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Author Spotlight: Asymmetric Field Flow Fractionation for Bioreactor Integration
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Artificial Intelligence based fractional flow reserve.

Adrian Bednarek1, Paweł Gąsior2, Miłosz Jaguszewski3

  • 1First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland.

Cardiology Journal
|August 14, 2025
PubMed
Summary

Artificial intelligence (AI) offers a noninvasive approach to deriving fractional flow reserve (FFR), a key indicator for guiding percutaneous coronary intervention (PCI). This AI-driven method shows promise in improving accuracy and efficiency over traditional invasive techniques.

Keywords:
artificial intelligencecomputational fluid dynamicscoronary physiologydeep learningfractional flow reservemachine learning

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Area of Science:

  • Cardiovascular Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Fractional flow reserve (FFR) is crucial for assessing coronary stenosis significance and guiding percutaneous coronary intervention (PCI).
  • Current FFR assessment is invasive, increasing procedure time, cost, and patient risk.
  • Noninvasive methods, including computational fluid dynamics, are being explored but often lack full automation and require significant computational resources.

Purpose of the Study:

  • To review the current state of artificial intelligence (AI) in deriving noninvasive fractional flow reserve (FFR).
  • To summarize AI-based FFR assessment methods using various imaging modalities.
  • To discuss limitations and future directions for AI-derived FFR in clinical practice.

Main Methods:

  • Review of existing literature on AI-derived FFR.
  • Analysis of AI applications utilizing computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound data.
  • Evaluation of current AI solutions and their integration into cathlab workflows.

Main Results:

  • AI is increasingly utilized for noninvasive FFR derivation, offering faster and potentially more accurate assessments.
  • AI-derived FFR leverages data from multiple imaging sources.
  • Emerging AI solutions aim to optimize cathlab performance and patient outcomes.

Conclusions:

  • AI-derived FFR presents a promising noninvasive alternative to traditional methods.
  • Further development is needed to overcome limitations and facilitate widespread adoption.
  • Future applications may include integration with mixed reality for enhanced procedural guidance.