ArticleFrontiers in medicine2022
Development and Validation of a Deep Neural Network for Accurate Identification of Endoscopic Images From Patients With Ulcerative Colitis and Crohn's Disease.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- 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
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- ChatGPT-Assisted Image Interpretation for Inflammatory Bowel Diseases: Ulcerative Colitis and Crohn's Disease.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Artificial Intelligence in Pediatric Inflammatory Bowel Disease: Applications in Diagnosis, Monitoring, and Therapeutic Decision-Making.Children (Basel, Switzerland) · 2026Review
- Mamba-based state-space modeling with window attention for explainable multi-class gastrointestinal disease diagnosis in endoscopic images.Frontiers in artificial intelligence · 2026Article
- Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images.Bioengineering (Basel, Switzerland) · 2025Article
- Interpretable Artificial Neural Network Models for Predicting Anti-Adalimumab Immune Complex and Serum Drug Level in Crohn's Disease: A Proof-of-Concept Study.Pharmaceutics · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.BMC microbiology · 2025Article
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Paradigm Shift in Inflammatory Bowel Disease Management: Precision Medicine, Artificial Intelligence, and Emerging Therapies.Journal of clinical medicine · 2025Review
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- Asia Pacific association of gastroenterology consensus statements on histopathological evaluation of inflammatory bowel diseases.Therapeutic advances in gastroenterology · 2025Article
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis.Scientific reports · 2024Article
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- Precision Medicine in Inflammatory Bowel Disease: A Spotlight on Emerging Molecular Biomarkers.Biomedicines · 2024Review
- Infliximab monitoring in Crohn's disease: a neural network approach for evaluating disease activity and immunogenicity.Therapeutic advances in gastroenterology · 2024Article
- Profiling the inflammatory bowel diseases using genetics, serum biomarkers, and smoking information.iScience · 2023Article
Corrections and comments
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Authors and funding
29 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aim: The identification of ulcerative colitis (UC) and Crohn's disease (CD) is a key element interfering with therapeutic response, but it is often difficult for less experienced endoscopists to identify UC and CD. Therefore, we aimed to develop and validate a deep learning diagnostic system trained on a large number of colonoscopy images to distinguish UC and CD. Methods: This multicenter, diagnostic study was performed in 5 hospitals in China. Normal individuals and active patients with inflammatory bowel disease (IBD) were enrolled. A dataset of 1,772 participants with 49,154 colonoscopy images was obtained between January 2018 and November 2020. We developed a deep learning model based on a deep convolutional neural network (CNN) in the examination. To generalize the applicability of the deep learning model in clinical practice, we compared the deep model with 10 endoscopists and applied it in 3 hospitals across China. Results: The identification accuracy obtained by the deep model was superior to that of experienced endoscopists per patient (deep model vs. trainee endoscopist, 99.1% vs. 78.0%; deep model vs. competent endoscopist, 99.1% vs. 92.2%, Conclusion: We developed a deep model to assist with the clinical diagnosis of IBD. This provides a diagnostic device for medical education and clinicians to improve the efficiency of diagnosis and treatment.
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