Diagnostic par l’intelligence artificielle dans les maladies inflammatoires de l’intestin : applications et orientations futures
Résumé
Principaux points à retenir
• L’IA peut améliorer la précision, l’objectivité et la reproductibilité des évaluations des MII à travers de nombreux indices d’évaluation de la maladie.
• Plusieurs modèles d’IA ont fait preuve de performances dont le niveau était celui d’un expert dans l’évaluation de l’activité des MII sur le plan endoscopique et histologique.
• Le déploiement de modèles d’IA peut contribuer à uniformiser la qualité de l’évaluation des maladies dans les centres universitaires comme dans les centres communautaires.
• Les prochaines étapes feront intervenir des modèles d’IA multimodaux. La mise au point de ces modèles et des systèmes unimodaux nécessitera des ensembles de données importants et diversifiés, ainsi qu’une gestion rigoureuse.
Références
Turner D, Ricciuto A, Lewis A, D’Amico F, Dhaliwal J, Griffiths AM, et al. STRIDE-II: an update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology. 2021;160(5):1570–1583. doi:10.1053/j.gastro.2020.12.031
Sandborn WJ, Feagan BG, Hanauer SB, Lochs H, Lofberg R, Modigliani R, et al. A review of activity indices and efficacy endpoints for clinical trials of medical therapy in adults with Crohn’s disease. Gastroenterology. 2002;122(2):512–530. doi:10.1053/gast.2002.31072
Burdine LK, Rakowsky S, Grossberg L, Rabinowitz L, Center BIR, Cheifetz AS, et al. Irritable bowel syndrome/inflammatory bowel disease overlap: less common than we think. Gastro Hep Adv. 2024;3(8):1135–1137. Published 2024 Aug 10. doi:10.1016/j.gastha.2024.08.005
Fairbrass KM, Costantino SJ, Gracie DJ, Ford AC. Prevalence of irritable bowel syndrome-type symptoms in patients with inflammatory bowel disease in remission: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2020;5(12):1053–1062. doi:10.1016/S2468-1253(20)30300-9
Ricci L, Toussaint Y, Becker J, Najjar H, Renier A, Choukour M, et al. Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease. Dig Liver Dis. 2022;54(4):483–489. doi:10.1016/j.dld.2021.09.005
Patel PV, Davis C, Ralbovsky A, Tinoco D, Williams CYK, Slatter S, et al. Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in inflammatory bowel disease. Gastro Hep Adv. 2025;4(2):100563. Published 2024 Oct 10. doi:10.1016/j.gastha.2024.10.003
Stidham RW, Yu D, Zhao X, Bishu S, Rice M, Bourque C, et al. Identifying the Presence, Activity, and Status of Extraintestinal Manifestations of Inflammatory Bowel Disease Using Natural Language Processing of Clinical Notes. Inflamm Bowel Dis. 2023;29(4):503–510. doi:10.1093/ibd/izac109
Hirten RP, Danieletto M, Sanchez-Mayor M, Whang JK, Lee KW, Landell K, et al. Physiological data collected from wearable devices identify and predict inflammatory bowel disease flares. Gastroenterology. 2025;168(5):939–951.e5. doi:10.1053/j.gastro.2024.12.024
Shahub S, Kumar RM, Lin KC, Banga I, Choi NK, Garcia NM, et al. Continuous monitoring of CRP, IL-6, and calprotectin in inflammatory bowel disease using a perspiration-based wearable device. Inflamm Bowel Dis. 2025;31(3):647–654. doi:10.1093/ibd/izae054
Khanna R, Zou G, D’Haens G, Rutgeerts P, McDonald JW, Daperno M, et al. Reliability among central readers in the evaluation of endoscopic findings from patients with Crohn’s disease. Gut. 2016;65(7):1119–1125. doi:10.1136/gutjnl-2014-308973
Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central reading of ulcerative colitis clinical trial videos using neural networks. Gastroenterology. 2021;160(3):710–719.e2. doi:10.1053/j.gastro.2020.10.024
Fan Y, Mu R, Xu H, Xie C, Zhang Y, Liu L, et al. Novel deep learning-based computer-aided diagnosis system for predicting inflammatory activity in ulcerative colitis. Gastrointest Endosc. 2023;97(2):335–346. doi:10.1016/j.gie.2022.08.015
