Improving arterial stiffness prediction with machine learning utilizing hemodynamics and biomechanical features derived from phase contrast magnetic resonance imaging.

Arterial stiffness has emerged as a prominent marker of risk for cardiovascular diseases. Few studies are interested in predicting symptomatic or asymptomatic arterial stiffness from hemodynamics and biomechanics parameters. Machine learning models can be used as an intelligent tool for arterial stiffness detection based on hemodynamic and biomechanical parameters.

A novel non-invasive method for estimating the local wave speed at a single site in the internal carotid artery.

Objectives Local wave speed is a biomarker which provides an objective analysis of the cardiovascular function. The aim of this study was to determine the local wave speed in the internal carotid artery by a new non-invasive method that measures blood velocity waveform at only one site. Methods For this purpose, the cepstral analysis was employed to determine the arrival time of the reflection wave and the wave speed in the carotid artery.