BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
4/15/2026 6:47:29 AM | Browse: 44 | Download: 141
 |
Received |
|
2025-10-14 10:54 |
|
Peer-Review Started |
|
2025-10-14 10:55 |
|
First Decision by Editorial Office Director |
|
2025-11-14 09:21 |
|
Return for Revision |
|
2025-11-14 09:21 |
|
Revised |
|
2025-11-27 03:35 |
|
Publication Fee Transferred |
|
2025-12-02 14:56 |
|
Second Decision by Editor |
|
2026-02-02 03:01 |
 |
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2026-02-02 09:51 |
|
Articles in Press |
|
2026-02-02 09:51 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2026-04-02 06:16 |
|
Publish the Manuscript Online |
|
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
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| 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
|
| 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 |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Science and Technique Commission of Shanghai Municipality |
21Y11921800 |
| Shanghai Municipal Health Commission |
202540163 |
|
| 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 |
All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.
