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Artif Intell Gastrointest Endosc. Sep 8, 2025; 6(3): 108281
Published online Sep 8, 2025. doi: 10.37126/aige.v6.i3.108281
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
Core Tip

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.