Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus.

ABSTRACT:

Background

Coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) patients often presents diffuse lesions, with extensive calcification, and it is time-consuming to measure coronary artery calcium score (CACS).

Objectives

To explore the predictive ability of deep learning (DL)-based CACS for obstructive CAD and hemodynamically significant CAD in T2DM.

Methods

469 T2DM patients suspected of CAD who accepted CACS scan and coronary CT angiography between January 2013 and December 2020 were enrolled. Obstructive CAD was defined as diameter stenosis ≥50%. Hemodynamically significant CAD was defined as CT-derived fractional flow reserve ≤0.8. CACS was calculated with a fully automated method based on DL algorithm. Logistic regression was applied to determine the i

SUBMITTER: Hu J 

PROVIDER: S-EPMC10944251 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature