ArticleInflammatory bowel diseases2025
Deployment of an Artificial Intelligence Histology Tool to Aid Qualitative Assessment of Histopathology Using the Nancy Histopathology Index in Ulcerative Colitis.
Article in Inflammatory bowel diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-enabled histology exhibits comparable accuracy to pathologists in assessing histological remission in ulcerative colitis: a systematic review, meta-analysis, and meta-regression.Journal of Crohn's & colitis · 2025Pooled it
- Global Roll-Out and Continued Multi-Site Validation of the Artificial Intelligence Histology Instrument for Qualitative Assessment of Histopathology in Ulcerative Colitis.United European gastroenterology journal · 2026Article
- Current approaches to disease severity and therapy effectiveness assessment in patients with inflammatory bowel disease.World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Potential of Clinical Care-Collected Gut Biopsies for Advancing Personalised Medicine in Inflammatory Bowel Disease.United European gastroenterology journal · 2026Review
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Deep learning-based quantitative histopathology of endoscopic biopsies in Crohn's disease: a retrospective cross-sectional validation study.Frontiers in immunology · 2026Article
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.International journal of general medicine · 2026Review
- Histological remission as a reliable predictor of clinical outcomes in patients with ulcerative colitis after conventional therapy: a 5-year analysis.BMC gastroenterology · 2025Article
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Rediscovering histology - the application of artificial intelligence in inflammatory bowel disease histologic assessment.Therapeutic advances in gastroenterology · 2025Review
- Zuoqing granules attenuate ulcerative colitis via macrophage polarization modulation: involvement of the PPAR-γ/NF-κB/STAT1 signaling axis.Frontiers in pharmacology · 2025Article
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21 authors.
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Abstract
backgroundUlcerative colitis (UC) is a chronic inflammatory bowel disease characterized by increased stool frequency, rectal bleeding, and urgency. To streamline the quantitative assessment of histopathology using the Nancy Index in UC patients, we developed a novel artificial intelligence (AI) tool based on deep learning and tested it in a proof-of-concept trial. In this study, we report the performance of a modified version of the AI tool.
methodsNine sites from 6 countries were included. Patients were aged ≥18 years and had UC. Slides were prepared with hematoxylin and eosin staining. A total of 791 images were divided into 2 groups: 630 for training the tool and 161 for testing vs expert histopathologist assessment. The refined AI histology tool utilized a 4-neural network structure to characterize images into a series of cell and tissue type combinations and locations, and then 1 classifier module assigned a Nancy Index score.
resultsIn comparison with the proof-of-concept tool, each feature demonstrated an improvement in accuracy. Confusion matrix analysis demonstrated an 80% correlation between predicted and true labels for Nancy scores of 0 or 4; a 96% correlation for a true score of 0 being predicted as 0 or 1; and a 100% correlation for a true score of 2 being predicted as 2 or 3. The Nancy metric (which evaluated Nancy Index prediction) was 74.9% compared with 72.3% for the proof-of-concept model.
conclusionsWe have developed a modified AI histology tool in UC that correlates highly with histopathologists' assessments and suggests promising potential for its clinical application.
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