Editorial
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 21, 2025; 31(19): 106628
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.106628
Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up
Vasily Isakov
Vasily Isakov, Department of Gastroenterology and Hepatology, Federal Research Center of Nutrition, Biotechnology and Food Safety, Moscow 115446, Russia
Author contributions: Isakov V wrote and edited the manuscript and reviewed the literature.
Supported by Ministry of Science and Higher Education of the Russian Federation, No. FGMF-2025-0003.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
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: Vasily Isakov, MD, PhD, Professor, Department of Gastroenterology and Hepatology, Federal Research Center of Nutrition, Biotechnology and Food Safety, 21 Kashirskoye Shosse, Moscow 115446, Russia. vasily.isakov@gmail.com
Received: March 4, 2025
Revised: April 6, 2025
Accepted: May 6, 2025
Published online: May 21, 2025
Processing time: 78 Days and 19.3 Hours
Core Tip

Core Tip: The recurrence rates of colorectal polyps after endoscopic mucosal resection remain high. Traditional surveillance strategies rely only on polyp characteristics, potentially missing important risk factors. Machine learning-based models leveraging patient- and polyp-related factors may accurately predict polyp recurrence. Personalized machine-learning-driven risk stratification may optimize surveillance, reduce unnecessary procedures, and improve early cancer detection and cost-effectiveness. Future models should be validated across diverse populations.