A Scoping Review of Endoscopic Assessment Considering Disease Extent in Ulcerative Colitis: Insights for the Artificial Intelligence Era.

Conventional endoscopic indices for ulcerative colitis (UC) primarily assess the most severely affected segment, potentially underestimating the cumulative inflammatory burden across the colon. Consequently, there is increasing interest in assessment methods that incorporate both disease severity and spatial extent, including emerging artificial intelligence (AI)-based approaches. The aim of this study was to map and critically evaluate current evidence on endoscopic indices that integrate disease extent in UC, including both conventional and AI-assisted approaches. Original studies in which patients with UC were evaluated via endoscopic approaches for disease activity, severity, or extent and clinically relevant outcomes were reported. Non-original articles and studies of other diseases were excluded. PubMed/MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 2000 and December 2025. Two reviewers independently screened studies and extracted data. Findings were synthesized narratively due to heterogeneity. Twenty-one studies were included: eight addressed index development or validation, 11 evaluated clinical outcomes, and four investigated AI-assisted assessments, with some studies contributing to multiple categories. Extent-based indices showed stronger correlations with biomarkers and histological activity than conventional focal scores, although their ability to predict relapse was inconsistent. AI-based systems enabled automated assessment of inflammatory distribution, improving reproducibility and showing promising clinical associations, but external validation remains limited. Extent-integrated endoscopic assessment provides a more comprehensive evaluation of disease burden in UC patients and may improve risk stratification and treatment monitoring. However, methodological heterogeneity and limited prospective validation hinder clinical implementation. Further standardization and multicenter studies are needed.