Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis.

Background: Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time-consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows.

Methods: In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images.