Computational Study of Methods for Determining the Elasticity of Red Blood Cells Using Machine Learning

RBC (Red Blood Cell) membrane is a highly elastic structure, and proper modelling of this elasticity is essential for biomedical applications that involve computational experiments with blood flow. In this work, we present a new method for estimating one of the key parameters of red blood cell elast...

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Bibliographic Details
Published in:Symmetry (Basel) 2022-08, Vol.14 (8), p.1732
Main Authors: Molčan, Samuel, Smiešková, Monika, Bachratý, Hynek, Bachratá, Katarína
Format: Article
Language:English
Subjects:
Artificial neural networks
Biomedical materials
Blood
Blood flow
Computer simulation
Data acquisition
elastic object in flow
Elasticity
Erythrocytes
Experiments
Hemodynamics
Laboratories
Machine learning
Malaria
Neural networks
red blood cells elasticity
regression neural networks
sequential data
simulations of blood flow
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Summary:RBC (Red Blood Cell) membrane is a highly elastic structure, and proper modelling of this elasticity is essential for biomedical applications that involve computational experiments with blood flow. In this work, we present a new method for estimating one of the key parameters of red blood cell elasticity, which uses a neural network trained on the simulation outputs. We test classic LSTM (Long-Short Term Memory) architecture for the time series regression task, and we also experiment with novel CNN-LSTM (Convolutional Neural Network) architecture. We paid special attention to investigating the impact of the way the three-dimensional training data are reduced to their two-dimensional projections. Such a comparison is possible thanks to working with simulation outputs that are equivalently defined for all dimensions and their combinations. The obtained results can be used as recommendations for an appropriate way to record real experiments for which the reduced dimension of the acquired data is essential.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14081732