ArticleScientific reports2021
Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-Based Prediction of Post-ERCP Pancreatitis: A Comparative Study Using Tabular, Image, and Multimodal Data.Diagnostics (Basel, Switzerland) · 2026Article
- AI-powered hierarchical classification of ampullary neoplasms: a deep learning approach using white-light and narrow-band imaging.Surgical endoscopy · 2026Article
- Transforming endoscopic retrograde cholangiopancreatography: the role of artificial intelligence in pre-operative planning, intraoperative navigation, and post-operative risk prediction.Frontiers in medicine · 2026Review
- Development of an artificial intelligence model to identify duodenal polyps in patients with familial adenomatous polyposis.iGIE : innovation, investigation and insights · 2025Article
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Evolving Strategies for the Optimal Management of Common Bile Duct Stones.Gut and liver · 2025Review
- Strategies for achieving successful cannulation in endoscopic retrograde cholangiopancreatography: A technical overview.World journal of gastrointestinal endoscopy · 2025Review
- Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease.Cancers · 2025Review
- Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.NPJ digital medicine · 2024Article
- Surgical strategies for challenging common bile duct stones in the endoscopic era: A comprehensive review of current evidence.World journal of gastrointestinal endoscopy · 2024Review
- Review
- Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology.Computational intelligence and neuroscience · 2022Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advancement of artificial intelligence (AI) has facilitated its application in medical fields. However, there has been little research for AI-assisted endoscopy, despite the clinical significance of the efficiency and safety of cannulation in the endoscopic retrograde cholangiopancreatography (ERCP). In this study, we aim to assist endoscopists performing ERCP through automatic detection of the ampulla and the identification of cannulation difficulty. We developed a novel AI-assisted system based on convolutional neural networks that predict the location of the ampulla and the difficulty of cannulation to the ampulla. ERCP data of 531 and 451 patients were utilized in the evaluation of our model for each task. Our model detected the ampulla with mean intersection-over-union 64.1%, precision 76.2%, recall 78.4%, and centroid distance 0.021. In classifying the cannulation difficulty, it achieved the recall of 71.9% for the class of easy cases and that of 61.1% for that of difficult cases. Remarkably, our model accurately detected AOV with varying morphological shape, size, and texture on par with the level of a human expert and showed promising results for recognizing cannulation difficulty. It demonstrated its potential to improve the quality of ERCP by assisting endoscopists.
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