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Articles Published Processes
4/15/2026 6:47:29 AM | Browse: 0 | Download: 0
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Received |
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2025-10-14 10:54 |
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Peer-Review Started |
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2025-10-14 10:55 |
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First Decision by Editorial Office Director |
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2025-11-14 09:21 |
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Return for Revision |
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2025-11-14 09:21 |
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Revised |
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2025-11-27 03:35 |
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Publication Fee Transferred |
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2025-12-02 14:56 |
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Second Decision by Editor |
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2026-02-02 03:01 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-02-02 09:51 |
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Articles in Press |
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2026-02-02 09:51 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-04-02 06:16 |
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Publish the Manuscript Online |
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2026-04-15 06:47 |
| 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) 2026. 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 |
Case Control Study |
| Article Title |
Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Shu-Yue Wang, Si-Qi Yin, Jie-Ying Yang, Ming-Yan Ji, Xiao-Qing Zeng, Sheng-Xiang Rao, Min-Zhi Lv, Jie Bao, Man-Ning Wang and Hong Gao |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Science and Technique Commission of Shanghai Municipality |
21Y11921800 |
| Shanghai Municipal Health Commission |
202540163 |
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| Corresponding Author |
Hong Gao, Chief Physician, Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai 200032, China. gao.hong@zs-hospital.sh.cn |
| Key Words |
Deep learning; Diagnostic model; Hepatic sinusoidal obstruction syndrome; Drug induced liver injury; Computed tomography; Pyrrolizidine alkaloids |
| Core Tip |
This study developed the first deep learning model for diagnosing pyrrolizidine-alkaloid induced hepatic sinusoidal obstruction syndrome based on computed tomography images. The model integrates multiscale convolutional modules and an anatomy-based region of interest sampling strategy. Initial validation showed promising diagnostic performance, with potential to improve diagnostic consistency among clinicians and reduce image interpretation time, suggesting its possible utility as a clinical decision-support tool. |
| Publish Date |
2026-04-15 06:47 |
| Citation |
Wang SY, Yin SQ, Yang JY, Ji MY, Zeng XQ, Rao SX, Lv MZ, Bao J, Wang MN, Gao H. Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images. World J Gastroenterol 2026; 32(15): 114778 |
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i15/114778.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v32.i15.114778 |
© 1993-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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