A 354-parameter shallow-deep neural network using age, AST, ALT, platelets and FIB-4 achieved external ROC-AUCs of 0.77 and 0.67 for advanced MASLD fibrosis, slightly above FIB-4's 0.75 and 0.60 on Malaysian and Indian cohorts.
A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis c.Hepatology, 38(2): 518–526
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Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models
A 354-parameter shallow-deep neural network using age, AST, ALT, platelets and FIB-4 achieved external ROC-AUCs of 0.77 and 0.67 for advanced MASLD fibrosis, slightly above FIB-4's 0.75 and 0.60 on Malaysian and Indian cohorts.