What question did this study set out to answer?

To review the accuracy and clinical utility of AI-based systems in endoscopic assessment of Crohn's disease activity and severity.

February 16, 2026Open Access

Poster Session I - A57 ARTIFICIAL INTELLIGENCE–ASSISTED ENDOSCOPIC ASSESSMENT OF CROHN’S DISEASE SEVERITY: A SYSTEMATIC REVIEW

Key Points

To review the accuracy and clinical utility of AI-based systems in endoscopic assessment of Crohn's disease activity and severity.
Conducted a systematic search following PRISMA guidelines across several databases including Ovid MEDLINE and Embase.
Identified studies exploring AI tools for assessing endoscopic disease activity in Crohn's disease.
Extracted statistical measures and performance metrics from qualifying studies.
Included five studies published between 2021 and 2024 using convolutional neural networks.
Models analyzed endoscopic images, comparing outputs against expert evaluations.
Accuracy ranged from 62.4% to 98.4%, with sensitivity from 32.4% to 98.9% and specificity from 71.2% to 99.8%.

Abstract

Abstract Background Endoscopic assessment is integral in guiding the management and monitoring of Crohn’s Disease (CD). While manual scoring systems are widely used, they are subject to significant inter-rater variability. Artificial intelligence (AI) has shown utility in assessing disease activity in ulcerative colitis, yet its role in CD remains underexplored. AI-based tools could improve standardization and efficiency in CD assessment with potential benefits for both clinical practice and research. Aims Review the accuracy and potential clinical utility of artificial intelligence-based systems in the endoscopic assessment of CD activity and severity. Methods A systematic search of Ovid MEDLINE, Embase, Pubmed, Scopus, and Cochrane database was performed using PRISMA guidelines to identify publications exploring the use of AI-based tools to assess endoscopic disease activity in CD. Studies meeting inclusion criteria were reviewed and statistical measures and other performance metrics were extracted. Results Five studies, published between 2021 and 2024, were included. All used convolutional neural networks to analyze still images from capsule or double-balloon endoscopy, comparing model output against expert readings. Each study used a different classification system for mucosal abnormalities, including ulcer severity, inflammatory stricturing, and villous changes. The accuracy, sensitivity, specificity, area-under-the-curve for the models ranged from 62.4-98.4%, 32.4-98.9%, 71.2-99.8%, and 0.565-0.989 respectively. Conclusions AI-based tools show considerable promise in the endoscopic assessment of CD severity. However, heterogeneity in their design and performance underscores the need for further validation and standardization prior to adoption in clinical practice. A57 Table 1: Publications assessing the utility of artificial intelligence in the endoscopic assessment of Crohn’s disease Funding Agencies None

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