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CC BY-NC-ND 4.0 · Endosc Int Open 2022; 10(07): E1004-E1013
DOI: 10.1055/a-1846-0642
Review

Clinical application and diagnostic accuracy of artificial intelligence in colonoscopy for inflammatory bowel disease: systematic review

Linda S. Yang
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
,
Evelyn Perry
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
,
Leonard Shan
2   Department of Surgery, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Fitzroy, Victoria, Australia
,
Helen Wilding
3   Library Service, St. Vincent’s Hospital Melbourne, Fitzroy, Victoria, Australia
,
William Connell
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
,
Alexander J. Thompson
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
,
Andrew C. F. Taylor
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
,
Paul V. Desmond
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
,
Bronte A. Holt
1   Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
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Abstract

Background and aims Artificial intelligence (AI) technology is being evaluated for its potential to improve colonoscopic assessment of inflammatory bowel disease (IBD), particularly with computer-aided image classifiers. This review evaluates the clinical application and diagnostic test accuracy (DTA) of AI algorithms in colonoscopy for IBD.

Methods A systematic review was performed on studies evaluating AI in colonoscopy of adult patients with IBD. MEDLINE, Embase, Emcare, PsycINFO, CINAHL, Cochrane Library and Clinicaltrials.gov databases were searched on 28th April 2021 for English language articles published between January 1, 2000 and April 28, 2021. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic accuracy was presented as median (interquartile range).

Results Of 1029 records screened, nine studies with 7813 patients were included for review. AI was used to predict endoscopic and histologic disease activity in ulcerative colitis, and differentiation of Crohn’s disease from Behcet’s disease and intestinal tuberculosis. DTA of AI algorithms ranged between 52–91 %. The sensitivity and specificity for AI algorithms predicting endoscopic severity of disease were 78 % (range 72–83, interquartile range 5.5) and 91 % (range 86–96, interquartile range 5), respectively.

Conclusions AI has been primarily used to assess disease activity in ulcerative colitis. The diagnostic performance is promising and suggests potential for other clinical application of AI in IBD colonoscopy such as dysplasia detection. However, current evidence is limited by retrospective data and models trained on still images only. Future prospective multicenter studies with full-motion videos are needed to replicate the real-world clinical setting.

