Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	108281
Country	China

Received	2025-04-10 07:00
Peer-Review Started	2025-04-10 07:01
To Make the First Decision
Return for Revision	2025-05-11 05:54
Revised	2025-05-20 12:52
Second Decision	2025-07-21 03:00
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-07-21 07:01
Articles in Press	2025-07-21 07:01
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-09-01 06:39
Publish the Manuscript Online	2025-09-03 06:22

ISSN	2689-7164 (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: https://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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

Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses
Manuscript Source	Invited Manuscript
All Author List	Jiang-Cheng Ding and Jun Zhang
Funding Agency and Grant Number
Corresponding Author	Jun Zhang, Adjunct Associate Professor, Chief Physician, PhD, Department of Digestive, Nanjing First Hospital, No. 68 Changle Road, Qinhuai District, Nanjing 210006, Jiangsu Province, China. zhangjun711028@126.com
Key Words	Machine learning; Artificial intelligence; Endoscopy; Image recognition; Gastroenterology
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.
Publish Date	2025-09-03 06:22
Citation	<p>Ding JC, Zhang J. Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses. <i>Artif Intell Gastrointest Endosc</i> 2025; 6(3): 108281</p>
URL	https://www.wjgnet.com/2689-7164/full/v6/i3/108281.htm
DOI	https://dx.doi.org/10.37126/aige.v6.i3.108281

Full Article (PDF)	AIGE-6-108281-with-cover.pdf
Manuscript File	108281_Auto_Edited_013900.docx
Answering Reviewers	108281-answering-reviewers.pdf
Audio Core Tip	108281-audio.mp3
Conflict-of-Interest Disclosure Form	108281-conflict-of-interest-statement.pdf
Copyright License Agreement	108281-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	108281-non-native-speakers.pdf
Peer-review Report	108281-peer-reviews.pdf
Scientific Misconduct Check	108281-scientific-misconduct-check.png
Scientific Editor Work List	108281-scientific-editor-work-list.pdf
CrossCheck Report	108281-crosscheck-report.pdf