Deep learning-based automated assessment of hepatic fibrosis via magnetic resonance images and nonimage data.

ABSTRACT:

Background

Accurate staging of hepatic fibrosis is critical for prognostication and management among patients with chronic liver disease, and noninvasive, efficient alternatives to biopsy are urgently needed. This study aimed to evaluate the performance of an automated deep learning (DL) algorithm for fibrosis staging and for differentiating patients with hepatic fibrosis from healthy individuals via magnetic resonance (MR) images with and without additional clinical data.

Methods

A total of 500 patients from two medical centers were retrospectively analyzed. DL models were developed based on delayed-phase MR images to predict fibrosis stages. Additional models were constructed by integrating the DL algorithm with nonimaging variables, including serologic biomarkers [aminotr

SUBMITTER: Li W 

PROVIDER: S-EPMC12397629 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature