Ding JC, Zhang J. Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses. Artif Intell Gastrointest Endosc 2025; 6(3): 108281 [DOI: 10.37126/aige.v6.i3.108281]
Corresponding Author of This Article
Jun Zhang, PhD, Adjunct Associate Professor, Chief Physician, Department of Digestive, Nanjing First Hospital, No. 68 Changle Road, Qinhuai District, Nanjing 210006, Jiangsu Province, China. zhangjun711028@126.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/
Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses
Jiang-Cheng Ding, Jun Zhang
Jiang-Cheng Ding, Jun Zhang, Department of Gastroenterology, Nanjing First Hospital, Nanjing 210006, Jiangsu Province, China
Author contributions: Ding JC performed the research; Zhang J designed the research study; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: The author declares no financial or non-financial conflicts of interest related to this work.
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: Jun Zhang, PhD, Adjunct Associate Professor, Chief Physician, Department of Digestive, Nanjing First Hospital, No. 68 Changle Road, Qinhuai District, Nanjing 210006, Jiangsu Province, China. zhangjun711028@126.com
Received: April 10, 2025 Revised: May 20, 2025 Accepted: July 21, 2025 Published online: September 8, 2025 Processing time: 146 Days and 20.6 Hours
Abstract
Clinical gastrointestinal endoscopy has significantly advanced owing to machine learning techniques, which have produced novel instruments and approaches for early-stage disease diagnosis, categorization, and therapy. Machine learning applications in gastrointestinal endoscopy, such as image identification, lesion detection, pathological categorization, and surgical aid, are examined in this minireview. We examine the potential of machine learning to improve treatment regimens, lower misdiagnosis rates, and increase diagnostic accuracy by evaluating previous research. In addition, this study discusses current issues such clinical applicability, model generalization, and data privacy. It also suggests future research directions to help clinicians and researchers in the field of gastrointestinal endoscopy.
Core Tip: This article systematically reviews recent research progress and developmental trends in machine learning applications for gastrointestinal endoscopic imaging. Focusing on tumor and non-tumor lesion analysis, it elaborates on convolutional neural networks' dual mechanisms: Enhancing image clarity through deep feature extraction and reconstruction algorithms, and enabling quantitative image analysis via multi-dimensional feature interpretation. The study further highlights their clinical value in developing artificial intelligence-assisted diagnostic models and achieving precision differential diagnosis in digestive diseases.
