| 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. |
| 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 |
Retrospective Cohort Study |
| Article Title |
Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Lin Qiu, Jian Ding, Chun-Xiao Lai, Hui Yang, Feng Li, Zhi-Jian Li, Wen Wu, Gui-Ming Liu, Quan-Sheng Guan, Xi-Gang Zhang, Rui-Ya Zhang, Li-Zhi Yi, Zhi-Fang Zhao, Lv Deng, Wei-Jian Lun, Zhen-Yu Wang, Wei-Ming Lu, Wei-Guang Qiao, Su-Ling Wang, Si-Mei Chen, Wen-Qian Shen, Li-Min Cheng, Ben-Gui Zhu, Shun-Hui He, Jie Dai and Yang Bai |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Science and Technology Projects in Guangzhou |
2023A04J2282 |
|
| Corresponding Author |
Yang Bai, Professor, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Avenue North, Guangzhou 510515, Guangdong Province, China. 13925001665@163.com |
| Key Words |
Yamada classification; Endoscopy; Deep learning; Artificial intelligence; Computer-aided diagnosis |
| Core Tip |
This study developed a novel deep learning (YOLOv7-based) computer-aided detection and classification system that significantly outperformed endoscopists in both detecting colorectal polyps (96.7% precision, 95.8% recall) and classifying them morphologically via the Yamada classification (80.2% F1-score). Achieving high image-based accuracy (detection: 99.2%; classification: 97.2%), this computer-aided detection and classification system offers a powerful tool to enhance polyp identification and characterization, particularly benefiting community hospital settings. |
| Publish Date |
2025-10-27 08:08 |
| Citation |
<p>Qiu L, Ding J, Lai CX, Yang H, Li F, Li ZJ, Wu W, Liu GM, Guan QS, Zhang XG, Zhang RY, Yi LZ, Zhao ZF, Deng L, Lun WJ, Wang ZY, Lu WM, Qiao WG, Wang SL, Chen SM, Shen WQ, Cheng LM, Zhu BG, He SH, Dai J, Bai Y. Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance. <i>World J Gastroenterol</i> 2025; 31(40): 111120</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i40/111120.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i40.111120 |
