ArticleEndoscopy2023
A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis.
Article in Endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 49 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Endoscopic Multimodal Assessment Using Advanced Imaging Integrated AI to Predict Recurrence in pOSt-oPerativE CRohn's Disease - PROSPER Study
A Novel Simplified Endoscopic Score aiMed at Evaluating Ulcerative cOlitis Activity Through TXI, RDI and NBI Vascular Assessment and at prEdicting Clinical Outcome and Its Applicability in an arTificial Intelligence System: the MONET Study
Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Prognostic Prediction Models for Ulcerative Colitis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Defining mucosal healing in randomized controlled trials of inflammatory bowel disease: A systematic review and future perspective.United European gastroenterology journal · 2024Pooled it
- Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Advanced endoscopy and artificial intelligence-enabled vascular healing for ulcerative colitis: promising frontiers or mere mirage?Clinical endoscopy · 2026Review
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- Current approaches to disease severity and therapy effectiveness assessment in patients with inflammatory bowel disease.World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Artificial intelligence in inflammatory bowel disease: bridging innovation, implementation and impact.Nature reviews. Gastroenterology & hepatology · 2026Review
- New Technologies for IBD Endoscopy.Journal of clinical medicine · 2026Review
- Harnessing artificial intelligence in gastrointestinal endoscopy for early detection of dysplastic lesions in inflammatory bowel disease.World journal of gastrointestinal endoscopy · 2026Review
- Beyond conventional endoscopy: Image-enhanced techniques in quiescent ulcerative colitis assessment.World journal of gastrointestinal endoscopy · 2026Review
- An automatic severity grading system using confocal laser endomicroscopy to evaluate inflammatory activity of ulcerative colitis: a prospective study.Scientific reports · 2025Article
- Artificial intelligence in inflammatory bowel disease: Current applications and future directions.World journal of gastroenterology · 2025Review
- A Novel Switching of Artificial Intelligence to Generate Simultaneously Multimodal Images to Assess Inflammation and Predict Outcomes in Ulcerative Colitis-(With Video).Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Endo-Histo foundational fusion model: a novel artificial intelligence for assessing histologic remission and response to therapy in ulcerative colitis clinical trial.Journal of Crohn's & colitis · 2025Article
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Review
- Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study.Journal of Crohn's & colitis · 2025Article
Corrections and comments
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Authors and funding
16 authors at 10 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEndoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos.
methods1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured.
resultsThe AI system detected ER (UCEIS ≤ 1) in WLE videos with 72 % sensitivity, 87 % specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO ≤ 3), the sensitivity was 79 %, specificity 95 %, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80 % to 85 %). The model's stratification of risk of flare was similar to that of physician-assessed endoscopy scores.
conclusionsOur system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.