AI-based quantification of inflammatory extent for relapse prediction in ulcerative colitis: a prospective cohort study

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dc.contributor.authorMaeda, Yasuharu
dc.contributor.authorKudo, Shin Ei
dc.contributor.authorOgata, Noriyuki
dc.contributor.authorTakenaka, Kento
dc.contributor.authorTakabayashi, Kaoru
dc.contributor.authorKuroki, Takanori
dc.contributor.authorKawabata, Yurie
dc.contributor.authorOkumura, Taishi
dc.contributor.authorSakurai, Tatsuya
dc.contributor.authorKouyama, Yuta
dc.contributor.authorIchimasa, Katsuro
dc.contributor.authorHayashi, Takemasa
dc.contributor.authorBaba, Toshiyuki
dc.contributor.authorOgata, Haruhiko
dc.contributor.authorOhtsuka, Kazuo
dc.contributor.authorMori, Yuichi
dc.contributor.authorIacucci, Marietta
dc.contributor.authorMisawa, Masashi
dc.contributor.funderJapan Society for the Promotion of Science
dc.contributor.funderTakeda Science Foundation
dc.date.accessioned2026-09-02T14:36:01Z
dc.date.available2026-09-02T14:36:01Z
dc.date.issued2026-07-30
dc.description© 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.
dc.description.abstractBackground 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.en
dc.description.sponsorshipJapan Society for the Promotion of Science|JP23K09537|KAKENHI the Takeda Science Foundation, and The Japanese Foundation for Research and Promotion of Endoscopy Grant.
dc.format.extent10
dc.format.extent4576526
dc.format.extent1332578
dc.identifier.articleidjjag115
dc.identifier.authororcidMaeda, Yasuharu
dc.identifier.authororcidKudo, Shin Ei
dc.identifier.authororcidOgata, Noriyuki
dc.identifier.authororcidTakenaka, Kento
dc.identifier.authororcidTakabayashi, Kaoru
dc.identifier.authororcidKuroki, Takanori
dc.identifier.authororcidKawabata, Yurie
dc.identifier.authororcidOkumura, Taishi
dc.identifier.authororcidSakurai, Tatsuya
dc.identifier.authororcidKouyama, Yuta
dc.identifier.authororcidIchimasa, Katsuro
dc.identifier.authororcidHayashi, Takemasa
dc.identifier.authororcidBaba, Toshiyuki
dc.identifier.authororcidOgata, Haruhiko
dc.identifier.authororcidOhtsuka, Kazuo
dc.identifier.authororcidMori, Yuichi
dc.identifier.authororcidIacucci, Marietta
dc.identifier.authororcidMisawa, Masashi
dc.identifier.citationMaeda, 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
dc.identifier.doi10.1093/ecco-jcc/jjag115
dc.identifier.endpage10
dc.identifier.issn1873-9946
dc.identifier.issued7
dc.identifier.journaltitleJournal of Crohn's and Colitis
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/19168
dc.identifier.urlhttps://www.scopus.com/pages/publications/105046204346
dc.identifier.volume20
dc.language.isoeng
dc.rightscc_by
dc.rightsother
dc.subjectDeep learning
dc.subjectEndoscopic remission
dc.subjectRelapse prediction
dc.subject[Medicine]
dc.subject[APCMicrobiome]
dc.subjectGastroenterology
dc.titleAI-based quantification of inflammatory extent for relapse prediction in ulcerative colitis: a prospective cohort studyen
dc.typeArticle (Peer reviewed)
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