Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

ABSTRACT:

Background

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality.

Methods

The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdo

SUBMITTER: Cho SW 

PROVIDER: S-EPMC11294037 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature