Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.
Keywords: Artificial intelligence; Coronary computed tomography angiography; Deep learning; Fractional flow reserve; Intermediate coronary stenoses.
© 2024. The Author(s).