Publication Name	World Journal of Gastroenterology
Manuscript ID	111137
Country	China

Category	Computer Science, Artificial Intelligence
Manuscript Type	Review
Article Title	Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination
Manuscript Source	Invited Manuscript
All Author List	Yang-Yang Wang, Bin Liu and Ji-Han Wang
Funding Agency and Grant Number	Funding Agency Grant Number Open Funds for Shaanxi Provincial Key Laboratory of Infection and Immune Diseases 2023-KFMS-1
Corresponding Author	Ji-Han Wang, MD, PhD, Yan’an Medical College, Yan’an University, No. 580 Shengdi Road, Yan’an 716000, Shaanxi, China. jihanwang@yau.edu.cn
Key Words	Gastrointestinal diseases; Endoscopic examination; Deep learning; Convolutional neural networks; Computer-aided diagnosis
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.
Citation	Wang YY, Liu B, Wang JH. Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination. World J Gastroenterol 2025; In press

Received	2025-06-24 08:38
Peer-Review Started	2025-06-24 08:38
To Make the First Decision
Return for Revision	2025-08-01 09:43
Revised	2025-08-07 10:46
Second Decision	2025-08-26 02:38
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-08-26 07:49
Articles in Press	2025-08-26 07:49
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com