Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Hussein M, González‐Bueno Puyal J, Lines D, Sehgal V, Toth D, Ahmad OF, Kader R, Everson M, Lipman G, Fernandez‐Sordo JO, Ragunath K, Esteban JM, Bisschops R, Banks M, Haefner M, Mountney P, Stoyanov D, Lovat LB, Haidry R. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks. United European Gastroenterol J 2022;10:528-537. [PMID: 35521666 PMCID: PMC9278593 DOI: 10.1002/ueg2.12233] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 03/31/2022] [Indexed: 02/06/2023] Open

For:	Hussein M, González‐Bueno Puyal J, Lines D, Sehgal V, Toth D, Ahmad OF, Kader R, Everson M, Lipman G, Fernandez‐Sordo JO, Ragunath K, Esteban JM, Bisschops R, Banks M, Haefner M, Mountney P, Stoyanov D, Lovat LB, Haidry R. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks. United European Gastroenterol J 2022;10:528-537. [PMID: 35521666 PMCID: PMC9278593 DOI: 10.1002/ueg2.12233] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 03/31/2022] [Indexed: 02/06/2023] Open

Number

Cited by Other Article(s)

Jong MR, de Groof AJ. Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus. Dig Liver Dis 2024;56:1126-1130. [PMID: 38071181 DOI: 10.1016/j.dld.2023.11.038] [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: 09/29/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 06/29/2024]

Tao Y, Fang L, Qin G, Xu Y, Zhang S, Zhang X, Du S. Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer. Thorac Cancer 2024;15:1296-1304. [PMID: 38685604 PMCID: PMC11147664 DOI: 10.1111/1759-7714.15261] [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: 10/22/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 05/02/2024] Open

Meinikheim M, Mendel R, Palm C, Probst A, Muzalyova A, Scheppach MW, Nagl S, Schnoy E, Römmele C, Schulz DAH, Schlottmann J, Prinz F, Rauber D, Rückert T, Matsumura T, Fernández-Esparrach G, Parsa N, Byrne MF, Messmann H, Ebigbo A. Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial. Endoscopy 2024. [PMID: 38547927 DOI: 10.1055/a-2296-5696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]

Affiliation(s)

Michael Meinikheim Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Robert Mendel Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Christoph Palm Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Andreas Probst Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Anna Muzalyova Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Markus W Scheppach Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Sandra Nagl Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Elisabeth Schnoy Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Christoph Römmele Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Dominik A H Schulz Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Jakob Schlottmann Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Friederike Prinz Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
David Rauber Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Tobias Rückert Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Tomoaki Matsumura Department of Gastroenterology, Chiba University Graduate School of Medicine, Chiba, Japan
Glòria Fernández-Esparrach Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain Faculty of Medicine, University of Barcelona, Barcelona, Spain Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
Nasim Parsa Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, United States Satisfai Health, Vancouver, Canada
Michael F Byrne Satisfai Health, Vancouver, Canada Gastroenterology, Vancouver General Hospital, The University of British Columbia, Vancouver, Canada
Helmut Messmann Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Alanna Ebigbo Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany

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Parasa S, Berzin T, Leggett C, Gross S, Repici A, Ahmad OF, Chiang A, Coelho-Prabhu N, Cohen J, Dekker E, Keswani RN, Kahn CE, Hassan C, Petrick N, Mountney P, Ng J, Riegler M, Mori Y, Saito Y, Thakkar S, Waxman I, Wallace MB, Sharma P. Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology. Gastrointest Endosc 2024:S0016-5107(23)03139-5. [PMID: 38639679 DOI: 10.1016/j.gie.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 04/20/2024]

Abstract

BACKGROUND AND AIMS

The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently.

METHODS

A modified Delphi process was used to develop these consensus statements.

RESULTS

Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology.

CONCLUSIONS

The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.

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Takeda T, Asaoka D, Ueyama H, Abe D, Suzuki M, Inami Y, Uemura Y, Yamamoto M, Iwano T, Uchida R, Utsunomiya H, Oki S, Suzuki N, Ikeda A, Akazawa Y, Matsumoto K, Ueda K, Hojo M, Nojiri S, Tada T, Nagahara A. Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett's Esophagus. J Clin Med 2024;13:1990. [PMID: 38610762 PMCID: PMC11012507 DOI: 10.3390/jcm13071990] [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: 03/01/2024] [Revised: 03/21/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open

Affiliation(s)

Tsutomu Takeda Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Daisuke Asaoka Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
Hiroya Ueyama Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Daiki Abe Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Maiko Suzuki Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
Yoshihiro Inami Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
Yasuko Uemura Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Momoko Yamamoto Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Tomoyo Iwano Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Ryota Uchida Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Hisanori Utsunomiya Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Shotaro Oki Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Nobuyuki Suzuki Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Atsushi Ikeda Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Yoichi Akazawa Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Kohei Matsumoto Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Kumiko Ueda Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Mariko Hojo Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Shuko Nojiri Department of Medical Technology Innovation Center, Juntendo University School of Medicine, Tokyo 113-8421, Japan;
Tomohiro Tada AI Medical Service Inc., Tokyo 171-0013, Japan;
Akihito Nagahara Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)

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Ratcliffe E, Britton J, Yalamanchili H, Rostami I, Nadir SMH, Korani M, Eruchie I, Wazirdin MA, Prasad N, Hamdy S, McLaughlin J, Ang Y. Dedicated service for Barrett's oesophagus surveillance endoscopy yields higher dysplasia detection and guideline adherence in a non-tertiary setting in the UK: a 5-year comparative cohort study. Frontline Gastroenterol 2024;15:21-27. [PMID: 38487558 PMCID: PMC10935534 DOI: 10.1136/flgastro-2023-102425] [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: 03/15/2023] [Accepted: 07/19/2023] [Indexed: 03/17/2024] Open

Affiliation(s)

Elizabeth Ratcliffe Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK Division of Diabetes, Endocrinology and Gastroenterology Faculty of Biology, Medicine and Health School of Medical Sciences, University of Manchester, Manchester, UK
James Britton Gastroenterology department, Northern Care Alliance NHS Trust, Salford, UK
Harika Yalamanchili Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Izabela Rostami Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Syed Mujtaba Hasnain Nadir Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Mohamed Korani Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Ikedichukwu Eruchie Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Muhammad Awais Wazirdin Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Neeraj Prasad Gastroenterology department, Wrightington Wigan and Leigh NHS Foundation Trust, Wigan, UK
Shaheen Hamdy Division of Diabetes, Endocrinology and Gastroenterology Faculty of Biology, Medicine and Health School of Medical Sciences, University of Manchester, Manchester, UK Gastroenterology department, Northern Care Alliance NHS Trust, Salford, UK
John McLaughlin Division of Diabetes, Endocrinology and Gastroenterology Faculty of Biology, Medicine and Health School of Medical Sciences, University of Manchester, Manchester, UK Gastroenterology department, Northern Care Alliance NHS Trust, Salford, UK
Yeng Ang Division of Diabetes, Endocrinology and Gastroenterology Faculty of Biology, Medicine and Health School of Medical Sciences, University of Manchester, Manchester, UK Gastroenterology department, Northern Care Alliance NHS Trust, Salford, UK

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Tsai MC, Yen HH, Tsai HY, Huang YK, Luo YS, Kornelius E, Sung WW, Lin CC, Tseng MH, Wang CC. Artificial intelligence system for the detection of Barrett's esophagus. World J Gastroenterol 2023;29:6198-6207. [PMID: 38186865 PMCID: PMC10768395 DOI: 10.3748/wjg.v29.i48.6198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/13/2023] [Accepted: 12/12/2023] [Indexed: 12/27/2023] Open

