Machine learning-based model for predicting metabolic dysfunction-associated steatotic liver disease using non-invasive parameters in young adults.

ABSTRACT:

Background

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly being diagnosed in young adults and is associated with long-term hepatic complications. Early detection remains challenging in asymptomatic individuals, highlighting the need for accurate and non-invasive risk assessment tools.

Methods

We developed and validated a machine learning (ML)-based model to predict MASLD in adults aged 20-40 years. A total of 13,047 participants from the Gangnam Severance Hospital were included in the training set, and 1,335 participants from the Yongin Severance Hospital were included in the external validation set. MASLD was defined as hepatic steatosis on ultrasonography with at least one cardiometabolic risk factor. Three models were constructed using st

SUBMITTER: Song K 

PROVIDER: S-EPMC12747903 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature