Artificial Intelligence-Driven Fractional Flow Reserve Assessment: Technical Foundations, Clinical Insights, and...

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.