Predicting coronary stenosis severity from X-ray angiograms

Coronary heart disease is a major burden on health care systems and is responsible for the deaths of more than 7 million people per year worldwide. In the fight against this disease, techniques such as coronary angiography and percutaneous coronary intervention (PCI) have been put forward to allow physicians to identify and treat stenoses (i.e., cholesterol blockages) in the arteries of the heart. Nevertheless, studies have shown the difficulty for interventional cardiologists to reliably assess the severity of coronary stenoses on coronary angiography. Fractional flow reserve (FFR) is the current standard to help physicians assess the true functional severity of coronary stenoses. However, it is not integrated into clinical practice due to considerable medical, financial, and logistical constraints. Stenoa offers artificial intelligence (AI) algorithms and software to identify coronary stenoses and predict their severity in real time, completely avoiding the use of expensive and cumbersome FFR equipment. The development of this technology offers physicians in the field the opportunity to instantly assess the ischemic severity of any stenosis, in real time and at a fraction of the cost.

Intern: 
Tomer Moran
Superviseur universitaire: 
Nicolo Piazza
Province: 
Quebec
Partenaire: 
Partner University: 
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