000150303 001__ 150303
000150303 005__ 20250203110924.0
000150303 0248_ $$2sideral$$a76388
000150303 037__ $$aART-2012-76388
000150303 041__ $$aeng
000150303 100__ $$0(orcid)0000-0002-8503-9291$$aCilla, M.$$uUniversidad de Zaragoza
000150303 245__ $$aMachine Learning Techniques as a Helpful Tool toward Determination of Plaque Vulnerability
000150303 260__ $$c2012
000150303 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150303 5203_ $$aAtherosclerotic 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
000150303 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000150303 590__ $$a2.348$$b2012
000150303 591__ $$aENGINEERING, BIOMEDICAL$$b26 / 76 = 0.342$$c2012$$dQ2$$eT2
000150303 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000150303 700__ $$0(orcid)0000-0001-6359-895X$$aMartínez, J
000150303 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, E.$$uUniversidad de Zaragoza
000150303 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, M.A.$$uUniversidad de Zaragoza
000150303 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000150303 773__ $$g59, 4 (2012), 1155-1161$$pIEEE trans. biomed. eng.$$tIEEE Transactions on Biomedical Engineering$$x0018-9294
000150303 8564_ $$s5510167$$uhttps://zaguan.unizar.es/record/150303/files/texto_completo.pdf$$yPostprint
000150303 8564_ $$s3309481$$uhttps://zaguan.unizar.es/record/150303/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000150303 909CO $$ooai:zaguan.unizar.es:150303$$particulos$$pdriver
000150303 951__ $$a2025-02-03-10:49:11
000150303 980__ $$aARTICLE