Machine learning to predict metabolic-associated fatty liver disease

Abstract

Metabolic-associated fatty liver disease (MAFLD) represents the most common cause of chronic liver disease worldwide and remains frequently underdiagnosed in its early stages. Tian et al recently reported a prospective observational study that developed a machine learning-based model to predict hepatic steatosis in high-risk individuals. The resulting XGBoost model demonstrated excellent predictive performance (area under the curve 0.82; cross-validation mean area under the curve 0.918). Importantly, the study highlighted clinically meaningful predictors such as the aspartate aminotransferase/alanine aminotransferase ratio, triglycerides, and waist circumference, alongside novel traditional Chinese medicine-derived features like greasy tongue coating and tongue edge redness. Nonetheless, challenges remain, including the need for standardized traditional Chinese medicine assessment, external multicenter validation, and refined modeling to account for MAFLD heterogeneity. Future studies should expand biomarker panels, incorporate advanced imaging, and evaluate clinical outcomes of model-driven interventions. Overall, Tian et al provide a valuable contribution by demonstrating that machine learning can improve early detection and personalized management of MAFLD.

Keywords: 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.