Affiliation(s)

Ming-Chang Tsai Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
Hsu-Heng Yen Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 500, Taiwan Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 400, Taiwan
Hui-Yu Tsai Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
Yu-Kai Huang Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
Yu-Sin Luo Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
Edy Kornelius School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan Department of Endocrinology and Metabolism, Chung-Shan Medical University Hospital, Taichung 402, Taiwan
Wen-Wei Sung School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan Department of Urology, Chung Shan Medical University Hospital, Taichung 402, Taiwan
Chun-Che Lin Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
Ming-Hseng Tseng Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan Information Technology Office, Chung Shan Medical University Hospital, Taichung 402, Taiwan
Chi-Chih Wang Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan

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Fockens KN, Jong MR, Jukema JB, Boers TGW, Kusters CHJ, van der Putten JA, Pouw RE, Duits LC, Montazeri NSM, van Munster SN, Weusten BLAM, Alvarez Herrero L, Houben MHMG, Nagengast WB, Westerhof J, Alkhalaf A, Mallant-Hent RC, Scholten P, Ragunath K, Seewald S, Elbe P, Baldaque-Silva F, Barret M, Ortiz Fernández-Sordo J, Villarejo GM, Pech O, Beyna T, van der Sommen F, de With PH, de Groof AJ, Bergman JJ. A deep learning system for detection of early Barrett's neoplasia: a model development and validation study. Lancet Digit Health 2023;5:e905-e916. [PMID: 38000874 DOI: 10.1016/s2589-7500(23)00199-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/22/2023] [Accepted: 09/18/2023] [Indexed: 11/26/2023]

Abstract

BACKGROUND

Computer-aided detection (CADe) systems could assist endoscopists in detecting early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic images. The aim of this study was to develop, test, and benchmark a CADe system for early neoplasia in Barrett's oesophagus.

METHODS

The CADe system was first pretrained with ImageNet followed by domain-specific pretraining with GastroNet. We trained the CADe system on a dataset of 14 046 images (2506 patients) of confirmed Barrett's oesophagus neoplasia and non-dysplastic Barrett's oesophagus from 15 centres. Neoplasia was delineated by 14 Barrett's oesophagus experts for all datasets. We tested the performance of the CADe system on two independent test sets. The all-comers test set comprised 327 (73 patients) non-dysplastic Barrett's oesophagus images, 82 (46 patients) neoplastic images, 180 (66 of the same patients) non-dysplastic Barrett's oesophagus videos, and 71 (45 of the same patients) neoplastic videos. The benchmarking test set comprised 100 (50 patients) neoplastic images, 300 (125 patients) non-dysplastic images, 47 (47 of the same patients) neoplastic videos, and 141 (82 of the same patients) non-dysplastic videos, and was enriched with subtle neoplasia cases. The benchmarking test set was evaluated by 112 endoscopists from six countries (first without CADe and, after 6 weeks, with CADe) and by 28 external international Barrett's oesophagus experts. The primary outcome was the sensitivity of Barrett's neoplasia detection by general endoscopists without CADe assistance versus with CADe assistance on the benchmarking test set. We compared sensitivity using a mixed-effects logistic regression model with conditional odds ratios (ORs; likelihood profile 95% CIs).

FINDINGS

Sensitivity for neoplasia detection among endoscopists increased from 74% to 88% with CADe assistance (OR 2·04; 95% CI 1·73-2·42; p<0·0001 for images and from 67% to 79% [2·35; 1·90-2·94; p<0·0001] for video) without compromising specificity (from 89% to 90% [1·07; 0·96-1·19; p=0·20] for images and from 96% to 94% [0·94; 0·79-1·11; ] for video; p=0·46). In the all-comers test set, CADe detected neoplastic lesions in 95% (88-98) of images and 97% (90-99) of videos. In the benchmarking test set, the CADe system was superior to endoscopists in detecting neoplasia (90% vs 74% [OR 3·75; 95% CI 1·93-8·05; p=0·0002] for images and 91% vs 67% [11·68; 3·85-47·53; p<0·0001] for video) and non-inferior to Barrett's oesophagus experts (90% vs 87% [OR 1·74; 95% CI 0·83-3·65] for images and 91% vs 86% [2·94; 0·99-11·40] for video).

INTERPRETATION

CADe outperformed endoscopists in detecting Barrett's oesophagus neoplasia and, when used as an assistive tool, it improved their detection rate. CADe detected virtually all neoplasia in a test set of consecutive cases.

FUNDING

Olympus.

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Affiliation(s)

K N Fockens Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
M R Jong Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
J B Jukema Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
T G W Boers Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
C H J Kusters Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
J A van der Putten Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
R E Pouw Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
L C Duits Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
N S M Montazeri Biostatistics Unit, Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
S N van Munster Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, Netherlands
B L A M Weusten Department of Gastroenterology and Hepatology, UMC Utrecht, University of Utrecht, Utrecht, Netherlands; Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, Netherlands
L Alvarez Herrero Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, Netherlands
M H M G Houben Department of Gastroenterology and Hepatology, HagaZiekenhuis Den Haag, Den Haag, Netherlands
W B Nagengast Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, Netherlands
J Westerhof Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, Netherlands
A Alkhalaf Department of Gastroenterology and Hepatology, Isala Hospital Zwolle, Zwolle, Netherlands
R C Mallant-Hent Department of Gastroenterology and Hepatology, Flevoziekenhuis Almere, Almere, Netherlands
P Scholten Department of Gastroenterology and Hepatology, Onze Lieve Vrouwe Gasthuis, Amsterdam, Netherlands
K Ragunath Department of Gastroenterology and Hepatology, Royal Perth Hospital, Curtin University, Perth, WA, Australia
S Seewald Department of Gastroenterology and Hepatology, Hirslanden Klinik, Zurich, Switzerland
P Elbe Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Division of Surgery, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
F Baldaque-Silva Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Center for Advanced Endoscopy Carlos Moreira da Silva, Gastroenterology Department, Pedro Hispano Hospital, Matosinhos, Portugal
M Barret Department of Gastroenterology and Hepatology, Cochin Hospital Paris, Paris, France
J Ortiz Fernández-Sordo Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, UK
G Moral Villarejo Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, UK
O Pech Department of Gastroenterology and Hepatology, St John of God Hospital, Regensburg, Germany
T Beyna Department of Gastroenterology and Hepatology, Evangalisches Krankenhaus Düsseldorf, Düsseldorf, Germany
F van der Sommen Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
P H de With Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
A J de Groof Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
J J Bergman Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.

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Patel A, Arora GS, Roknsharifi M, Kaur P, Javed H. Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review. Cureus 2023;15:e47755. [PMID: 38021699 PMCID: PMC10676286 DOI: 10.7759/cureus.47755] [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: 10/26/2023] [Indexed: 12/01/2023] Open

Wang Q, Liu Y, Li Z, Tang Y, Long W, Xin H, Huang X, Zhou S, Wang L, Liang B, Li Z, Xu M. Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation. Front Immunol 2023;14:1182277. [PMID: 37215115 PMCID: PMC10196375 DOI: 10.3389/fimmu.2023.1182277] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/21/2023] [Indexed: 05/24/2023] Open

Abstract

Background

Gastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy.

Methods

To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy.

Results

Eight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features.

Conclusion

In summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.

