| 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) 2021. 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 |
Observational Study |
| Article Title |
Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Bing Li, Shi-Lun Cai, Wei-Min Tan, Ji-Chun Li, Ayimukedisi Yalikong, Xiao-Shuang Feng, Hon-Ho Yu, Pin-Xiang Lu, Zhen Feng, Li-Qing Yao, Ping-Hong Zhou, Bo Yan and Yun-Shi Zhong |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Key R&D Program of China |
No. 2018YFC1315000, 2018YFC1315005 |
| National Key R&D Program of China |
No. 2019YFC1315800, 2019YFC1315802 |
| National Natural Science Foundation of China |
No. 81861168036, 81702305 |
| Science and Technology Commission Foundation of Shanghai Municipality |
No. 19411951600, 19411951601 |
| Macao SAR Science and Technology Development Foundation |
No. 0023/2018/AFJ |
| Dawn Program of Shanghai Education Commission |
No. 18SG08 |
|
| Corresponding Author |
Yun-Shi Zhong, MD, PhD, Professor, Department of Endoscopy Center, Zhongshan Hospital of Fudan University, No. 180 Fenglin Road, Shanghai 200032, China. zhongyunshi@yahoo.com |
| Key Words |
Computer-aided detection; Esophageal squamous cell carcinoma; Endoscopy; Screening; Narrow-band imaging; White-light imaging |
| Core Tip |
The computer-assisted diagnosis (CAD) system under conventional endoscopic white-light imaging (WLI) for screening of early esophagus squamous cell carcinoma (ESCC) has high accuracy. However, few studies have examined different characteristics of CAD application in WLI and narrow-band imaging (NBI) models. In this study, the CAD system we constructed under the NBI model for screening of early ESCC have higher accuracy and specificity than the CAD-WLI system. Endoscopists could achieve the best diagnostic efficacy by using both CAD-WLI and CAD-NBI. The two CAD systems have different advantages in avoiding missed diagnosis and excessive biopsy, which could help endoscopists, especially those with less experience, in more efficient screening of early ESCC. |
| Publish Date |
2021-01-14 11:34 |
| Citation |
Li B, Cai SL, Tan WM, Li JC, Yalikong A, Feng XS, Yu HH, Lu PX, Feng Z, Yao LQ, Zhou PH, Yan B, Zhong YS. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J Gastroenterol 2021; 27(3): 281-293 |
| URL |
https://www.wjgnet.com/1007-9327/full/v27/i3/281.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v27.i3.281 |
