Applications of machine learning for simulations of red blood cells in microfluidic devices.

Journal: BMC bioinformatics
Published Date: Mar 11, 2020

Abstract

BACKGROUND: For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the movement and behavior of the dominant component of the blood, the red blood cells. This can be done quite precisely in small channels and within a short timeframe. However, larger volumes or timescales require different approaches. Instead of simplifying the simulation, we use a neural network to predict the movement of the red blood cells.

Authors

  • Hynek Bachratý
    Department of Software Technologies, Faculty of Management Science and Informatics, University of žilina, Cell-in-fluid Research Group, žilina, Slovakia.
  • Katarína Bachratá
    Department of Software Technologies, Faculty of Management Science and Informatics, University of žilina, Cell-in-fluid Research Group, žilina, Slovakia.
  • Michal Chovanec
    Department of Technical Cybernetics, Faculty of Management Science and Informatics, University of žilina, žilina, Slovakia.
  • Iveta Jančigová
    Department of Software Technologies, Faculty of Management Science and Informatics, University of žilina, Cell-in-fluid Research Group, žilina, Slovakia.
  • Monika Smiešková
    Department of Software Technologies, Faculty of Management Science and Informatics, University of žilina, Cell-in-fluid Research Group, žilina, Slovakia.
  • Kristína Kovalčíková
    Department of Software Technologies, Faculty of Management Science and Informatics, University of žilina, Cell-in-fluid Research Group, žilina, Slovakia.

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