Abstract

The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease. Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples (out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. Moreover, these videos are of different durations (from 6 to more than 100 frames). We present a two-stage end-to-end machine learning pipeline able to automatically classify cell motions in videos with a high class imbalance. By extending, comparing, and combining two state-of-the-art methods, a convolutional neural network (CNN) model and a recurrent CNN, we are able to automatically discard 97% of the unreliable cell sequences (first stage) and classify highly and poorly deformable red cell sequences with 97% accuracy and an F1-score of 0.94 (second stage). Dataset and codes are publicly released for the community.

MeSH

Cells, Blood, Anemia, Sickle Cell, Classification, Natural Science Disciplines

Cited by

• AI based image analysis of red blood cells in oscillating microchannels. RSC Adv, 2023
• Digital in-line holographic microscopy for label-free identification and tracking of biological cells. Mil Med Res, 2024
• Sickle cell disease classification using deep learning. Heliyon, 2023
• Unbiased Morphometric Assessment of Red Blood Cell Storage Lesion in the Presence of Shear-Induced Stomatocytes. Transfus Med Hemother, 2025
• Mathematical modeling of SCD: a literature review. J Sick Cell Dis, 2025