Elisa Pennazio
Development of an innovative Machine Learning algorithm for single-site PWV assessment.
Rel. Danilo Demarchi, Irene Buraioli, Fabio Rossi. Politecnico di Torino, Master of science program in Biomedical Engineering, 2024
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality worldwide, responsible for about 18 million deaths per year. Early detection and treatment of CVD can significantly reduce the risk of premature mortality and support patients in leading healthier lives. Among the several predictive parameters, arterial stiffness represents a valuable indicator of cardiovascular risk, since it describes the rigidity of the arterial wall. This parameter is strongly related to Pulse Wave Velocity (PWV), which refers to the propagation speed of the pressure wave through the circulatory system. Clinically, PWV is determined non-invasively by dividing the distance between two arterial points by the time the pressure wave takes to travel that distance.
The carotid-femoral PWV (cfPWV) is the most widely used clinical method, as it provides a comprehensive assessment of the cardiovascular system's overall condition
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