Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/11242
Title: Machine Learning Approach for Predicting Wall Shear Distribution for Abdominal Aortic Aneurysm and Carotid Bifurcation Models
Authors: Jordanski M.
Radović M.
Milosevic Z.
Filipovic, Nenad
Obradović Z.
Journal: IEEE Journal of Biomedical and Health Informatics
Issue Date: 1-Mar-2018
Abstract: © 2013 IEEE. Computer simulations based on the finite element method represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper, we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis. In order to capture relationships between geometric parameters, blood density, dynamic viscosity and velocity, and WSS distribution of geometrically parameterized abdominal aortic aneurysm (AAA) and carotid bifurcation models, we proposed multivariate linear regression, multilayer perceptron neural network and Gaussian conditional random fields (GCRF). Results obtained in this paper show that machine learning approaches can successfully predict WSS distribution at different cardiac cycle time points. Even though all proposed methods showed high potential for WSS prediction, GCRF achieved the highest coefficient of determination (0.930-0.948 for AAA model and 0.946-0.954 for carotid bifurcation model) demonstrating benefits of accounting for spatial correlation. The proposed approach can be used as an alternative method for real time calculation of WSS distribution.
URI: https://scidar.kg.ac.rs/handle/123456789/11242
Type: journal article
DOI: 10.1109/JBHI.2016.2639818
ISSN: 21682194
SCOPUS: 85043273920
Appears in Collections:Faculty of Engineering, Kragujevac

Page views(s)

55

Downloads(s)

1

Files in This Item:
File Description SizeFormat 
PaperMissing.pdf
  Restricted Access
29.86 kBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.