Machine learning to predict metabolic-associated fatty liver disease

doi:10.3748/wjg.v31.i45.114413

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Dec 7, 2025 (publication date) through Dec 6, 2025

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Dec 7, 2025; 31(45): 114413
Published online Dec 7, 2025. doi: 10.3748/wjg.v31.i45.114413

Machine learning to predict metabolic-associated fatty liver disease

Ottavia Cicerone, Marcello Maestri

Ottavia Cicerone, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia 27100, Italy

Marcello Maestri, General Surgery Unit I - Liver Service, Fondazione IRCCS Policlinico San Matteo, Pavia 27100, Italy

Author contributions: Maestri M contributed to the project administration; Cicerone O and Maestri M contributed to the concept and design of the study, the writing of the original draft and the review and editing of the manuscript.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

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: 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

Received: September 18, 2025
Revised: September 29, 2025
Accepted: October 28, 2025
Published online: December 7, 2025
Processing time: 76 Days and 13.2 Hours

Core Tip

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.