Ultrasound and SWE-based transfer learning for predicting fibrotic NASH.

ABSTRACT: The aim of this study was to develop a combined deep-learning model utilizing liver ultrasound, liver elastography images, and clinical features to predict and diagnose fibrotic non-alcoholic steatohepatitis (NASH). A rat model of liver steatosis and fibrosis was established through a high-fat diet and subcutaneous CCl₄ injections. Two-dimensional ultrasound and shear wave elastography (SWE) images were acquired. Three deep learning models, based on the ResNet-18 architecture, were designed: (1) a pure image model using only liver ultrasound, (2) a pure image model using only liver elastography, and (3) a combined model incorporating liver ultrasound, liver elastography images, and clinical features. The performance of these models was evaluated using three-fold cross-validation, receiver

SUBMITTER: Xia F 

PROVIDER: S-EPMC12749606 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature