ArticleFrontiers in medicine2022
Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn's Disease and Ulcerative Colitis.
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 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled 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
- 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
- Gastrointestinal Lesion Detection Using Ensemble Deep Learning Through Global Contextual Information.Bioengineering (Basel, Switzerland) · 2025Article
- An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.United European gastroenterology journal · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- 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 Inflammatory Bowel Disease Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa.Frontiers in pediatrics · 2025Review
- Artificial intelligence in inflammatory bowel disease endoscopy - a review of current evidence and a critical perspective on future challenges.Therapeutic advances in gastroenterology · 2025Review
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- Artificial intelligence in colonoscopy: from detection to diagnosis.The Korean journal of internal medicine · 2024Review
- Advances in Imaging of Inflammation, Fibrosis, and Cancer in the Gastrointestinal Tract.International journal of molecular sciences · 2022Review
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12 authors.
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Abstract
Objective: Evaluation of the endoscopic features of Crohn's disease (CD) and ulcerative colitis (UC) is the key diagnostic approach in distinguishing these two diseases. However, making diagnostic differentiation of endoscopic images requires precise interpretation by experienced clinicians, which remains a challenge to date. Therefore, this study aimed to establish a convolutional neural network (CNN)-based model to facilitate the diagnostic classification among CD, UC, and healthy controls based on colonoscopy images. Methods: A total of 15,330 eligible colonoscopy images from 217 CD patients, 279 UC patients, and 100 healthy subjects recorded in the endoscopic database of Tongji Hospital were retrospectively collected. After selecting the ResNeXt-101 network, it was trained to classify endoscopic images either as CD, UC, or normal. We assessed its performance by comparing the per-image and per-patient parameters of the classification task with that of the six clinicians of different seniority. Results: In per-image analysis, ResNeXt-101 achieved an overall accuracy of 92.04% for the three-category classification task, which was higher than that of the six clinicians (90.67, 78.33, 86.08, 73.66, 58.30, and 86.21%, respectively). ResNeXt-101 also showed higher differential diagnosis accuracy compared with the best performing clinician (CD 92.39 vs. 91.70%; UC 93.35 vs. 92.39%; normal 98.35 vs. 97.26%). In per-patient analysis, the overall accuracy of the CNN model was 90.91%, compared with 93.94, 78.79, 83.33, 59.09, 56.06, and 90.91% of the clinicians, respectively. Conclusion: The ResNeXt-101 model, established in our study, performed superior to most clinicians in classifying the colonoscopy images as CD, UC, or healthy subjects, suggesting its potential applications in clinical settings.
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