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Hussein M, Lines D, González-Bueno Puyal J, Kader R, Bowman N, Sehgal V, Toth D, Ahmad OF, Everson M, Esteban JM, Bisschops R, Banks M, Haefner M, Mountney P, Stoyanov D, Lovat LB, Haidry R. Computer-aided characterization of early cancer in Barrett's esophagus on i-scan magnification imaging: a multicenter international study. Gastrointest Endosc 2023;97:646-654. [PMID: 36460087 PMCID: PMC10590905 DOI: 10.1016/j.gie.2022.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/08/2022] [Accepted: 11/18/2022] [Indexed: 12/02/2022]

Abstract

BACKGROUND AND AIMS

We aimed to develop a computer-aided characterization system that could support the diagnosis of dysplasia in Barrett's esophagus (BE) on magnification endoscopy.

METHODS

Videos were collected in high-definition magnification white-light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and nondysplastic BE (NDBE) from 4 centers. We trained a neural network with a Resnet101 architecture to classify frames as dysplastic or nondysplastic. The network was tested on 3 different scenarios: high-quality still images, all available video frames, and a selected sequence within each video.

RESULTS

Fifty-seven patients, each with videos of magnification areas of BE (34 dysplasia, 23 NDBE), were included. Performance was evaluated by a leave-1-patient-out cross-validation method. In all, 60,174 (39,347 dysplasia, 20,827 NDBE) magnification video frames were used to train the network. The testing set included 49,726 i-scan-3/optical enhancement magnification frames. On 350 high-quality still images, the network achieved a sensitivity of 94%, specificity of 86%, and area under the receiver operator curve (AUROC) of 96%. On all 49,726 available video frames, the network achieved a sensitivity of 92%, specificity of 82%, and AUROC of 95%. On a selected sequence of frames per case (total of 11,471 frames), we used an exponentially weighted moving average of classifications on consecutive frames to characterize dysplasia. The network achieved a sensitivity of 92%, specificity of 84%, and AUROC of 96%. The mean assessment speed per frame was 0.0135 seconds (SD ± 0.006).

CONCLUSION

Our network can characterize BE dysplasia with high accuracy and speed on high-quality magnification images and sequence of video frames, moving it toward real-time automated diagnosis.

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Affiliation(s)

Mohamed Hussein Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
David Lines Odin Vision, UK
Juana González-Bueno Puyal Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Odin Vision, UK
Rawen Kader Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
Nicola Bowman Division of Surgery and Interventional Sciences, University College London, UK
Vinay Sehgal Department of Gastroenterology, University College London Hospital, UK
Daniel Toth Odin Vision, UK
Omer F Ahmad Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
Martin Everson Division of Surgery and Interventional Sciences, University College London, UK
Jose Miguel Esteban Department of Gastroenterology and Hepatology, Clínico San Carlos, Madrid, Spain
Raf Bisschops Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
Matthew Banks Department of Gastroenterology, University College London Hospital, UK
Michael Haefner Krankenhaus der Barmherzigen Schwestern, Department of Internal Medicine II, Vienna, Austria
Peter Mountney Odin Vision, UK
Danail Stoyanov Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
Laurence B Lovat Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
Rehan Haidry Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK

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Chadebecq F, Lovat LB, Stoyanov D. Artificial intelligence and automation in endoscopy and surgery. Nat Rev Gastroenterol Hepatol 2023;20:171-182. [PMID: 36352158 DOI: 10.1038/s41575-022-00701-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/03/2022] [Indexed: 11/10/2022]

Meinikheim M, Messmann H, Ebigbo A. Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia. Clin Endosc 2023;56:14-22. [PMID: 36646423 PMCID: PMC9902686 DOI: 10.5946/ce.2022.247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/25/2022] [Indexed: 01/18/2023] Open

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

Zanetto A. UEG week and UEG journal: A strong scientific liaison. United European Gastroenterol J 2022;10:901-905. [PMID: 36126040 PMCID: PMC9557967 DOI: 10.1002/ueg2.12312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open

Schmitz R, Kather JN. Artificial intelligence in Barrett's oesophagus and the need for shared and combined data. United European Gastroenterol J 2022;10:525-527. [PMID: 35704382 DOI: 10.1002/ueg2.12260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open