Deep learning-based coronary calcium score derived from non-gated chest CT and major adverse cardiovascular events in patients with type 2 diabetes mellitus.

ABSTRACT:

Background

Deep learning (DL) models can quantify coronary artery calcification using non-gated chest CT scans. However, the prognostic value of a DL-based coronary artery calcium score (DL-CACS) for predicting major adverse cardiovascular events (MACEs) in patients with type 2 diabetes mellitus (T2DM) remains unclear.

Objectives

This study aimed to evaluate the prognostic value of DL-CACS derived from non-gated chest CT scans in patients with T2DM and to develop a risk stratification model for predicting MACEs.

Methods

Patients with T2DM who underwent non-gated chest CT scans were retrospectively included and followed up for at least 2 years. Patients from Hospital A were randomly assigned to a training cohort and an internal validation cohort in a 3:2 ratio. Two pre

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12509387 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature