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

Table 1 Comparative performance and characteristics of non-invasive fibrosis scores in pediatric non-alcoholic fatty liver disease/metabolic dysfunction-associated steatotic liver disease

	Key components	Target population	Reported AUROC for advanced fibrosis (F ≥ 3)	Ref.
CH-MASLD-Fib (machine learning model, hypothetical)	Multiple clinical and laboratory variables (machine learning-derived)	Pediatric (Chinese cohort)	Approximately 0.92 (derivation cohort)	Yao et al[1]
NAFLD fibrosis score	Age, BMI, impaired fasting glucose/diabetes, AST/ALT ratio, platelet count, albumin	Adult (developed in adults)	No diagnostic ability (AUROC: Approximately 0.50; P = 0.14)	Angulo et al[8]
FIB-4 index	Age, AST, ALT, platelet count	Adult (developed in adults)	Poor (AUROC range: Approximately 0.32-0.54)	Shah et al[9]
AST to platelet ratio index	AST level, platelet count	Adult (developed in adults)	Fair for any fibrosis (AUROC range: Approximately 0.70-0.80); poor for advanced fibrosis (AUROC range: Approximately 0.50-0.60)	Chrysanthos et al[10]
Pediatric NAFLD fibrosis score	ALT, alkaline phosphatase, platelet count, GGT	Pediatric (developed in children)	0.74 (95%CI: 0.66-0.82)	Alkhouri et al[7]

AUROC: Area under the receiver operating characteristic curve; CH-MASLD-Fib: Chronic metabolic dysfunction-associated steatotic liver disease with fibrosis; F: Fibrosis; NAFLD: Non-alcoholic fatty liver disease; FIB-4: Fibrosis-4; AST: Aspartate aminotransferase; BMI: Body mass index; ALT: Alanine aminotransferase; GGT: Gamma-glutamyl transpeptidase; CI: Confidence interval.