Copyright
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jul 21, 2025; 31(27): 108200
Published online Jul 21, 2025. doi: 10.3748/wjg.v31.i27.108200
Published online Jul 21, 2025. doi: 10.3748/wjg.v31.i27.108200
Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk
Yuan Tian, Hang-Yi Zhou, Ming-Lin Liu, Zhao-Xian Yan, Juan Du, Department of Chinese Medicine, Changhai Hospital, Naval Medical University, Shanghai 200433, China
Yuan Tian, Hang-Yi Zhou, Ming-Lin Liu, Zhao-Xian Yan, Juan Du, School of Traditional Chinese Medicine, Naval Medical University, Shanghai 200433, China
Yi Ruan, PLA Naval Medical Center, Shanghai 200433, China
Zhao-Xian Yan, School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
Xiao-Hua Hu, Digital Innovation Laboratory, Changhai Hospital, Naval Medical University, Shanghai 200433, China
Co-first authors: Yuan Tian and Hang-Yi Zhou.
Co-corresponding authors: Xiao-Hua Hu and Juan Du.
Author contributions: Hu XH and Du J contributed equally to this study as co-corresponding authors; Hu XH and Du J conceived and planned this study; Tian Y and Zhou HY contributed equally to this study as co-first authors; Tian Y and Zhou HY performed the vast majority of the data acquisition and analysis for this experiment; Liu ML, Ruan Y, and Yan ZX performed the remaining data collection and analysis; Tian Y and Du J wrote the first draft of the manuscript; Hu XH and Du J were responsible for the execution and supervision of the entire project.
Institutional review board statement: The study was reviewed and approved by the Shanghai Changhai Hospital Medical Ethics Committee (Approval No. CHEC2025-129).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: The data are available from the corresponding author upon reasonable request.
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/
Corresponding author: Juan Du, Department of Chinese Medicine, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Yangpu District, Shanghai 200433, China.
Received: April 8, 2025
Revised: May 20, 2025
Accepted: July 1, 2025
Published online: July 21, 2025
Processing time: 105 Days and 1.3 Hours
Revised: May 20, 2025
Accepted: July 1, 2025
Published online: July 21, 2025
Processing time: 105 Days and 1.3 Hours
Core Tip
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.