Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models
A recent study evaluates the effectiveness of machine-learning-enhanced non-invasive testing for detecting advanced fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). The study compared traditional FIB-4 testing with various machine learning models, including shallow-deep neural networks. Results indicated that the shallow-deep neural network outperformed FIB-4 and other models in detecting advanced fibrosis while maintaining a lower complexity in its parameters.
- ▪The study involved three biopsy-confirmed MASLD cohorts from China, Malaysia, and India, totaling 784 patients.
- ▪FIB-4 achieved external ROC-AUCs of 0.75 and 0.60 in Malaysia and India, respectively.
- ▪The shallow-deep neural network (s-DNN) achieved external ROC-AUCs of 0.77 and 0.67, outperforming FIB-4 and other models.
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Computer Science > Machine Learning arXiv:2605.20523 (cs) [Submitted on 19 May 2026] Title:Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models Authors:Athanasios Angelakis, Gabriele De Vito, Eleni-Myrto Trifylli, Filomena Ferrucci View a PDF of the paper titled Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models, by Athanasios Angelakis and 3 other authors View PDF HTML (experimental) Abstract:Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD).
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