Publication Name	World Journal of Gastroenterology
Manuscript ID	111137
Country	China

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	2025-09-10 00:25
Publish the Manuscript Online	2025-09-19 07:57

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.
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Website	http://www.wjgnet.com

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.
Publish Date	2025-09-19 07:57
Citation	<p>Wang YY, Liu B, Wang JH. Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination. <i>World J Gastroenterol</i> 2025; 31(36): 111137</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i36/111137.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i36.111137

Full Article (PDF)	WJG-31-111137-with-cover.pdf
Manuscript File	111137_Auto_Edited_023458.docx
Answering Reviewers	111137-answering-reviewers.pdf
Audio Core Tip	111137-audio.mp3
Conflict-of-Interest Disclosure Form	111137-conflict-of-interest-statement.pdf
Copyright License Agreement	111137-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	111137-non-native-speakers.pdf
Peer-review Report	111137-peer-reviews.pdf
Scientific Misconduct Check	111137-scientific-misconduct-check.png
Scientific Editor Work List	111137-scientific-editor-work-list.pdf
CrossCheck Report	111137-crosscheck-report.pdf