Artificial Intelligence Prediction of Cardiovascular Events Using Opportunistic Epicardial Adipose Tissue Assessments From Computed Tomography Calcium Score.

ABSTRACT:

Background

Recent studies have used basic epicardial adipose tissue (EAT) assessments (eg, volume and mean Hounsfield unit [HU]) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACEs).

Objectives

The purpose of this study was to create novel, hand-crafted EAT features, "fat-omics," to capture the pathophysiology of EAT and improve MACE prediction.

Methods

We studied a cohort of 400 patients with low-dose cardiac computed tomography calcium score examinations. We purposefully used a MACE-enriched cohort (56% event rate) for feature engineering purposes. We divided the cohort into training/testing sets (80%/20%). We segmented EAT using a previously validated, deep-learning method with optional manual correction. We extracted 148 initial

SUBMITTER: Hu T 

PROVIDER: S-EPMC11450955 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature