Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease in children, affecting up to 38% with obesity of children. With the global shift from non-alcoholic fatty liver disease (NAFLD) to MASLD using affirmative criteria (hepatic steatosis plus ≥ 1 cardiometabolic risk factor) and approximately 99% concordance in pediatrics, the development of non-invasive fibrosis tools is accelerating. Yao et al report a machine-learning "chronic MASLD with fibrosis (CH-MASLD-Fib)" score for advanced fibrosis with area under the receiver operating characteristic curve (AUROC) of 0.92. While timely, we urge caution. First, high accuracy from a single-center cohort signals overfitting: Complex models can learn cohort-specific noise and fail to generalize. Consistent with this, established pediatric scores (NAFLD fibrosis score, fibrosis-4, pediatric NAFLD fibrosis score) perform modestly (AUROC: Approximately 0.6-0.7), and aspartate aminotransferase-to-platelet ratio index is variable, raising concern that CH-MASLD-Fib's result reflects a statistical artifact. Second, MASLD epidemiology varies by ethnicity (highest in Hispanic, lower in Black children); a model derived in a mono-ethnic Chinese cohort may misclassify other populations. Third, clinical utility and cost-effectiveness are unproven; dependence on specialized assays (e.g., bile acids, cholinesterase) would limit access and increase cost. We recommend external validation in multi-ethnic cohorts, head-to-head comparisons with simple serum indices and elastography, and formal economic analyses. Until then, clinical judgment anchored in readily available markers and judicious, targeted liver biopsy remains paramount.

Keywords: Cost-effectiveness; Ethnic diversity; External validation; Health economics; Liver fibrosis; Machine learning; Metabolic dysfunction-associated steatotic liver disease; Non-invasive biomarkers; Overfitting.