Classification of red cell dynamics with convolutional and recurrent neural networks : a sickle cell disease case study Darrin, M. Samudre, A. Sahun, M. Atwell, S. Badens, C. Charrier, A. Helfer, E. Viallat, A. Cohen-Addad, V. /Giffard-Roisin, Sophie 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. 2023 text https://www.documentation.ird.fr/hor/fdi:010087700 fdi:010087700 Darrin M., Samudre A., Sahun M., Atwell S., Badens C., Charrier A., Helfer E., Viallat A., Cohen-Addad V., Giffard-Roisin Sophie. Classification of red cell dynamics with convolutional and recurrent neural networks : a sickle cell disease case study. 2023, 13 (1), [12 ] EN