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Articles Published Processes
12/3/2025 9:10:07 AM | Browse: 24 | Download: 107
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Received |
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2025-09-18 03:19 |
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Peer-Review Started |
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2025-09-18 03:22 |
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First Decision by Editorial Office Director |
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2025-09-29 09:11 |
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Return for Revision |
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2025-09-29 09:11 |
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Revised |
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2025-09-29 12:17 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-10-28 02:36 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-10-28 09:31 |
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Articles in Press |
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2025-10-28 09:31 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-11-18 01:57 |
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Publish the Manuscript Online |
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2025-12-03 09:10 |
| 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) 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 |
Letter to the Editor |
| Article Title |
Machine learning to predict metabolic-associated fatty liver disease
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ottavia Cicerone and Marcello Maestri |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Marcello Maestri, MD, PhD, Professor, General Surgery Unit I - Liver Service, Fondazione IRCCS Policlinico San Matteo, P.le Golgi 19, Pavia 27100, Italy. m.maestri@smatteo.pv.it |
| Key Words |
Metabolic-associated fatty liver disease; Hepatic steatosis; Machine learning; Predictive model; Chronic liver disease |
| Core Tip |
Machine learning can enhance early detection of metabolic-associated fatty liver disease by integrating biochemical, clinical, and traditional Chinese medicine features into predictive models. Tian et al provide a promising framework, though external validation and refinement for disease heterogeneity are needed before widespread clinical adoption. |
| Publish Date |
2025-12-03 09:10 |
| Citation |
Cicerone O, Maestri M. Machine learning to predict metabolic-associated fatty liver disease. World J Gastroenterol 2025; 31(45): 114413 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i45/114413.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i45.114413 |
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