Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.

ABSTRACT:

Background

Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).

Methods

The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1. AI-QCT and blinded readers assessed coronary artery stenosis following the Coronary Artery Disease Reporting and Data System consensus. Accuracy of AI-QCT was compared with a level 3 and two level 2 clinical readers against an invasive quantitative coronary angiography (QCA) reference standard (≥50% stenosis) in an

SUBMITTER: Bernardo R 

PROVIDER: S-EPMC11784206 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature