BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
12/3/2025 9:10:07 AM | Browse: 100 | Download: 319
 |
Received |
|
2025-09-18 03:19 |
|
Peer-Review Started |
|
2025-09-18 03:22 |
|
First Decision by Editorial Office Director |
|
2025-09-29 09:11 |
|
Return for Revision |
|
2025-09-29 09:11 |
|
Revised |
|
2025-09-29 12:17 |
 |
Publication Fee Transferred |
|
|
|
Second Decision by Editor |
|
2025-10-28 02:36 |
|
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2025-10-28 09:31 |
|
Articles in Press |
|
2025-10-28 09:31 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2025-11-18 01:57 |
|
Publish the Manuscript Online |
|
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
|
| 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 |
Letter to the Editor |
| Article Title |
Machine learning to predict metabolic-associated fatty liver disease
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ottavia Cicerone and Marcello Maestri |
| ORCID |
|
| Funding Agency and Grant Number |
|
| 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 |
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.
