Document Detail

Machine Learning Techniques as a helpful tool towards determination of plaque vulnerability.
MedLine Citation:
PMID:  22287230     Owner:  NLM     Status:  Publisher    
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 3D 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. The 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.
M Cilla; J Martinez; E Pena; M Martinez
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-1-24
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  -     ISSN:  1558-2531     ISO Abbreviation:  -     Publication Date:  2012 Jan 
Date Detail:
Created Date:  2012-1-30     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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