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Evaluation of Diagnostic Efficacy and Consistency of Deep Learning and CFD Approaches for CT-FFR Across Different Coronary Vessels, Cardiac Phases, and Clinical Settings
Clinical Report: Evaluation of Diagnostic Efficacy of Deep Learning and CFD for CT-FFR
Overview
This study compares the diagnostic performance of deep learning (DL) and computational fluid dynamics (CFD) approaches for CT-derived fractional flow reserve (CT-FFR) against invasive FFR.
Background
Coronary artery disease (CAD) is a leading cause of morbidity and mortality. CT-derived FFR (CT-FFR) offers a non-invasive alternative to invasive FFR, which is the gold standard for identifying ischemia-producing lesions. Understanding the performance of DL and CFD methods for CT-FFR is important for non-invasive functional assessments.
Data Highlights
Method
AUC
Correlation with Invasive FFR
Deep Learning
0.90 (95% CI: 0.88-0.93)
rho = 0.71 (p < 0.001)
Computational Fluid Dynamics
0.89 (95% CI: 0.86-0.92)
rho = 0.68 (p < 0.001)
Key Findings
DL and CFD methods showed high diagnostic performance with AUCs of 0.90 and 0.89, respectively.
Both methods were strongly correlated with invasive FFR (DL: rho = 0.71; CFD: rho = 0.68).
Stable performance was observed across different coronary vessels and cardiac phases.
In gray-zone lesions, DL and CFD had comparable correct classification rates (86.4% vs. 84.6%).
No significant differences in diagnostic performance were found between the two methods (p > 0.05).
Clinical Implications
The findings indicate that both DL-based and CFD-based CT-FFR can be reliably used for non-invasive assessment of hemodynamically significant stenosis. This supports their potential integration into clinical workflows for evaluating coronary artery disease.
Conclusion
DL and CFD approaches for CT-FFR exhibit similar diagnostic performance.
