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Assessment of Fractional Flow Reserve from Coronary CT Angiography Using a Deep Learning-Based Algorithm: A
Ludovica R M Lanzafame1, Claudia Gulli1, Maria Teresa Cannizzaro2
1Diagnostic and Interventional Radiology Unit, BIOMORF Department, University Hospital "Policlinico G. Martino", Via Consolare Valeria 1, 98100 Messina, Italy.
Diagnostics (Basel, Switzerland)
|March 14, 2026
Summary
A deep learning algorithm accurately computes non-invasive fractional flow reserve (FFR-CT) from coronary computed tomography angiography (CCTA). This AI tool also reliably assigns cardiovascular risk categories, aiding in ischemia assessment and patient stratification.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Coronary artery disease (CAD) diagnosis often relies on invasive procedures.
- Non-invasive methods for assessing coronary stenosis severity are crucial for improving patient care.
- Deep learning (DL) offers potential for advanced image analysis in cardiovascular imaging.
Purpose of the Study:
- To evaluate the diagnostic accuracy of a DL-based algorithm for non-invasive fractional flow reserve (FFR-CT) calculation.
- To assess the DL model's capability in automatically assigning Coronary Artery Disease-Reporting and Data System (CAD-RADS) risk categories.
- To compare DL-derived FFR-CT and CAD-RADS classifications against invasive coronary angiography (ICA) and expert radiologist assessments.
Main Methods:
- Retrospective analysis of coronary computed tomography angiography (CCTA) data from 60 patients with suspected CAD.
- Application of a DL algorithm to estimate FFR-CT values and assign CAD-RADS categories from CCTA.
- Evaluation of diagnostic performance using ICA as the reference standard, including ROC curve analysis and agreement statistics (Cohen's kappa).
Main Results:
- FFR-CT derived from DL showed high diagnostic accuracy (AUC=0.935, sensitivity=93.2%, specificity=93.7%) for identifying significant coronary stenoses on a per-patient basis.
- The DL model demonstrated excellent agreement with the reference standard (k=0.836) and consistent per-vessel performance.
- Automated CAD-RADS classifications by the DL algorithm showed good agreement with expert radiologist assessments (k=0.765).
Conclusions:
- The DL-based FFR-CT computation is a highly accurate, non-invasive method for assessing myocardial ischemia.
- The algorithm's ability to automatically assign CAD-RADS categories enhances its utility for cardiovascular risk stratification.
- This DL approach shows promise for improving the non-invasive diagnosis and management of coronary artery disease.
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