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Articles Published Processes
8/21/2026 3:32:18 AM | Browse: 2 | Download: 0
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Received |
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2026-02-11 01:00 |
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Peer-Review Started |
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2026-02-11 01:04 |
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First Decision by Editorial Office Director |
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2026-03-20 10:57 |
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Return for Revision |
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2026-03-20 10:57 |
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Revised |
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2026-03-21 23:15 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-05-27 02:42 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-05-27 07:31 |
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Articles in Press |
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2026-05-27 07:31 |
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Edit the Manuscript by Language Editor |
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2026-05-31 20:40 |
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Typeset the Manuscript |
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2026-08-13 09:38 |
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Publish the Manuscript Online |
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2026-08-21 03:32 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Correspondence |
| Article Title |
Letter to the Editor: Artificial intelligence in hepatology - when deep learning meets drug-induced liver injury
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yasser Fouad, Alaa M Mostafa, Safaa M Abdelhalim and Mohammed Eslam |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yasser Fouad, MD, Professor, Department of Endemic Medicine and Gastroenterology, Faculty of Medicine, Minia University, Main Road, Minia 19111, Egypt. yasserfouad10@yahoo.com |
| Key Words |
Drug-induced liver injury; Pyrrolizidine alkaloid; Sinusoidal obstruction syndrome; Deep learning; Computed tomography model |
| Core Tip |
Drug-induced liver injury remains challenging due to its vague presentation and reliance on exclusion-based techniques. This complexity is particularly evident in cases of pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome, which often require invasive treatments and expert imaging interpretation. Recent advancements in deep learning applied to computed tomography are changing the diagnostic landscape by identifying subtle, diffuse parenchymal abnormalities that might be missed using standard methods. Artificial intelligence can improve clinician performance, enhance diagnostic consistency, and reduce interpretation times, as demonstrated by the validated model discussed here. These integrated technologies could lead to more timely and informed clinical decision-making, improving the early identification of complex drug-induced liver injury characteristics. |
| Publish Date |
2026-08-21 03:32 |
| Citation |
Fouad Y, Mostafa AM, Abdelhalim SM, Eslam M. Letter to the Editor: Artificial intelligence in hepatology - when deep learning meets drug-induced liver injury. World J Gastroenterol 2026; 32(35): 119939
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i35/119939.htm |
| DOI |
https://doi.org/10.3748/wjg.119939 |
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