| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2023. 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 Study |
| Article Title |
An artificial intelligence system for the detection of Barrett’s esophagus
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ming-Chang Tsai, Hsu-Heng Yen, Hui-Yu Tsai, Yu-Kai Huang, Yu-Sin Luo, Edy Kornelius, Wen-Wei Sung, Chun-Che Lin, Ming-Hseng Tseng and Chi-Chih Wang |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Chi-Chih Wang, MD, PhD, Associate Professor, Director, Director, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, No.110 Sec. 1, Jianguo N. Rd., South Dist., Taichung 402, Taiwan. bananaudwang@gmail.com |
| Key Words |
Barrett’s esophagus; Artificial intelligence system; Endoscopy; Narrow-band imaging; Gastroesophageal reflux disease |
| Core Tip |
The prevalence of Barrett’s esophagus (BE) diagnosed by endoscopy significantly differs from BE diagnosed by histology (7.8% vs. 1.3%). Current research showed that image-enhanced endoscopy can only increase the detection ability for dysplasia lesions in BE. Our artificial intelligence prediction system, which was trained by endoscopic BE images with the Olympus narrow-band imaging system, still provided good prediction results for images of histological BE. The accuracy, sensitivity, and specificity are 94.37%, 94.29%, and 94.44%, respectively, in the final test, which indicates that endoscopic BE images have characteristics similar to images of histological BE. |
| Publish Date |
2023-12-27 05:42 |
| Citation |
Tsai MC, Yen HH, Tsai HY, Huang YK, Luo YS, Kornelius E, Sung WW, Lin CC, Tseng MH, Wang CC. An artificial intelligence system for the detection of Barrett’s esophagus. World J Gastroenterol 2023; 29(48): 6198-6207 |
| URL |
https://www.wjgnet.com/1007-9327/full/v29/i48/6198.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v29.i48.6198 |
