AI-based quantification of inflammatory extent for relapse prediction in ulcerative colitis: a prospective cohort study
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Date
2026-07-30
Authors
Maeda, Yasuharu
Kudo, Shin Ei
Ogata, Noriyuki
Takenaka, Kento
Takabayashi, Kaoru
Kuroki, Takanori
Kawabata, Yurie
Okumura, Taishi
Sakurai, Tatsuya
Kouyama, Yuta
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Abstract
Background and Aims: Endoscopic remission is a key therapeutic goal in ulcerative colitis (UC); however, conventional indices focus on peak severity without accounting for the spatial extent of inflammation. We developed a deep learning-based score, Quantitative Ulcerative Colitis Assessment using Deep Learning (QUAD), and evaluated whether incorporating inflammatory extent improves relapse prediction in patients with UC in clinical remission. To our knowledge, no studies have examined whether artificial intelligence (AI)-derived assessment of inflammatory extent predicts clinical relapse. Methods: This prospective cohort study evaluated relapse prediction over 24 months in patients with UC in clinical remission. The QUAD model assigns a score of 0-3 to each image quadrant, yielding a total score of 0-12. The model was trained on 84 743 images from 998 patients with UC across three centers. Still image-based validation and automated full-length video analysis were conducted to assess the impact of inflammatory extent on relapse prediction. Results: Clinical relapse occurred in 19.4% of patients with QUAD ≥ 4 compared with 5.2% of those with QUAD < 4 (P = .01), with an area under the curve (AUC) of 0.67 (95% confidence interval [CI]: 0.57-0.76). Notably, automated video-based analysis showed that distal inflammatory burden yielded the highest predictive performance, with analysis of the distal 10% segment achieving an AUC of 0.73 (95% CI: 0.62-0.85), compared with whole-colon assessment (AUC, 0.62; 95% CI: 0.48-0.76) and still image-based evaluation. Conclusion: AI-augmented assessment integrating inflammatory severity and extent may provide complementary prognostic information beyond severity-based evaluation.
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© 2026, the Author(s) . Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords
Deep learning , Endoscopic remission , Relapse prediction , [Medicine] , [APCMicrobiome] , Gastroenterology
Citation
Maeda, Y, Kudo, S E, Ogata, N, Takenaka, K, Takabayashi, K, Kuroki, T, Kawabata, Y, Okumura, T, Sakurai, T, Kouyama, Y, Ichimasa, K, Hayashi, T, Baba, T, Ogata, H, Ohtsuka, K, Mori, Y, Iacucci, M & Misawa, M 2026, 'AI-based quantification of inflammatory extent for relapse prediction in ulcerative colitis: a prospective cohort study', Journal of Crohn's and Colitis, vol. 20, no. 7, jjag115, pp. 1-10. https://doi.org/10.1093/ecco-jcc/jjag115
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