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Articles Published Processes
9/3/2026 2:08:37 AM | Browse: 0 | Download: 0
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Received |
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2026-07-10 08:15 |
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Peer-Review Started |
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2026-07-10 08:15 |
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First Decision by Editorial Office Director |
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Return for Revision |
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2026-07-16 07:14 |
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Revised |
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2026-07-23 11:52 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-07-29 02:38 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-07-29 10:07 |
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Articles in Press |
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2026-07-29 10:07 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-09-01 00:22 |
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Publish the Manuscript Online |
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2026-09-03 02:08 |
| ISSN |
2220-6132 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence for fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ahmed Salman and Mohamed AbdAlla Salman |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Ahmed Salman, FRACP, FRCP, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Rowville 3178, VIC, Egypt. awea844@gmail.com |
| Key Words |
Artificial intelligence; Biliary tract; Cholangiography; Endoscopic retrograde cholangiopancreatography; Fluoroscopy; Patient safety |
| 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. |
| Publish Date |
2026-09-03 02:08 |
| Citation |
Salman A, Salman MA. Artificial intelligence for fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography. World J Transl Med 2026; 12(3): 125559
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| URL |
https://www.wjgnet.com/2220-6132/full/v12/i3/125559.htm |
| DOI |
https://doi.org/10.5528/wjtm.125559 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
