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Articles Published Processes
7/14/2026 7:33:58 AM | Browse: 26 | Download: 121
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Received |
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2026-01-26 07:11 |
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Peer-Review Started |
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2026-01-26 07:12 |
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First Decision by Editorial Office Director |
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2026-02-04 10:10 |
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Return for Revision |
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2026-02-04 10:10 |
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Revised |
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2026-02-15 09:08 |
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Publication Fee Transferred |
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2026-02-25 12:57 |
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Second Decision by Editor |
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2026-03-25 02:36 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-03-25 07:05 |
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Articles in Press |
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2026-03-25 07:05 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-06-28 02:42 |
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Publish the Manuscript Online |
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2026-07-14 07:33 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Clinical Trials Study |
| Article Title |
Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Wen Chen, Meng Wu, Heng-Yu Wang, Hong-Bo Wu, Jie Li, Yu-Hao Sun, Fang Huang, Min Gao, Zhi-Hang Zhong, Yan-Min Wu and Lei Chen |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Chongqing Science and Health Joint Medical Research Project |
No. 2023ZDXM007 |
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| Corresponding Author |
Lei Chen, Full Professor, MD, PhD, Department of Gastroenterology, Southwest Hospital of Army Medical University, No. 30 Gaotanyan, Shapingba District, Chongqing 400038, China. xhl13228683896@tmmu.edu.cn |
| Key Words |
Colonoscopy; Artificial intelligence; Mucosa touch rate; Polyp detection rate; Adenoma detection rate |
| Core Tip |
This study introduces an artificial intelligence-based mucosa touch rate (MTR) as a novel real-time quality indicator for colonoscopy. Using a deep learning model, MTR objectively quantifies mucosal contact during withdrawal. The results demonstrate a strong negative correlation between MTR and polyp detection rate (PDR). Prospective validation shows that real-time MTR feedback significantly improves PDR, particularly among less experienced endoscopists, highlighting its potential for real-time skill assessment and standardized training in colonoscopy quality control. |
| Publish Date |
2026-07-14 07:33 |
| Citation |
Chen W, Wu M, Wang HY, Wu HB, Li J, Sun YH, Huang F, Gao M, Zhong ZH, Wu YM, Chen L. Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy. World J Gastroenterol 2026; 32(27): 119276
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i27/119276.htm |
| DOI |
https://doi.org/10.3748/wjg.119276 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
