Multicenter Evaluation of Interpretable AI for Coronary Artery Disease Diagnosis from PET Biomarkers.

ABSTRACT:

Background

Positron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD).

Methods

From 17,348 patients undergoing cardiac PET/CT across four sites, we retrospectively enrolled 1,664 subjects who had invasive coronary angiography within 180 days and no prior CAD. Deep learning was used to derive coronary artery calcium score (CAC) from CT attenuation correction maps. XGBoost machine learning model was developed using data from one site to detect CAD, defined as left main stenosis ≥50% or ≥70% in other arteries. The model utilized 10 i

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12236934 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature