was lower than that of the precancerous lesion group, but there was no significant difference in serum PGⅡ between the two groups (P > 0.05); serum G-17 and CDH-17 levels were higher than those of the precancerous lesion group (P < 0.05). There was no statistically significant difference in the diagnostic coincidence rates of serum PGI, G-17, and CDH-17 alone (P > 0.05). The sensitivity, specificity and accuracy of serum PGⅠ, G-17, and CDH-17 combined with narrow-band imaging magnifying endoscopy in the diagnosis of early gastric cancer and precancerous lesions were higher than those of any serum index alone (PGⅠ/G-17/CDH-17) or narrowband imaging magnifying endoscopy alone (P < 0.05).
CONCLUSION The detection of serum PG, G-17, and CDH-17 combined with narrow-band imaging magnifying endoscopy has high sensitivity, specificity, and accuracy in the differential diagnosis of early gastric cancer and precancerous lesions.
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Affiliation(s)
- Jie Pan
- Zhejiang Chinese Medicine University, Hangzhou 310053, Zhejiang Province, China,General Medicine Department of Lishui People's Hospital, Lishui 323000, Zhejiang Province, China
| | - Li-Mei Lei
- General Medicine Department of Lishui People's Hospital, Lishui 323000, Zhejiang Province, China
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Yang H, Wu Y, Yang B, Wu M, Zhou J, Liu Q, Lin Y, Li S, Li X, Zhang J, Wang R, Xie Q, Li J, Luo Y, Tu M, Wang X, Lan H, Bai X, Wu H, Zeng F, Zhao H, Yi Z, Zeng F. Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm. Gastrointest Endosc 2022; 96:787-795.e6. [PMID: 35718070 DOI: 10.1016/j.gie.2022.06.011] [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: 01/30/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIMS The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted diagnosis system (GEADS) to improve diagnostic accuracy and clinical utility. METHODS In this retrospective, multicenter study, a convolutional neural network was trained to assess upper GI diseases based on 26,228 endoscopic images from Dazhou Central Hospital that were randomly assigned (3:1:1) to a training dataset, validation dataset, and test dataset, respectively. To validate the model, 6 external independent datasets comprising 51,372 images of upper GI diseases were collected. In addition, 1 prospective dataset comprising 27,975 images was collected. The performance of GEADS was compared with endoscopists with 2 professional degrees of expertise: expert and novice. Eight endoscopists were in the expert group with >5 years of experience, whereas 3 endoscopists were in the novice group with 1 to 5 years of experience. RESULTS The GEADS model achieved an accuracy of .918 (95% confidence interval [CI], .914-.922), with an F1 score of .884 (95% CI, .879-.889), recall of .873 (95% CI, .868-.878), and precision of .890 (95% CI, .885-.895) in the internal validation dataset. In the external validation datasets and 1 prospective validation dataset, the diagnostic accuracy of the GEADS ranged from .841 (95% CI, .834-.848) to .949 (95% CI, .935-.963). With the help of the GEADS, the diagnosing accuracies of novice and expert endoscopists were significantly improved (P < .001). CONCLUSIONS The AI system can assist endoscopists in improving the accuracy of diagnosing upper GI diseases.
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Affiliation(s)
- Hang Yang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Yu Wu
- Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Bo Yang
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jun Zhou
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qin Liu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Yifei Lin
- Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shilin Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Xue Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jie Zhang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Rui Wang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qianrong Xie
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jingqi Li
- College of Aulin, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Yue Luo
- College of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Mengjie Tu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China; Department of Surgery, Shantou University Medical College, Shantou, Guangdong, China
| | - Xiao Wang
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Haitao Lan
- Department of Sichuan, Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Xuesong Bai
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Huaping Wu
- Department of Cardiac &Vascular Surgery, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Fanwei Zeng
- Department of Spinal Surgery, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China
| | - Hong Zhao
- Department of Hepatobiliary Surgery, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Zhang Yi
- Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China; Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
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Jin J, Zhang Q, Dong B, Ma T, Mei X, Wang X, Song S, Peng J, Wu A, Dong L, Kong D. Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video). Front Oncol 2022; 12:927868. [PMID: 36338757 PMCID: PMC9630732 DOI: 10.3389/fonc.2022.927868] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/05/2022] [Indexed: 12/04/2022] Open
Abstract
The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ2 = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ2 = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ2 = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ2 = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ2 = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.
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Affiliation(s)
- Jing Jin
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qianqian Zhang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bill Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Tao Ma
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Xuecan Mei
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xi Wang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shaofang Song
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Jie Peng
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Aijiu Wu
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Lanfang Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Derun Kong
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Derun Kong,
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Ishioka M, Osawa H, Hirasawa T, Kawachi H, Nakano K, Fukushima N, Sakaguchi M, Tada T, Kato Y, Shibata J, Ozawa T, Tajiri H, Fujisaki J. Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: Retrospective study. Dig Endosc 2022; 35:483-491. [PMID: 36239483 DOI: 10.1111/den.14455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/12/2022] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Endoscopists' abilities to diagnose early gastric cancers (EGCs) vary, especially between specialists and nonspecialists. We developed an artificial intelligence (AI)-based diagnostic support tool "Tango" to differentiate EGCs and compared its performance with that of endoscopists. METHODS The diagnostic performances of Tango and endoscopists (34 specialists, 42 nonspecialists) were compared using still images of 150 neoplastic and 165 non-neoplastic lesions. Neoplastic lesions included EGCs and adenomas. The primary outcome was to show the noninferiority of Tango (based on sensitivity) over specialists. The secondary outcomes were the noninferiority of Tango (based on accuracy) over specialists and the superiority of Tango (based on sensitivity and accuracy) over nonspecialists. The lower limit of the 95% confidence interval (CI) of the difference between Tango and the specialists for sensitivity was calculated, with >-10% defined as noninferiority and >0% defined as superiority in the primary outcome. The comparable differences between Tango and the endoscopists for each performance were calculated, with >10% defined as superiority and >0% defined as noninferiority in the secondary outcomes. RESULTS Tango achieved superiority over the specialists based on sensitivity (84.7% vs. 65.8%, difference 18.9%, 95% CI 12.3-25.3%) and demonstrated noninferiority based on accuracy (70.8% vs. 67.4%). Tango achieved superiority over the nonspecialists based on sensitivity (84.7% vs. 51.0%) and accuracy (70.8% vs. 58.4%). CONCLUSIONS The AI-based diagnostic support tool for EGCs demonstrated a robust performance and may be useful to reduce misdiagnosis.
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Affiliation(s)
- Mitsuaki Ishioka
- Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroyuki Osawa
- Division of Gastroenterology, Department of Medicine, Jichi Medical University, Tochigi, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroshi Kawachi
- Department of Pathology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kaoru Nakano
- Department of Pathology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | | | - Mio Sakaguchi
- Department of Pathology, Jichi Medical University, Tochigi, Japan
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan.,Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | | | - Junichi Shibata
- AI Medical Service Inc., Tokyo, Japan.,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Tsuyoshi Ozawa
- AI Medical Service Inc., Tokyo, Japan.,Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hisao Tajiri
- The Jikei University School of Medicine, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
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Ma M, Li Z, Yu T, Liu G, Ji R, Li G, Guo Z, Wang L, Qi Q, Yang X, Qu J, Wang X, Zuo X, Ren H, Li Y. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front Oncol 2022; 12:945904. [PMID: 35992850 PMCID: PMC9389533 DOI: 10.3389/fonc.2022.945904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
Background and aim Magnifying image-enhanced endoscopy was demonstrated to have higher diagnostic accuracy than white-light endoscopy. However, differentiating early gastric cancers (EGCs) from benign lesions is difficult for beginners. We aimed to determine whether the computer-aided model for the diagnosis of gastric lesions can be applied to videos rather than still images. Methods A total of 719 magnifying optical enhancement images of EGCs, 1,490 optical enhancement images of the benign gastric lesions, and 1,514 images of background mucosa were retrospectively collected to train and develop a computer-aided diagnostic model. Subsequently, 101 video segments and 671 independent images were used for validation, and error frames were labeled to retrain the model. Finally, a total of 117 unaltered full-length videos were utilized to test the model and compared with those diagnostic results made by independent endoscopists. Results Except for atrophy combined with intestinal metaplasia (IM) and low-grade neoplasia, the diagnostic accuracy was 0.90 (85/94). The sensitivity, specificity, PLR, NLR, and overall accuracy of the model to distinguish EGC from non-cancerous lesions were 0.91 (48/53), 0.78 (50/64), 4.14, 0.12, and 0.84 (98/117), respectively. No significant difference was observed in the overall diagnostic accuracy between the computer-aided model and experts. A good level of kappa values was found between the model and experts, which meant that the kappa value was 0.63. Conclusions The performance of the computer-aided model for the diagnosis of EGC is comparable to that of experts. Magnifying the optical enhancement model alone may not be able to deal with all lesions in the stomach, especially when near the focus on severe atrophy with IM. These results warrant further validation in prospective studies with more patients. A ClinicalTrials.gov registration was obtained (identifier number: NCT04563416). Clinical Trial Registration ClinicalTrials.gov, identifier NCT04563416.
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Affiliation(s)
- Mingjun Ma
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Tao Yu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guanqun Liu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Rui Ji
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guangchao Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhuang Guo
- Department of Gastroenterology, Shengli Oilfield Central Hospital, Dongying, China
| | - Limei Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Qingqing Qi
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Xiaoxiao Yang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Junyan Qu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Xiao Wang
- Department of Pathology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Hongliang Ren
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
- *Correspondence: Yanqing Li,
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Sugano K, Spechler SJ, El-Omar EM, McColl KEL, Takubo K, Gotoda T, Fujishiro M, Iijima K, Inoue H, Kawai T, Kinoshita Y, Miwa H, Mukaisho KI, Murakami K, Seto Y, Tajiri H, Bhatia S, Choi MG, Fitzgerald RC, Fock KM, Goh KL, Ho KY, Mahachai V, O'Donovan M, Odze R, Peek R, Rugge M, Sharma P, Sollano JD, Vieth M, Wu J, Wu MS, Zou D, Kaminishi M, Malfertheiner P. Kyoto international consensus report on anatomy, pathophysiology and clinical significance of the gastro-oesophageal junction. Gut 2022; 71:1488-1514. [PMID: 35725291 PMCID: PMC9279854 DOI: 10.1136/gutjnl-2022-327281] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/03/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVE An international meeting was organised to develop consensus on (1) the landmarks to define the gastro-oesophageal junction (GOJ), (2) the occurrence and pathophysiological significance of the cardiac gland, (3) the definition of the gastro-oesophageal junctional zone (GOJZ) and (4) the causes of inflammation, metaplasia and neoplasia occurring in the GOJZ. DESIGN Clinical questions relevant to the afore-mentioned major issues were drafted for which expert panels formulated relevant statements and textural explanations.A Delphi method using an anonymous system was employed to develop the consensus, the level of which was predefined as ≥80% of agreement. Two rounds of voting and amendments were completed before the meeting at which clinical questions and consensus were finalised. RESULTS Twenty eight clinical questions and statements were finalised after extensive amendments. Critical consensus was achieved: (1) definition for the GOJ, (2) definition of the GOJZ spanning 1 cm proximal and distal to the GOJ as defined by the end of palisade vessels was accepted based on the anatomical distribution of cardiac type gland, (3) chemical and bacterial (Helicobacter pylori) factors as the primary causes of inflammation, metaplasia and neoplasia occurring in the GOJZ, (4) a new definition of Barrett's oesophagus (BO). CONCLUSIONS This international consensus on the new definitions of BO, GOJ and the GOJZ will be instrumental in future studies aiming to resolve many issues on this important anatomic area and hopefully will lead to better classification and management of the diseases surrounding the GOJ.
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Affiliation(s)
- Kentaro Sugano
- Division of Gastroenterology, Department of Medicine, Jichi Medical University, Shimotsuke, Japan
| | - Stuart Jon Spechler
- Division of Gastroenterology, Center for Esophageal Diseases, Baylor University Medical Center, Dallas, Texas, USA
| | - Emad M El-Omar
- Microbiome Research Centre, St George & Sutherland Clinical Campuses, School of Clinical Medicine, Faculty of Medicine & Health, Sydney, New South Wales, Australia
| | - Kenneth E L McColl
- Division of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Kaiyo Takubo
- Research Team for Geriatric Pathology, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Takuji Gotoda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Katsunori Iijima
- Department of Gastroenterology, Akita University Graduate School of Medicine, Akita, Japan
| | - Haruhiro Inoue
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Takashi Kawai
- Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
| | | | - Hiroto Miwa
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Hyogo College of Medicine, Kobe, Japan
| | - Ken-Ichi Mukaisho
- Education Center for Medicine and Nursing, Shiga University of Medical Science, Otsu, Japan
| | - Kazunari Murakami
- Department of Gastroenterology, Oita University Faculty of Medicine, Yuhu, Japan
| | - Yasuyuki Seto
- Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hisao Tajiri
- Jikei University School of Medicine, Minato-ku, Tokyo, Japan
| | | | - Myung-Gyu Choi
- Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, The Republic of Korea
| | - Rebecca C Fitzgerald
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge, UK
| | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Duke NUS School of Medicine, National University of Singapore, Singapore
| | | | - Khek Yu Ho
- Department of Medicine, National University of Singapore, Singapore
| | - Varocha Mahachai
- Center of Excellence in Digestive Diseases, Thammasat University and Science Resarch and Innovation, Bangkok, Thailand
| | - Maria O'Donovan
- Department of Histopathology, Cambridge University Hospital NHS Trust UK, Cambridge, UK
| | - Robert Odze
- Department of Pathology, Tuft University School of Medicine, Boston, Massachusetts, USA
| | - Richard Peek
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Massimo Rugge
- Department of Medicine DIMED, Surgical Pathology and Cytopathology Unit, University of Padova, Padova, Italy
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - Jose D Sollano
- Department of Medicine, University of Santo Tomas, Manila, Philippines
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Friedrich-Alexander University Erlangen, Nurenberg, Germany
| | - Justin Wu
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Ming-Shiang Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Peter Malfertheiner
- Medizinixhe Klinik und Poliklinik II, Ludwig Maximillian University Klinikum, Munich, Germany
- Klinik und Poliklinik für Radiologie, Ludwig Maximillian University Klinikum, Munich, Germany
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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
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Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Zhou Y, Yuan X, Zhang X, Liu W, Wu Y, Yen GG, Hu B, Yi Z. Evolutionary Neural Architecture Search for Automatic Esophageal Lesion Identification and Segmentation. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2022; 3:436-450. [DOI: 10.1109/tai.2021.3134600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yao Zhou
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
| | - Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaozhi Zhang
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Wu
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
| | - Gary G. Yen
- School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhang Yi
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
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Chen PC, Lu YR, Kang YN, Chang CC. The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study. J Med Internet Res 2022; 24:e27694. [PMID: 35576561 PMCID: PMC9152716 DOI: 10.2196/27694] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 10/23/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. OBJECTIVE The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. METHODS We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. RESULTS We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. CONCLUSIONS For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. TRIAL REGISTRATION PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223.
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Affiliation(s)
- Pei-Chin Chen
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yun-Ru Lu
- Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Anesthesiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yi-No Kang
- Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan.,Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.,Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
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Noda H, Kaise M, Higuchi K, Koizumi E, Yoshikata K, Habu T, Kirita K, Onda T, Omori J, Akimoto T, Goto O, Iwakiri K, Tada T. Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer. BMC Gastroenterol 2022; 22:237. [PMID: 35549679 PMCID: PMC9102244 DOI: 10.1186/s12876-022-02312-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/04/2022] [Indexed: 12/24/2022] Open
Abstract
Background Endocytoscopy (ECS) aids early gastric cancer (EGC) diagnosis by visualization of cells. However, it is difficult for non-experts to accurately diagnose EGC using ECS. In this study, we developed and evaluated a convolutional neural network (CNN)-based system for ECS-aided EGC diagnosis. Methods We constructed a CNN based on a residual neural network with a training dataset comprising 906 images from 61 EGC cases and 717 images from 65 noncancerous gastric mucosa (NGM) cases. To evaluate diagnostic ability, we used an independent test dataset comprising 313 images from 39 EGC cases and 235 images from 33 NGM cases. The test dataset was further evaluated by three endoscopists, and their findings were compared with CNN-based results. Results The trained CNN required 7.0 s to analyze the test dataset. The area under the curve of the total ECS images was 0.93. The CNN produced 18 false positives from 7 NGM lesions and 74 false negatives from 28 EGC lesions. In the per-image analysis, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 83.2%, 76.4%, 92.3%, 93.0%, and 74.6%, respectively, with the CNN and 76.8%, 73.4%, 81.3%, 83.9%, and 69.6%, respectively, for the endoscopist-derived values. The CNN-based findings had significantly higher specificity than the findings determined by all endoscopists. In the per-lesion analysis, the accuracy, sensitivity, specificity, PPV, and NPV of the CNN-based findings were 86.1%, 82.1%, 90.9%, 91.4%, and 81.1%, respectively, and those of the results calculated by the endoscopists were 82.4%, 79.5%, 85.9%, 86.9%, and 78.0%, respectively. Conclusions Compared with three endoscopists, our CNN for ECS demonstrated higher specificity for EGC diagnosis. Using the CNN in ECS-based EGC diagnosis may improve the diagnostic performance of endoscopists.
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Affiliation(s)
- Hiroto Noda
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan.
| | - Mitsuru Kaise
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Kazutoshi Higuchi
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Eriko Koizumi
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Keiichiro Yoshikata
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Tsugumi Habu
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Kumiko Kirita
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Takeshi Onda
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Jun Omori
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Teppei Akimoto
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Osamu Goto
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Katsuhiko Iwakiri
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan.,AI Medical Service Inc., Tokyo, Japan
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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Panarese A. Usefulness of artificial intelligence in early gastric cancer. Artif Intell Cancer 2022; 3:17-26. [DOI: 10.35713/aic.v3.i2.17] [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: 12/31/2021] [Revised: 03/27/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is a major cancer worldwide, with high mortality and morbidity. Endoscopy, important for the early detection of GC, requires trained skills, high-quality technologies, surveillance and screening programs. Early diagnosis allows a better prognosis, through surgical or curative endoscopic therapy. Magnified endoscopy with virtual chromoendoscopy remarkably improve the detection of early gastric cancer (EGC) when endoscopy is performed by expert endoscopists. Artificial intelligence (AI) has also been introduced to GC diagnostics to increase diagnostic efficiency. AI improves the early detection of gastric lesions because it supports the non-expert and experienced endoscopist in defining the margins of the tumor and the depth of infiltration. AI increases the detection rate of EGC, reduces the rate of missing tumors, and characterizes EGCs, allowing clinicians to make the best therapeutic decision, that is, one that ensures curability. AI has had a remarkable evolution in medicine in recent years, moving from the research phase to clinical practice. In addition, the diagnosis of GC has markedly progressed. We predict that AI will allow great evolution in the diagnosis and treatment of EGC by overcoming the variability in performance that is currently a limitation of chromoendoscopy.
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Affiliation(s)
- Alba Panarese
- Department of Gastroenterology and Endoscopy, Central Hospital, Taranto 74123, Italy
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50
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Frazzoni L, Arribas J, Antonelli G, Libanio D, Ebigbo A, van der Sommen F, de Groof AJ, Fukuda H, Ohmori M, Ishihara R, Wu L, Yu H, Mori Y, Repici A, Bergman JJGHM, Sharma P, Messmann H, Hassan C, Fuccio L, Dinis-Ribeiro M. Endoscopists' diagnostic accuracy in detecting upper gastrointestinal neoplasia in the framework of artificial intelligence studies. Endoscopy 2022; 54:403-411. [PMID: 33951743 DOI: 10.1055/a-1500-3730] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Estimates on miss rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper GI endoscopy are not fully established owing to the lack of infrastructure to measure endoscopists' competence. We aimed to assess endoscopists' accuracy for the recognition of UGIN exploiting the framework of artificial intelligence (AI) validation studies. METHODS Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 were performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically verified expert-annotated ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), and area under the curve (AUC) for all UGIN, for esophageal squamous cell neoplasia (ESCN), Barrett esophagus-related neoplasia (BERN), and gastric adenocarcinoma (GAC). RESULTS Seven studies (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall) with 122 endoscopists were included. The pooled endoscopists' sensitivity and specificity for UGIN were 82 % (95 % confidence interval [CI] 80 %-84 %) and 79 % (95 %CI 76 %-81 %), respectively. Endoscopists' accuracy was higher for GAC detection (AUC 0.95 [95 %CI 0.93-0.98]) than for ESCN (AUC 0.90 [95 %CI 0.88-0.92]) and BERN detection (AUC 0.86 [95 %CI 0.84-0.88]). Sensitivity was higher for Eastern vs. Western endoscopists (87 % [95 %CI 84 %-89 %] vs. 75 % [95 %CI 72 %-78 %]), and for expert vs. non-expert endoscopists (85 % [95 %CI 83 %-87 %] vs. 71 % [95 %CI 67 %-75 %]). CONCLUSION We show suboptimal accuracy of endoscopists for the recognition of UGIN even within a framework that included a higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence.
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Affiliation(s)
- Leonardo Frazzoni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Julia Arribas
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Giulio Antonelli
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Diogo Libanio
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Fons van der Sommen
- Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hiromu Fukuda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayasu Ohmori
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Alessandro Repici
- Digestive Endoscopy Unit, Humanitas Research Hospital - IRCCS, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Mário Dinis-Ribeiro
- Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal
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