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Artificial Intelligence-Driven Fractional Flow Reserve Assessment: Technical Foundations, Clinical Insights, and
Abdelrahman Hafez1, Kamal Awad1, Juan M Farina1
1Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA.
Insights
Artificial intelligence (AI) is advancing coronary lesion assessment beyond invasive fractional flow reserve (FFR). AI-driven methods offer noninvasive evaluation of coronary artery disease (CAD), improving diagnosis and patient care.
Area of Science:
- Cardiology and Medical Imaging
Background:
- Coronary artery disease (CAD) is a major cause of death, necessitating accurate stenosis assessment.
- Invasive fractional flow reserve (FFR) is the gold standard but is invasive and costly.
- CT-derived FFR (FFRct) offered noninvasive assessment but faced computational and imaging challenges.
Purpose of the Study:
- To review the evolution from invasive FFR to AI-driven functional assessment of coronary lesions.
- To examine the principles, validation, and challenges of FFRct and AI-based methods.
- To outline future directions for AI in noninvasive cardiac imaging.
Main Methods:
- Review of computational fluid dynamics (CFD)-based FFRct principles and validation.
- Analysis of machine learning (ML) and deep learning (DL) approaches for coronary lesion assessment.
- Discussion of validation studies on diagnostic accuracy, prognostic value, and clinical utility.
Main Results:
- AI methods, including ML and DL, are emerging as powerful tools for noninvasive functional assessment.
- AI models demonstrate potential in challenging cases like intermediate stenoses and heavy calcification.
- Barriers include data quality dependence, explainability, and clinical workflow integration.
Conclusions:
- AI has the potential to revolutionize noninvasive coronary imaging, integrating anatomical and physiological assessment.
- AI can enable personalized care and improved outcomes through rapid, comprehensive evaluation.
- Future directions include AI-enabled virtual PCI planning and multimodal risk stratification.
Abstract:
Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes.
