Abstract

The flow dynamics of red blood cells in blood capillaries and in microfluidic channels is complex. Cells can obtain different shapes such as discoid, parachute, slipper-like shapes and various intermediate states depending on flow conditions and their viscoelastic properties. We use artificial intelligence based analysis of red blood cells (RBCs) in an oscillating microchannel to distinguish healthy red blood cells from red blood cells treated with formaldehyde to chemically modify their viscoelastic behavior. We used TensorFlow to train and validate a deep learning model and achieved a testing accuracy of over 97%. This method is a first step to a non-invasive, label-free characterization of diseased red blood cells and will be useful for diagnostic purposes in haematology labs. This method provides quantitative data on the number of affected cells based on single cell classification.

MeSH

Cells, Erythrocytes, Blood, Blood Cells, Artificial Intelligence

Cited by

• Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning. Ann Hematol, 2025
• Recent advances in microscale techniques for red blood cells manipulation. Biomicrofluidics, 2025
• Enhancing cell characterization with microfluidics and AI: a comprehensive review of mechanical, electrical, and hybrid techniques. Biotechnol Rep (Amst), 2025