Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs....

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 (FFR_AI) to computational fluid dynamics CT-derived FFR (FFR_CT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFR_AI 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 FFR_CT and FFR measurements in this retrospective proof of concept study. FFR_AI was compared with FFR_CT 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 FFR_AI 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 FFR_CT 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 FFR_AI and FFR_CT (p = 0.12). FFR_AI performed similarly to FFR_CT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFR_AI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.