Classification of fetal and adult red blood cells based on hydrodynamic deformation and deep video recognition

Peter Johannes Tejlgaard Kampen, Gustav Ragnar Støttrup-Als, Nicklas Bruun-Andersen, Joachim Secher, Freja Høier, Anne Todsen Hansen, Morten Hanefeld Dziegiel, Anders Nymark Christensen, Kirstine Berg-Sørensen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Flow based deformation cytometry has shown potential for cell classification. We demonstrate the principle with an injection moulded microfluidic chip from which we capture videos of adult and fetal red blood cells, as they are being deformed in a microfluidic chip. Using a deep neural network - SlowFast - that takes the temporal behavior into account, we are able to discriminate between the cells with high accuracy. The accuracy was larger for adult blood cells than for fetal blood cells. However, no significant difference was observed between donors of the two types.
Original languageEnglish
Article number5
JournalBiomedical Microdevices
Volume26
Issue number1
Number of pages9
ISSN1387-2176
DOIs
Publication statusPublished - 2024

Keywords

  • Erythrocytes
  • Fetus
  • Microfluidics
  • Microfluidic Analytical Techniques
  • Hydrodynamics
  • Deformation
  • Microfluidic flow cytometry
  • Neural network
  • Red blood cell
  • SlowFast

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