Diagnostic performance of a coronary CT angiography-based deep learning model for the prediction of vessel-specific...

Fractional flow reserve (FFR) and instantaneous wave-Free Ratio (iFR) pressure measurements during invasive coronary angiography (ICA) are the gold standard for assessing vessel-specific ischemia. Artificial intelligence has emerged to compute FFR based on coronary computed tomography angiography (CCTA) images (CT-FFR_AI). We assessed a CT-FFR_AI deep learning model for the prediction of vessel-specific ischemia compared to invasive FFR/iFR measurements.

We retrospectively selected 322 vessels from 275 patients at two centers who underwent CCTA and invasive FFR and/or iFR measurements during ICA within three months. A junior and senior radiologist at each center supervised vessel centerline-building to generate curvilinear reformats that were processed for CT-FFR_AI binary outcomes (≤ 0.80 or > 0.80) prediction. Reliability for CT-FFR_AI outcomes based on radiologists' supervision was assessed with Cohen's κ. Diagnostic values of CT-FFR_AI were calculated using invasive FFR ≤ 0.80 (n = 224) and invasive iFR ≤ 0.89 (n = 238) as the gold standard. A multinomial logistic regression model, including all false-positive and false-negative cases, assessed the impact of patient- and CCTA-related factors on diagnostic values of CT-FFR_AI.

Concordance for CT-FFR_AI binary outcomes was substantial (κ = 0.725, p < 0.001). Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT-FFR_AI in predicting vessel-specific ischemia on a per-vessel analysis, based on senior radiologists' evaluations, were 85% (58/68) and 91% (78/86), 82% (128/156) and 78% (119/152), 67% (58/86) and 70% (78/111), 93% (128/138) and 94% (119/127), and 83% (186/224) and 83% (197/238), respectively. Coronary calcifications significantly reduced the diagnostic accuracy of CT-FFR_AI (p < 0.001; OR, 1.002; 95% CI 1.001-1.003).

CT-FFR_AI demonstrates high diagnostic performance in predicting vessel-specific coronary ischemia compared to invasive FFR and iFR. Coronary calcifications negatively affect specificity, suggesting that further improvements in spatial resolution could enhance accuracy.

Question How accurately can a new deep learning model (CT-FFR_AI) assess vessel-specific ischemia from CCTA non-invasively compared to two validated pressure measurements during invasive coronary angiography? Findings CT-FFR_AI achieved high diagnostic accuracy in predicting vessel-specific ischemia, with high sensitivity and negative predictive value, independent of scanner type and radiologists' experience. Clinical relevance CT-FFR_AI provides a non-invasive alternative to Fractional Flow Reserve and instantaneous wave-Free Ratio measurements during invasive coronary angiography for detecting vessel-specific ischemia, potentially reducing the need for invasive procedures, lowering healthcare costs, and improving patient safety.