- Academic Editors
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†These authors contributed equally.
Background: The noninvasive computed tomography angiography–derived
fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With
advancements in associated software, the diagnostic capability of CT-FFR may have
evolved. This study evaluates the effectiveness of a novel deep learning-based
software in predicting coronary ischemia through CT-FFR. Methods: In
this prospective study, 138 subjects with suspected or confirmed coronary artery
disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT)
angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR)
measurement. The diagnostic performance of the CT-FFR was determined using the
FFR as the reference standard. Results: With a threshold of
0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity,
specificity, area under the receiver operating characteristic curve (AUC),
positive predictive value (PPV), and negative predictive value (NPV) of 97.1%,
96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the
CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV
of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The
Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR
(p