Development of a Neural Network to Detect Hepatic Steatosis in Metabolic Dysfunction-Associated Steatotic Liver Disease.

ABSTRACT:

Background and aims

Early identification of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical for risk stratification and timely intervention. Conventional noninvasive indices (fatty liver index, hepatic steatosis index, Zhejiang University index, and MASLD index) are limited by linear assumptions and moderate predictive accuracy. We aimed to develop and externally validate a neural network model for noninvasive detection of hepatic steatosis.

Methods

We used a retrospective cohort of 17,465 Japanese health checkup participants (2010-2020). All clinical data were obtained at the Ehime General Health Care Association. The cohort was split into a development cohort (n = 8426) and an internal validation cohort (n = 9039). A feedforward neural network

SUBMITTER: Hirooka M 

PROVIDER: S-EPMC12546783 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature