A Review on the Estimation of Coronary Fractional Flow Reserve Using Artificial Intelligence

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Coronary artery disease (CAD) remains a leading global cause of mortality.
• Invasive coronary angiography (ICA) is the gold standard for CAD diagnosis.
• Fractional flow reserve (FFR) assessment during ICA provides crucial functional stenosis data but requires invasive procedures.

Purpose of the Study:

• To review the current progress of AI-driven FFR estimation techniques.
• To analyze various AI approaches for FFR calculation.
• To highlight the potential of AI in improving CAD diagnosis workflows.

Main Methods:

• Systematic review of studies utilizing AI for FFR estimation.
• Analysis of different AI algorithms and methodologies applied to FFR.
• Evaluation of AI-based FFR accuracy and integration into clinical practice.

Main Results:

• AI-based FFR estimation is emerging as a viable, less invasive alternative.
• Various AI approaches show promise in accurately predicting FFR.
• Integration into physician workflows is a key area of development.

Conclusions:

• AI technology is rapidly advancing FFR estimation for CAD.
• AI offers a path towards faster, more accurate, and less invasive FFR assessment.
• Further research and clinical validation are essential for widespread adoption.