Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.

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 me

SUBMITTER: Darrin M 

PROVIDER: S-EPMC9839696 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature