| 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) 2024. 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 |
Classification of pediatric video capsule endoscopy images for small bowel abnormalities using deep learning models
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yi-Hsuan Huang, Qian Lin, Xin-Yan Jin, Chih-Yi Chou, Jia-Jie Wei, Jiao Xing, Hong-Mei Guo, Zhi-Feng Liu and Yan Lu |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yan Lu, Associate Research Scientist, PhD, Department of Gastroenterology, Children’s Hospital of Nanjing Medical University, No. 8 Jiangdong South Road, Jianye District, Nanjing 210008, Jiangsu Province, China. luyan_cpu@163.com |
| Key Words |
Deep learning; Video capsule endoscopy; Children; Erosion; Ulcer; Polyp; Convolutional neural network; Vision transformer |
| Core Tip |
This study addresses the challenges clinicians face in manually reviewing video capsule endoscopy (VCE) images, a process that is both time-consuming and labor-intensive. To alleviate this burden, we utilize deep learning models, including DenseNet121, Visual geometry group-16, ResNet50, and vision transformer, to automatically classify small bowel lesions in pediatric VCE images. Our models effectively distinguished between normal tissue, erosions/erythema, ulcers, and polyps with high accuracy. This approach significantly enhances the efficiency and accuracy of diagnosing lesions in pediatric VCE, offering a promising tool for clinical applications. |
| Publish Date |
2025-06-06 03:09 |
| Citation |
<p>Huang YH, Lin Q, Jin XY, Chou CY, Wei JJ, Xing J, Guo HM, Liu ZF, Lu Y. Classification of pediatric video capsule endoscopy images for small bowel abnormalities using deep learning models. <i>World J Gastroenterol</i> 2025; 31(21): 107601</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i21/107601.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i21.107601 |
