Artificial Intelligence based fractional flow reserve.
Authors
Affiliations (10)
Affiliations (10)
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland.
- Division of Cardiology and Structural Heart Diseases, Medical University of Silesia, Katowice, Poland.
- First Department of Cardiology, Medical University of Gdansk, Gdansk, Poland.
- Department of Cardiology, Andrzej Frycz Modrzewski Krakow University, Bielsko-Biala, Poland.
- American Heart of Poland, Center for Cardiovascular Research and Development, Katowice, Poland.
- Academy of Silesia, Faculty of Medicine, Katowice, Poland.
- Cardiology and Cardiac Surgery Center in Bielsko-Biala, American Heart of Poland, Bielsko-Biała, Poland.
- Third Department of Cardiology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
- Department of Cardiology, National Medical Institute of Internal Affairs and Administration Ministry, Warsaw, Poland.
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland. [email protected].
Abstract
Fractional flow reserve (FFR) - a physiological indicator of coronary stenosis significance - has now become a widely used parameter also in the guidance of percutaneous coronary intervention (PCI). Several studies have shown the superiority of FFR compared to visual assessment, contributing to the reduction in clinical endpoints. However, the current approach to FFR assessment requires coronary instrumentation with a dedicated pressure wire and thus increasing invasiveness, cost, and duration of the procedure. Alternative, noninvasive methods of FFR assessment based on computational fluid dynamics are being widely tested; these approaches are generally not fully automated and may sometimes require substantial computational power. Nowadays, one of the most rapidly expanding fields in medicine is the use of artificial intelligence (AI) in therapy optimization, diagnosis, treatment, and risk stratification. AI usage contributes to the development of more sophisticated methods of imaging analysis and allows for the derivation of clinically important parameters in a faster and more accurate way. Over the recent years, AI utility in deriving FFR in a noninvasive manner has been increasingly reported. In this review, we critically summarize current knowledge in the field of AI-derived FFR based on data from computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound. Available solutions, possible future directions in optimizing cathlab performance, including the use of mixed reality, as well as current limitations standing behind the wide adoption of these techniques, are overviewed.