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 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<0.002) and level 2 readers (AUC 0.79; p<0.001 and AUC 0.76; p<0.001). The advantage of AI-QCT was most prominent in those with above median plaque volume. At the per-vessel level, AI-QCT achieved an AUC of 0.86, similar to level 3 (AUC 0.82; p=0.098) stenosis, but superior to level 2 readers (both AUC 0.69; p<0.001).

Conclusions: AI-QCT demonstrated superior agreement with invasive QCA compared to clinical CCTA assessments, particularly compared to level 2 readers in those with extensive CAD. Integrating AI-QCT into routine clinical practice holds promise for improving the accuracy of stenosis quantification through CCTA.