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Machine Learning Identification Framework of Hemodynamics of Blood Flow in Patient-Specific Coronary Arteries with Abnormality

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Abstract

In this study, we put forth a new deep neural network framework to predict flow behavior in a coronary arterial network with different properties in the presence of any abnormality like stenosis. An artificial neural network (ANN) model is trained using synthetic data so that it can predict the pressure and velocity within the arterial network. The data required to train the neural network were obtained from the CFD analysis of several geometries of arteries with specific features in ABAQUS software. The proposed approach precisely predicts the hemodynamic behavior of the blood flow. The average accuracy of the pressure prediction was 98.7%, and the average velocity magnitude accuracy was 93.2%. Our model can also be used to predict fractional flow reserve (FFR), which is one of the main indices to determine the severity of stenosis, and our model predicts this index successfully based on the artery features.

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Abbreviations

CFD:

Computational fluid dynamics

FEM:

Finite element method

MRI:

Magnetic resonance imaging

FVM:

Finite volume method

WSS:

Wall shear stress

GCRF:

Gaussian conditional random field

MLR:

Multiple regression

MLP:

Multilayer perceptron

MSE:

Mean square error

MAE:

Mean absolute error

AI:

Artificial intelligence

ML:

Machine learning

LV:

Left ventricular

LA:

Left atrial

ML:

Machine learning

CAD:

Coronary artery disease

AAA:

Abdominal aortic aneurysm

ANN:

Artificial neural network

LAD:

Left anterior descending

FFR:

Flow fraction reserve

LDL:

Low-density lipoprotein

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Acknowledgements

This work is supported by Micro+Nanosystems & Applied Biophysics Laboratory, Department of Mechanical Engineering, Babol Noshirvani University of Technology, Department of Genetics, Faculty of Medicine, Babol University of Medical Sciences and by the department of Cancer Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Babol Noshirvani University of Technology, P.O. Box 484, BabolBabol, Iran

    Mohammad Farajtabar & Morsal Momeni Larimi

  2. Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

    Mohit Biglarian

  3. Department of Cancer Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Isar 11, Babol, 47138-18983, Iran

    Davood Sabour & Morteza Miansari

  4. Department of Genetics, Faculty of Medicine, Babol University of Medical Sciences, Ganjafrooz St, Babol, 47176-47745, Iran

    Davood Sabour

  5. Micro+Nanosystems & Applied Biophysics Laboratory, Department of Mechanical Engineering, Babol Noshirvani University of Technology, P.O. Box 484, Babol, Iran

    Morteza Miansari

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Farajtabar, M., Larimi, M.M., Biglarian, M. et al. Machine Learning Identification Framework of Hemodynamics of Blood Flow in Patient-Specific Coronary Arteries with Abnormality. J. of Cardiovasc. Trans. Res. 16, 722–737 (2023). https://doi.org/10.1007/s12265-022-10339-5

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