Machine learning techniques as a helpful tool toward determination of plaque vulnerability.

Atherosclerotic cardiovascular disease results in millions of sudden deaths annually, and coronary artery disease accounts for the majority of this toll. Plaque rupture plays main role in the majority of acute coronary syndromes. Rupture has been usually associated with stress concentrations, which are determined mainly by tissue properties and plaque geometry. The aim of this study is develop a tool, using machine learning techniques to assist the clinical professionals on decisions of the vulnerability of the atheroma plaque. In practice, the main drawbacks of 3-D finite element analysis to predict the vulnerability risk are the huge main memories required and the long computation times. Therefore, it is essential to use these methods which are faster and more efficient. This paper discusses two potential applications of computational technologies, artificial neural networks and support vector machines, used to assess the role of maximum principal stress in a coronary vessel with atheroma plaque as a function of the main geometrical features in order to quantify the vulnerability risk.