Artificial Intelligence-Driven Assessment of Coronary Computed Tomography Angiography for Intermediate Stenosis: Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve

Abstract

We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary computed tomography angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR). This single-center retrospective study included 195 symptomatic patients (mean age 61 ± 10 years, 149 men, 585 coronary arteries) with 215 intermediate coronary lesions, with quantitative coronary angiography (QCA) diameter stenosis ranging from 20% to 80%. An AI-driven research prototype (AI-CCTA) was used to quantify stenosis on CCTA images. The diagnostic accuracy of AI-CCTA was assessed on a per-vessel basis using ICA stenosis grading (with ≥50% stenosis) or invasive FFR (≤0.80) as reference standards. AI-driven diameter stenosis was correlated with the QCA results and expert manual measurements subsequently. The disease prevalence in the 585 coronary arteries, as determined by invasive angiography (≥50%), was 46.5%. AI-CCTA exhibited sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) of 71.7%, 89.8%, 85.9%, 78.5%, and 0.81, respectively. The diagnostic performance of AI-CCTA was moderate for the 215 intermediate lesions assessed using QCA and FFR, with an AUC of 0.63 for QCA and FFR. AI-CCTA demonstrated a moderate correlation with QCA (r = 0.42, p <0.001) for measuring the degree of stenosis, which was notably better than the results from manual quantification versus QCA (r = 0.26, p = 0.001). In conclusion, AI-driven CCTA analysis exhibited promising results. AI-CCTA demonstrated a moderate relation with QCA in intermediate coronary stenosis lesions; however, its results surpassed those of manual evaluations.