AI-derived automated quantification of cardiac chambers and myocardium from non-contrast CT: Prediction of major adverse cardiovascular events in asymptomatic subjects.

ABSTRACT:

Background and aims

The significance of left ventricular mass and chamber volumes from non-contrast computed tomography (CT) for predicting major adverse cardiovascular events (MACE) has not been studied. Our objective was to evaluate the role of artificial intelligence-enabled multi-chamber cardiac volumetry from non-contrast CT for long-term risk stratification in asymptomatic subjects without known coronary artery disease.

Methods

Our study included 2022 asymptomatic individuals (55.6 ± 9.0 years; 59.2 % male) from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial. Multi-chamber cardiac volumetry was performed using deep-learning algorithms from routine non-contrast CT scans for coronary artery calcium scoring. MACE was

SUBMITTER: Razipour A 

PROVIDER: S-EPMC11834152 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature