1. Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment
- Author
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James K Min, Paul Knaapen, Nick S Nurmohamed, Erik S G Stroes, Andrew D Choi, Ibrahim Danad, James Earls, Michiel J Bom, Ruben W de Winter, Rachel Bernardo, Ruurt Jukema, and Ralf Sprengers
- Subjects
Diseases of the circulatory (Cardiovascular) system ,RC666-701 - 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 area under the curve (AUC) analysis, evaluated per-patient and per-vessel and stratified by plaque volume.Results Among 208 patients with a mean age of 58±9 years and 37% women, AI-QCT demonstrated superior concordance with QCA compared with clinical CCTA assessments. For the detection of obstructive stenosis (≥50%), AI-QCT achieved an AUC of 0.91 on a per-patient level, outperforming level 3 (AUC 0.77; p
- Published
- 2025
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