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Articles Published Processes
11/27/2025 7:53:57 AM | Browse: 9 | Download: 45
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Received |
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2025-06-30 07:33 |
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Peer-Review Started |
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2025-06-30 07:33 |
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First Decision by Editorial Office Director |
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2025-07-11 10:19 |
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Return for Revision |
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2025-07-13 11:33 |
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Revised |
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2025-07-26 11:41 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-10-27 02:41 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-10-27 09:46 |
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Articles in Press |
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2025-10-27 09:46 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-11-17 05:26 |
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Publish the Manuscript Online |
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2025-11-27 07:53 |
| ISSN |
1948-5182 (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) 2025. 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 |
Minireviews |
| Article Title |
Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Machine learning for non-invasive diagnosis and risk stratification
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Mona Abd-Elmonem Hegazy |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Mona Abd-Elmonem Hegazy, Department of Internal Medicine, Kasr Aliny hospital, Faculty of Medicine, Cairo University, kasr Alainy Street, Garden City, Cairo 12556, Egypt. monahegazy@cu.edu.eg |
| Key Words |
Metabolic dysfunction-associated steatotic liver disease; Machine learning; Deep learning; Risk prediction; Disease stratification |
| Core Tip |
Artificial intelligence (AI), machine learning and deep learning, holds transformative potential for the non-invasive diagnosis and risk stratification of metabolic dysfunction-associated steatotic liver disease (MASLD). These AI models utilize readily available clinical data, biomarkers, and imaging modalities (ultrasound, computed tomography, magnetic resonance imaging) to detect steatosis, predict disease risk, and stage fibrosis with greater accuracy than conventional scoring systems such as fibrosis-4. Despite their promising performance, several challenges hinder widespread clinical adoption, including the need for data standardization, rigorous prospective validation, model interpretability, and seamless integration into existing healthcare workflows. Overcoming these barriers is essential to fully harness AI potential in improving MASLD diagnosis and management. |
| Publish Date |
2025-11-27 07:53 |
| Citation |
Hegazy MAE. Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Machine learning for non-invasive diagnosis and risk stratification. World J Hepatol 2025; 17(11): 111354 |
| URL |
https://www.wjgnet.com/1948-5182/full/v17/i11/111354.htm |
| DOI |
https://dx.doi.org/10.4254/wjh.v17.i11.111354 |
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