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.

[1]  R D Kamm,et al.  On the sensitivity of wall stresses in diseased arteries to variable material properties. , 2003, Journal of biomechanical engineering.

[2]  L. Gibson,et al.  Static circumferential tangential modulus of human atherosclerotic tissue. , 1994, Journal of biomechanics.

[3]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[4]  Omid Omidvar,et al.  Neural Networks and Pattern Recognition , 1997 .

[5]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[6]  E Peña,et al.  3D computational parametric analysis of eccentric atheroma plaque: influence of axial and circumferential residual stresses , 2012, Biomechanics and Modeling in Mechanobiology.

[7]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[8]  Celestino Ordóñez,et al.  Creating a quality map of a slate deposit using support vector machines , 2007 .

[9]  Antonio Colombo,et al.  Association of plaque characterization by intravascular ultrasound virtual histology and arterial remodeling. , 2005, The American journal of cardiology.

[10]  R. Ogden,et al.  Hyperelastic modelling of arterial layers with distributed collagen fibre orientations , 2006, Journal of The Royal Society Interface.

[11]  Patrick Segers,et al.  Identifying the vulnerable plaque: A review of invasive and non-invasive imaging modalities , 2008 .

[12]  R. Kamm,et al.  Distribution of Circumferential Stress in Ruptured and Stable Atherosclerotic Lesions A Structural Analysis With Histopathological Correlation , 1993, Circulation.

[13]  José M. Matías,et al.  Shape functional optimization with restrictions boosted with machine learning techniques , 2010, J. Comput. Appl. Math..

[14]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[15]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[16]  Antheunis Versluis,et al.  Fatigue and plaque rupture in myocardial infarction. , 2006, Journal of biomechanics.

[17]  H Hanke,et al.  The Discovery of the Pathophysiological Aspects of Atherosclerosis — A Review , 2001, Acta chirurgica Belgica.

[18]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[19]  Anil K. Jain,et al.  Neural networks and pattern recognition , 1994 .

[20]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[21]  S. Einav,et al.  Influence of microcalcifications on vulnerable plaque mechanics using FSI modeling. , 2008, Journal of biomechanics.

[22]  R. Virmani,et al.  Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. , 2000, Arteriosclerosis, thrombosis, and vascular biology.

[23]  Jacques Ohayon,et al.  Necrotic core thickness and positive arterial remodeling index: emergent biomechanical factors for evaluating the risk of plaque rupture. , 2008, American journal of physiology. Heart and circulatory physiology.

[24]  Jacques Ohayon,et al.  In-vivo prediction of human coronary plaque rupture location using intravascular ultrasound and the finite element method , 2001, Coronary artery disease.

[25]  Jacques Ohayon,et al.  Biomechanical interaction between cap thickness, lipid core composition and blood pressure in vulnerable coronary plaque: impact on stability or instability , 2004, Coronary artery disease.

[26]  José M. Matías,et al.  Functional classification of ornamental stone using machine learning techniques , 2010, J. Comput. Appl. Math..

[27]  M J Davies,et al.  Stability and instability: two faces of coronary atherosclerosis. The Paul Dudley White Lecture 1995. , 1996, Circulation.

[28]  R Krishna Kumar,et al.  Influence of lumen shape and vessel geometry on plaque stresses: possible role in the increased vulnerability of a remodelled vessel and the “shoulder” of a plaque , 2005, Heart.

[29]  Gerhard Sommer,et al.  Determination of layer-specific mechanical properties of human coronary arteries with nonatherosclerotic intimal thickening and related constitutive modeling. , 2005, American journal of physiology. Heart and circulatory physiology.