Supplementary material



Publication History

Received: 30 November 2021

Accepted after revision: 02 May 2022

Article published online:
15 July 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 GBD 2017 Inflammatory Bowel Diseaes Collaborators. The global, regional, and national burden of inflammatory bowel disease in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol 2020; 5: 17-30
    CrossrefPubMedGoogle Scholar
  • 2 Riddell RH, Goldman H, Ransohoff DF. et al. Dysplasia in inflammatory bowel disease: standardized classification with provisional clinical applications. Hum Pathol 1983; 14: 931-968
    CrossrefPubMedGoogle Scholar
  • 3 Australia Cancer Council. Surveillance interval for IBD patients. In: Cancer Council Australia. 2019
    PubMedGoogle Scholar
  • 4 Laine L, Kaltenbach T, Barkun A. et al. SCENIC international consensus statement on surveillance and management of dysplasia in inflammatory bowel disease. Gastroenterology 2015; 148: 639-651 e628
    CrossrefPubMedGoogle Scholar
  • 5 Travis SP, Schnell D, Krzeski P. et al. Developing an instrument to assess the endoscopic severity of ulcerative colitis: the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). Gut 2012; 61: 535-542
    CrossrefPubMedGoogle Scholar
  • 6 Mohammed Vashist N, Samaan M, Mosli MH. et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cochrane Database Syst Rev 2018; 1: CD011450
    PubMedGoogle Scholar
  • 7 Daperno M, DʼHaens G, Van Assche G. et al. Development and validation of a new, simplified endoscopic activity score for Crohnʼs disease: the SES-CD. Gastrointest Endosc 2004; 60: 505-512
    Google Scholar
  • 8 Leoncini G, Donato F, Reggiani-Bonetti L. et al. Diagnostic interobserver variability in Crohnʼs disease- and ulcerative colitis-associated dysplasia: a multicenter digital survey from the IG-IBD Pathologists Group. Tech Coloproctol 2021; 25: 101-108
    CrossrefPubMedGoogle Scholar
  • 9 Misawa M, Kudo SE, Mori Y. et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018; 154: 2027-2029 e2023
    CrossrefPubMedGoogle Scholar
  • 10 Kudo SE, Misawa M, Mori Y. et al. Artificial intelligence-assisted system improves endoscopic identification of colorectal neoplasms. Clin Gastroenterol Hepatol 2020; 18: 1874-1881 e1872
    CrossrefPubMedGoogle Scholar
  • 11 Repici A, Badalamenti M, Maselli R. et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 2020; 159: 512-520 e517
    CrossrefPubMedGoogle Scholar
  • 12 Barua I, Vinsard DG, Jodal HC. et al. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy 2021; 53: 277-284
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Tontini GE, Rimondi A, Vernero M. et al. Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons. Therap Adv Gastroenterol 2021; 14 DOI: 10.1177/17562848211017730.
    CrossrefPubMedGoogle Scholar
  • 14 Gubatan J, Levitte S, Patel A. et al. Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions. World J Gastroenterol 2021; 27: 1920-1935
    CrossrefPubMedGoogle Scholar
  • 15 Sundaram S, Choden T, Mattar MC. et al. Artificial intelligence in inflammatory bowel disease endoscopy: current landscape and the road ahead. Ther Adv Gastrointest Endosc 2021; 14 DOI: 10.1177/26317745211017809.
    CrossrefPubMedGoogle Scholar
  • 16 van der Laan JJH, van der Waaij AM, Gabriels RY. et al. Endoscopic imaging in inflammatory bowel disease: current developments and emerging strategies. Expert Rev Gastroenterol Hepatol 2021; 15: 115-126
    CrossrefPubMedGoogle Scholar
  • 17 McInnes MDF, Moher D, Thombs BD. et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: The PRISMA-DTA Statement. JAMA 2018; 319: 388-396
    CrossrefPubMedGoogle Scholar
  • 18 Whiting PF, Rutjes AW, Westwood ME. et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155: 529-536
    CrossrefPubMedGoogle Scholar
  • 19 Leeflang MM. Systematic reviews and meta-analyses of diagnostic test accuracy. Clin Microbiol Infect 2014; 20: 105-113
    CrossrefPubMedGoogle Scholar
  • 20 Yao H, Najarian K, Gryak J. et al. Fully automated endoscopic disease activity assessment in ulcerative colitis. Gastrointest Endosc 2021; 93: 728-736 e721
    CrossrefPubMedGoogle Scholar
  • 21 Takenaka K, Ohtsuka K, Fujii T. et al. Deep neural network accurately predicts prognosis of ulcerative colitis using endoscopic images. Gastroenterology 2021; 160: 2175-2177 e2173
    CrossrefPubMedGoogle Scholar
  • 22 Takenaka K, Ohtsuka K, Fujii T. et al. Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis. Gastroenterology 2020; 158: 2150-2157
    CrossrefPubMedGoogle Scholar
  • 23 Bhambhvani HP, Zamora A. Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis. Eur J Gastroenterol Hepatol 2021; 33: 645-649
    CrossrefPubMedGoogle Scholar
  • 24 Gottlieb K, Requa J, Karnes W. et al. Central reading of ulcerative colitis clinical trial videos using neural networks. Gastroenterology 2021; 160: 710-719 e712
    CrossrefPubMedGoogle Scholar
  • 25 Gutierrez Becker B, Arcadu F, Thalhammer A. et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Ther Adv Gastrointest Endosc 2021; 14 DOI: 10.1177/2631774521990623.
    CrossrefPubMedGoogle Scholar
  • 26 Ozawa T, Ishihara S, Fujishiro M. et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. Gastrointest Endosc 2019; 89: 416-421 e411
    CrossrefPubMedGoogle Scholar
  • 27 Kim JM, Kang JG, Kim S. et al. Deep-learning system for real-time differentiation between Crohnʼs disease, intestinal Behcetʼs disease, and intestinal tuberculosis. J Gastroenterol Hepatol 2021; 36: 2141-2148
    CrossrefPubMedGoogle Scholar
  • 28 Stidham RW, Liu W, Bishu S. et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw Open 2019; 2: e193963
    CrossrefPubMedGoogle Scholar
  • 29 Queneherve L, David G, Bourreille A. et al. Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases. Gastrointest Endosc 2019; 89: 626-636
    CrossrefPubMedGoogle Scholar
  • 30 Maeda Y, Kudo SE, Mori Y. et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc 2019; 89: 408-415
    CrossrefPubMedGoogle Scholar
  • 31 Bossuyt P, De Hertogh G, Eelbode T. et al. Computer-aided diagnosis with monochromatic light endoscopy for scoring histologic remission in ulcerative colitis. Gastroenterology 2021; 160: 23-25
    CrossrefPubMedGoogle Scholar
  • 32 Bossuyt P, Nakase H, Vermeire S. et al. Automatic, computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density. Gut 2020; 69: 1778-1786
    CrossrefPubMedGoogle Scholar