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Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis
- Source :
- Reviews in Cardiovascular Medicine, Vol 25, Iss 1, p 20 (2024)
- Publication Year :
- 2024
- Publisher :
- IMR Press, 2024.
-
Abstract
- 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 < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.
Details
- Language :
- English
- ISSN :
- 15306550
- Volume :
- 25
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Reviews in Cardiovascular Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b257bc87945d49b0aebe80a2dd1a5943
- Document Type :
- article
- Full Text :
- https://doi.org/10.31083/j.rcm2501020