ArticleUnited European gastroenterology journal2024
An artificial intelligence-driven scoring system to measure histological disease activity in ulcerative colitis.
Article in United European gastroenterology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 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
- Automated Bergmann-Kliesch Score assessment in testicular biopsies using artificial intelligence-based whole-slide image analysis: a retrospective monocentric development and validation study.Basic and clinical andrology · 2026Article
- REMIL-IBD: Region-filtered multiple instance learning for interpretable slide-level grading of inflammatory bowel disease.Journal of pathology informatics · 2026Article
- Standardized, repeatable ulcerative colitis histology scoring and endpoint assessment using an automated, foundation model-based tool.medRxiv : the preprint server for health sciences · 2026Article
- 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
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Deep learning-based quantitative histopathology of endoscopic biopsies in Crohn's disease: a retrospective cross-sectional validation study.Frontiers in immunology · 2026Article
- Review
- Development of an AI model to predict tooth movement during orthodontic treatment.Bioinformation · 2026Article
- Artificial intelligence in inflammatory bowel disease: Current applications and future directions.World journal of gastroenterology · 2025Review
- Histologic Assessment in Ulcerative Colitis: A Survey of Pathologists' Practices and Perspectives.Gastroenterology research · 2025Article
- An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.United European gastroenterology journal · 2025Article
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Deployment of an Artificial Intelligence Histology Tool to Aid Qualitative Assessment of Histopathology Using the Nancy Histopathology Index in Ulcerative Colitis.Inflammatory bowel diseases · 2025Article
- Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology.The American journal of pathology · 2025Article
- Rediscovering histology - the application of artificial intelligence in inflammatory bowel disease histologic assessment.Therapeutic advances in gastroenterology · 2025Review
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.Therapeutic advances in gastroenterology · 2025Review
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Machine-learning for quantitative histopathology of piglet intestinal tissues: challenges with limited training data.Frontiers in veterinary science · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
BACKGROUND AND
aimsAssessment and scoring of histological images in Ulcerative colitis (UC) is prone to inter- and intra-observer variability. This study aimed to investigate whether an artificial intelligence (AI) system developed using image processing and machine learning algorithms could measure histological disease activity based on the Nancy index.
methodsA total of 200 histological images of patients with UC were used in this study. A novel AI algorithm was developed using state-of-the-art image processing and machine learning algorithms based on deep learning and feature extraction. The cell regions of each image, followed by the Nancy index, were manually annotated and measured independently by four histopathologists. Manual and AI-automated measurements of the Nancy index score were conducted and assessed using the intraclass correlation coefficient (ICC).
resultsThe 200-image dataset was divided into two groups (80% was used for training and 20% for testing). Intraclass correlation coefficient statistical analyses were performed to evaluate the AI tool and used as a reference to calculate the accuracy. The average ICC among the histopathologists was 89.3 and the average ICC between histopathologists and the AI tool was 87.2. The AI tool was found to be highly correlated with histopathologists.
conclusionsThe high correlation of performance of the AI method suggests promising potential for inflammatory bowel disease clinical applications. A standardized automated histological AI-driven scoring system can potentially be used in daily inflammatory bowel disease practice to reduce training needs and resource use, eliminate the subjectivity of the pathologists, and assess disease severity for treatment decisions.
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