Kim JE, Choi YH, Lee YC, Seong G, Song JH, Kim TJ, et al. Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis. Sci Rep. 2023;13(1):11351. Published 2023 Jul 13. doi:10.1038/s41598-023-38206-6
Khanna R, Feagan BG, Zou G, Stitt LW, McDonald JWD, Bressler B, et al. Reliability and responsiveness of clinical and endoscopic outcome measures in Crohn’s disease. Inflamm Bowel Dis. 2025;31(3):706–715. doi:10.1093/ibd/izae089
Cai L, Wittrup E, Minoccheri C, Hiatt TK, Rice MD, Bishu S, et al. Artificial intelligence for quantifying endoscopic mucosal ulceration in Crohn’s disease. Clin Gastroenterol Hepatol. Published online August 18, 2025. doi:10.1016/j.cgh.2025.05.026
Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, Gonzalez-Suarez B, et al. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health. 2024;6(5):e345–e353. doi:10.1016/S2589-7500(24)00048-7
Fan S, Xu L, Fan Y, Wei K, Li L. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol. 2018;63(16):165001. Published 2018 Aug 10. doi:10.1088/1361-6560/aad51c
Barash Y, Azaria L, Soffer S, Margalit Yehuda R, Shlomi O, Ben-Horin S, et al. Ulcer severity grading in video capsule images of patients with Crohn’s disease: an ordinal neural network solution. Gastrointest Endosc. 2021;93(1):187–192. doi:10.1016/j.gie.2020.05.066
Cardoso P, Mascarenhas M, Afonso J, Ribeiro T, Mendes F, Martins M, et al. Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network. Therap Adv Gastroenterol. 2024;17:17562848241251569. Published 2024 May 27. doi:10.1177/17562848241251569
Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis. 2024;56(7):1164–1172. doi:10.1016/j.dld.2023.11.005
Najdawi F, Sucipto K, Mistry P, Hennek S, Jayson CKB, Lin M, et al. Artificial intelligence enables quantitative assessment of ulcerative colitis histology. Mod Pathol. 2023;36(6):100124. doi:10.1016/j.modpat.2023.100124
Iacucci M, Parigi TL, Del Amor R, Meseguer P, Mandelli G, Bozzola A, et al. Artificial intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis. Gastroenterology. 2023;164(7):1180–1188.e2. doi:10.1053/j.gastro.2023.02.031
Chen H, Lin X, Pan X, Xu H, Zhang X, Liang G, et al. Development and validation of a blood routine-based extent and severity clinical decision support tool for ulcerative colitis. Sci Rep. 2023;13(1):21368. Published 2023 Dec 4. doi:10.1038/s41598-023-48569-5
Jairath V, Jeyarajah J, Zou G, Parker CE, Olson A, Khanna R, et al. A composite disease activity index for early drug development in ulcerative colitis: development and validation of the UC-100 score. Lancet Gastroenterol Hepatol. 2019;4(1):63–70. doi:10.1016/S2468-1253(18)30306-6
He M, Li C, Tang W, Kang Y, Zuo Y, Wang Y. Machine learning gene expression predicting model for ustekinumab response in patients with Crohn’s disease. Immun Inflamm Dis. 2021;9(4):1529–1540. doi:10.1002/iid3.506
Stidham RW, Liu Y, Enchakalody B, Van T, Krishnamurthy V, Su GL, et al. the use of readily available longitudinal data to predict the likelihood of surgery in Crohn disease. Inflamm Bowel Dis. 2021;27(8):1328–1334. doi:10.1093/ibd/izab035
Iacucci M, Santacroce G, Meseguer P, Dieguez A, Del Amor R, Kolawole BB, et al. Endo-Histo foundational fusion model: a novel artificial intelligence for assessing histologic remission and response to therapy in ulcerative colitis clinical trial. J Crohns Colitis. 2025;19(7):jjaf108. doi:10.1093/ecco-jcc/jjaf108
Jong MR, Boers TGW, Fockens KN, Jukema JB, Kusters CHJ, Jaspers TJM, et al. GastroNet-5M: a multicenter dataset for developing foundation models in gastrointestinal endoscopy. Gastroenterology. Published online July 30, 2025. doi:10.1053/j.gastro.2025.07.030
Budzyn K, Romanczyk M, Kitala D, Kolodziej P, Bugajski M, Adami HO, et al. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. Lancet Gastroenterol Hepatol. 2025;10(10):896–903. doi:10.1016/S2468-1253(25)00133-5
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