The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia.

ABSTRACT:

Objectives

This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold partitioning, in predicting sarcopenia based on computed tomography (CT) and magnetic resonance imaging (MRI) data.

Methods

A retrospective analysis was conducted on a dataset comprising 191 patients diagnosed with sarcopenia and 327 control patients. Participants were randomly assigned to training and validation cohorts in a 6:4 ratio. The paravertebral muscles at the lumbar 3/4 intervertebral disc level were manually delineated as the region of interest (ROI) on non-enhanced CT and axial T2-weighted MRI images. Muscle and adipose tissues were automatically segmented from the ROI u

SUBMITTER: Zuo H 

PROVIDER: S-EPMC12507879 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature