Wang QC, Jiao J, Zhang CQ. Application of artificial intelligence in portal hypertension and esophagogastric varices. World J Gastroenterol 2025; 31(24): 108508 [DOI: 10.3748/wjg.v31.i24.108508]
Corresponding Author of This Article
Chun-Qing Zhang, MD, PhD, Professor, Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Weiqi Road, Jinan 250000, Shandong Province, China. zhangchunqing_sdu@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Jun 28, 2025; 31(24): 108508 Published online Jun 28, 2025. doi: 10.3748/wjg.v31.i24.108508
Application of artificial intelligence in portal hypertension and esophagogastric varices
Qing-Chen Wang, Jian Jiao, Chun-Qing Zhang
Qing-Chen Wang, Jian Jiao, Chun-Qing Zhang, Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250000, Shandong Province, China
Co-first authors: Qing-Chen Wang and Jian Jiao.
Author contributions: Wang QC and Jiao J contributed equally to this work as co-first authors; Wang QC and Jiao J reviewed literature and produced the initial draft; Zhang CQ reviewed and edited the manuscript; All authors have read and approved the final manuscript.
Supported by the National Natural Science Foundation of China, No. 81970533; and the Natural Science Foundation of Shandong Province, No. ZR2022ZD21.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chun-Qing Zhang, MD, PhD, Professor, Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Weiqi Road, Jinan 250000, Shandong Province, China. zhangchunqing_sdu@163.com
Received: April 16, 2025 Revised: May 7, 2025 Accepted: June 9, 2025 Published online: June 28, 2025 Processing time: 71 Days and 18 Hours
Abstract
Esophagogastric variceal bleeding is a common and severe complication of cirrhotic portal hypertension. Hepatic venous pressure gradient measurement and esophagogastroduodenoscopy are the diagnostic gold standards for portal hypertension and esophagogastric variceal bleeding, respectively. With advancements in artificial intelligence in medicine, non-invasive diagnostic methods are increasingly replacing traditional invasive procedures, permitting more rational and personalized patient care. This review summarizes the formation and diagnosis of portal hypertension, as well as the primary prophylaxis, secondary prophylaxis, and management of acute esophagogastric variceal bleeding. This study also highlights the latest progress in artificial intelligence in the diagnosis and treatment of portal hypertension and esophagogastric varices.
Core Tip: This review highlights the latest progress of artificial intelligence (AI) in the diagnosis and treatment of portal hypertension and esophagogastric varices. It emphasizes AI’s potential in early non-invasive diagnosis, risk prediction, and treatment optimization. Key points include the application of machine learning and deep learning in analyzing medical imaging and clinical data to improve diagnostic accuracy and personalized treatment. The review also discusses challenges in AI implementation, such as data quality, model interpretability, and regulatory requirements, and suggests future research directions focusing on enhancing AI’s role in clinical practice.
