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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 28, 2025; 31(36): 111137
Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.111137
Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.111137
Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination
Yang-Yang Wang, School of Physics and Electronic Information, Yan’an University, Yan’an 716000, Shaanxi Province, China
Bin Liu, Department of Pharmacy, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei Province, China
Ji-Han Wang, Yan’an Medical College, Yan’an University, Yan’an 716000, Shaanxi Province, China
Co-first authors: Yang-Yang Wang and Bin Liu.
Author contributions: Wang YY and Liu B made equal contributions as co-first authors; Wang YY and Wang JH conceptualized and designed the study, and constructed figures presented in this manuscript; Wang YY searched and reviewed published articles, and wrote the original manuscript; Liu B made revisions to the revised manuscript; Wang JH reviewed the original manuscript, and provided the funding acquisition; all authors approved the submitted version.
Supported by Open Funds for Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, No. 2023-KFMS-1.
Conflict-of-interest statement: All the authors report 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: Ji-Han Wang, MD, PhD, Yan’an Medical College, Yan’an University, No. 580 Shengdi Road, Yan’an 716000, Shaanxi Province, China. jihanwang@yau.edu.cn
Received: June 24, 2025
Revised: August 7, 2025
Accepted: August 26, 2025
Published online: September 28, 2025
Processing time: 87 Days and 16 Hours
Revised: August 7, 2025
Accepted: August 26, 2025
Published online: September 28, 2025
Processing time: 87 Days and 16 Hours
Core Tip
Core Tip: This review summarizes the latest advances in the application of deep learning-based convolutional neural networks in gastrointestinal endoscopy. It highlights convolutional neural networks’ roles in lesion detection, classification, segmentation, and real-time decision support, emphasizing their potential to enhance diagnostic accuracy, reduce variability, and integrate into clinical workflows for improved patient outcomes.