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9/3/2025 7:36:48 AM | Browse: 1125 | Download: 491
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Received |
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2025-04-10 07:00 |
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Peer-Review Started |
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2025-04-10 07:01 |
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First Decision by Editorial Office Director |
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2025-05-10 18:58 |
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2025-05-11 05:54 |
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Revised |
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2025-05-20 12:52 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-07-21 03:00 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-07-21 07:01 |
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Articles in Press |
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2025-07-21 07:01 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-09-01 06:39 |
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Publish the Manuscript Online |
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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
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| 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
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Jiang-Cheng Ding and Jun Zhang |
| Funding Agency and Grant Number |
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| 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 |
Ding JC, Zhang J. Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses. Artif Intell Gastrointest Endosc 2025; 6(3): 108281 |
| URL |
https://www.wjgnet.com/2689-7164/full/v6/i3/108281.htm |
| DOI |
https://dx.doi.org/10.37126/aige.v6.i3.108281 |
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