Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.
Sci Rep
; 13(1): 745, 2023 01 13.
Article
en En
| MEDLINE
| ID: mdl-36639503
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Anemia de Células Falciformes
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2023
Tipo del documento:
Article
País de afiliación:
Francia