Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 59, Issue 4, Pages 1155-1161Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2185495
Keywords
Artificial neural network (ANN); coronary disease; parametric finite element analysis; support vector machine (SVM)
Categories
Funding
- Spanish Ministry of Science and Technology [DPI 2010-20746-C03-01]
- Center for International Business Education and Research initiative
- Diputacion General de Aragon [B137/09]
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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.
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