Horizon 1 1 17 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES Scientific Reports - Nature [12 ] 2023 fdi:010087700 ENG Scientific Reports - Nature 2045-2322 ISI:000968670400019 1 10.1038/s41598-023-27718-w https://www.documentation.ird.fr/hor/fdi:010087700 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-06/010087700.pdf 13 Horizon (IRD) 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. 050 ; 020 UR219