{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T14:32:41Z","timestamp":1790778761013,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Digestive Endoscopy"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Virtual Chromoendoscopy (VCE) is pivotal for assessing activity and predicting outcomes in Ulcerative Colitis (UC), though interobserver variability and the need for expertise persist. Artificial intelligence (AI) offers standardized VCE\u2010based assessment. This study introduces a novel AI model to detect and simultaneously generate various endoscopic modalities, enhancing AI\u2010driven inflammation assessment and outcome prediction in UC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Endoscopic videos in high\u2010definition white\u2010light, iScan2, iScan3, and NBI from UC patients of the international PICaSSO iScan and NBI cohort (302 and 54 patients, respectively) were used to develop a neural network to identify the acquisition modality of each frame and for inter\u2010modality image switching. 2535 frames from 169 videos of the iScan cohort were switched to different modalities and trained a deep\u2010learning model for inflammation assessment. Subsequently, the model was tested on a subset of the iScan and NBI cohorts (72 and 51 videos, respectively). Performance in predicting endoscopic and histological activity and outcomes was evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model efficiently classified and converted images across modalities (92% accuracy). Performance in predicting endoscopic and histological remission was excellent, especially with different modalities combined in both iScan (accuracy 81.3% and 89.6%; AUROC 0.92 and 0.89 by UCEIS and PICaSSO, respectively) and the NBI cohort. Moreover, it showed a remarkable ability in predicting clinical outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our multimodal \u201cAI\u2010switching\u201d model innovatively detects and transitions between different endoscopic modalities, refining inflammation assessment and outcome prediction in UC by integrating model\u2010derived images.<\/jats:p><\/jats:sec>","DOI":"10.1111\/den.15067","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T04:09:33Z","timestamp":1750046973000},"page":"1078-1088","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Novel Switching of Artificial Intelligence to Generate Simultaneously Multimodal Images to Assess Inflammation and Predict Outcomes in Ulcerative Colitis\u2014(With 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University College of Cork  Cork Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bisi Bode","family":"Kolawole","sequence":"additional","affiliation":[{"name":"School of Engineering Computer Science and Informatics London South Bank University  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ujwala","family":"Chaudhari","sequence":"additional","affiliation":[{"name":"School of Engineering Computer Science and Informatics London South Bank University  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rocio","family":"del Amor","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, HUMAN\u2010Tech Universitat Polit\u00e8cnica de Val\u00e8ncia  Val\u00e8ncia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"Meseguer","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, HUMAN\u2010Tech Universitat Polit\u00e8cnica de Val\u00e8ncia  Val\u00e8ncia Spain"},{"name":"valgrAI\u2014Valencian Graduate School and Research Network of Artificial Intelligence  Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valery","family":"Naranjo","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, HUMAN\u2010Tech Universitat Polit\u00e8cnica de Val\u00e8ncia  Val\u00e8ncia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Puga\u2010Tejada","sequence":"additional","affiliation":[{"name":"APC Microbiome Ireland, College of Medicine and Health University College of Cork  Cork Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Capobianco","sequence":"additional","affiliation":[{"name":"APC Microbiome Ireland, College of Medicine and Health University College of Cork  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