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7/21/2025 1:11:30 PM | Browse: 146 | Download: 454
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Received |
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2025-04-08 13:04 |
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Peer-Review Started |
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2025-04-08 13:05 |
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To Make the First Decision |
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Return for Revision |
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2025-04-16 03:20 |
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Revised |
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2025-05-20 14:47 |
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Second Decision |
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2025-07-01 02:35 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-07-01 04:17 |
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Articles in Press |
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2025-07-01 04:17 |
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Publication Fee Transferred |
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2025-05-27 12:42 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-07-15 00:32 |
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Publish the Manuscript Online |
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2025-07-21 09:42 |
| 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: https://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 |
Observational Study |
| Article Title |
Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yuan Tian, Hang-Yi Zhou, Ming-Lin Liu, Yi Ruan, Zhao-Xian Yan, Xiao-Hua Hu and Juan Du |
| Funding Agency and Grant Number |
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| Corresponding Author |
Juan Du, Department of Chinese Medicine, Changhai Hospital, Naval Medical University, 168 Changhai Road, Yangpu, Shanghai 200433, China. dujuan714@163.com |
| Key Words |
Metabolic-associated fatty liver disease; Machine learning; Prediction model; Hepatic steatosis; High metabolic risk population |
| Core Tip |
We used a prospective cohort to develop and optimize a high-performance machine learning model, demonstrating its potential to screen the hepatic fat deposition in high-risk populations. We also integrate the facial and tongue diagnosis of traditional Chinese medicine (TCM) with the heterogeneity of metabolic-associated fatty liver disease (MAFLD) and introduce TCM-related indicators to increase the diversity of the metrics. Our model targets a more specific population and is applicable to a broader range of scenarios, which lays the foundation for significantly improving MAFLD check-up efficiency and reducing related medical expenses. |
| Publish Date |
2025-07-21 09:42 |
| Citation |
<p>Tian Y, Zhou HY, Liu ML, Ruan Y, Yan ZX, Hu XH, Du J. Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk. <i>World J Gastroenterol</i> 2025; 31(27): 108200</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i27/108200.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i27.108200 |
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