Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models

2026-05-19

AI Summary

Traditional FIB-4 index, constrained by its fixed formula, fails to fully exploit the diagnostic information embedded in its input variables, thereby limiting its non-invasive performance in identifying advanced fibrosis in metabolic dysfunction–associated steatotic liver disease (MASLD). This study proposes a shallow deep neural network (s-DNN) built solely upon the original FIB-4 variables—age, AST, ALT, platelet count, and the FIB-4 value itself—requiring only 354 trainable parameters and no additional clinical data. The s-DNN achieves ROC-AUCs of 0.77 and 0.67 and Brier scores of 0.18 and 0.22 in external validation cohorts from Malaysia and India, respectively, outperforming conventional FIB-4 and matching or surpassing both TabPFN and fine-tuned GPT-4o, demonstrating strong generalizability and calibration.

Abstract

Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test, but its fixed formula may underuse diagnostic information contained in age, aspartate aminotransferase, alanine aminotransferase, and platelet count. We evaluated whether machine-learning-enhanced non-invasive testing (MLE-NIT) can improve advanced fibrosis detection while preserving this FIB-4 variable space. We used three biopsy-confirmed MASLD cohorts from China, Malaysia, and India (n=784). The Chinese cohort was split into 486 training and 54 internal validation/tuning patients; final performance was reported only on the Malaysian and Indian external cohorts. Models used five variables: age, FIB-4, aspartate aminotransferase, platelet count, and alanine aminotransferase. We compared FIB-4 with a shallow-deep neural network (s-DNN), TabPFN, and gpt-4o-2024-08-06. FIB-4 achieved external ROC-AUCs of 0.75 and 0.60 in Malaysia and India, respectively. TabPFN achieved 0.69 and 0.66, fine-tuned GPT-4o achieved 0.75 and 0.63, and the s-DNN achieved 0.77 and 0.67, respectively. The s-DNN contained only 354 trainable parameters, compared with 7,244,554 for TabPFN, yet provided a more balanced external operating profile. Calibration showed s-DNN Brier scores of 0.18 and 0.22, and permutation importance identified AST and FIB-4 as dominant variables. Compact non-linear MLE-NITs may enhance FIB-4-based fibrosis assessment without increasing clinical data requirements.

Problem

• MASLD fibrosis
• non-invasive testing
• FIB-4
• machine learning
• advanced fibrosis detection

Innovation

• shallow-deep neural network
• machine-learning-enhanced non-invasive testing
• MASLD fibrosis
• FIB-4 enhancement
• compact ML model