Salman A, Salman MA. Artificial intelligence for fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography. World J Transl Med 2026; 12(3): 125559 [DOI: 10.5528/wjtm.125559]
Corresponding Author of This Article
Ahmed Salman, FRACP, FRCP, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 11562, Egypt. awea844@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
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review-article
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Salman A, Salman MA. Artificial intelligence for fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography. World J Transl Med 2026; 12(3): 125559 [DOI: 10.5528/wjtm.125559]
World J Transl Med. Sep 28, 2026; 12(3): 125559 Published online Sep 28, 2026. doi: 10.5528/wjtm.125559
Artificial intelligence for fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography
Ahmed Salman, Mohamed AbdAlla Salman
Ahmed Salman, Internal Medicine, Kasr Alainy School of Medicine, Cairo 11562, Egypt
Mohamed AbdAlla Salman, General Surgery, Kasr Alainy School of Medicine, Cairo 11562, Egypt
Author contributions: Salman A conceived the review, conducted the literature search, interpreted the evidence, drafted the manuscript, and critically revised it; Salman MA appraised the literature, contributed surgical and procedural interpretation, revised the manuscript, and approved the final version; both authors accept accountability for the work.
AI contribution statement: OpenAI Codex (GPT-5) was used to assist with language editing, structural organization, and drafting revisions in response to peer-review comments. The authors critically reviewed and revised all artificial-intelligence-assisted text, verified the cited sources and numerical statements, and retain full responsibility for the manuscript’s interpretation, accuracy, originality, integrity, citations, and conclusions. No artificial intelligence tool was used to generate research data or perform clinical decision-making.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Ahmed Salman, FRACP, FRCP, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 11562, Egypt. awea844@gmail.com
Received: July 10, 2026 Revised: July 23, 2026 Accepted: July 29, 2026 Published online: September 28, 2026 Processing time: 55 Days and 11.7 Hours
Abstract
Endoscopic retrograde cholangiopancreatography (ERCP) is a dominant therapeutic approach for biliary and pancreatic ductal diseases, but it remains technically demanding, operator-dependent, and primarily fluoroscopy-guided. The cholangiogram produced during each ERCP is the central image on which intraprocedural decisions rest, but it is interpreted subjectively, with appreciable interobserver variability. Unlike luminal mucosa or cross-sectional imaging, fluoroscopic cholangiography has remained a relatively underexploited area for artificial intelligence (AI). This narrative review outlines the potential applications of AI, particularly deep learning, in interpreting fluoroscopic cholangiograms during ERCP. Reported applications include differentiating malignant from benign biliary strictures, detecting and quantifying common bile duct stones, automated scoring of stone-extraction difficulty, stent-length selection, real-time cannulation and papilla guidance, predicting post-ERCP pancreatitis, and reducing radiation dose. Although early evidence is promising across several of these domains, it is largely based on small, single-center, retrospective datasets, with limited external validation and no regulatory-approved systems. Because erroneous output may immediately influence an invasive decision, the most credible near-term role is assistive: Confidence-aware, physician-in-the-loop support evaluated prospectively for safety, workflow, generalizability, governance, equity, privacy, and patient-important outcomes.
Core Tip: Artificial intelligence may help transform fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography (ERCP) from subjective visual assessment to objective, reproducible decision support. Current applications include biliary stricture characterization, bile duct and stone segmentation, stone-extraction difficulty scoring, stent-length selection, papilla and cannula localization, post-ERCP pancreatitis prediction, and potential radiation-dose reduction. However, most evidence remains retrospective and early-stage. Future progress requires prospective validation, external testing, explainability, workflow integration, data governance, and multimodal physician-in-the-loop systems that combine fluoroscopy with endoscopic, endosonographic, and clinical data.
