Abstract
BACKGROUND
Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by measuring the deformability of RBCs from laser diffraction patterns under varying shear stress. In addition to estimations from model comparisons, use of maximum Elongation Index differences (ΔEImax) at different laser intensity levels was recently proposed for the estimation of SC fractions.
OBJECTIVE
Implement a convolutional neural network to accurately estimate rigid-cell fraction and RBC concentration from laser diffraction patterns without using a theoretical model and eliminating the ektacytometer dependency for deformability measurements.
METHODS
RBCs were collected from control patients. Rigid-cell fraction experiments were performed using varying concentrations of glutaraldehyde. Serial dilutions were used for varying the concentration of RBC. A convolutional neural network was constructed using Python and TensorFlow.
RESULTS AND CONCLUSIONS
Measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. The proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.
TY - JOUR
T1 - Applications of deep learning to the assessment of red blood cell deformability.
AU - Turgut,Alper,
AU - Yalçin,Özlem,
PY - 2021/7/6/pubmed
PY - 2021/10/14/medline
PY - 2021/7/5/entrez
KW - Ektacytometry
KW - RBC
KW - deep learning
KW - neural network
SP - 51
EP - 60
JF - Biorheology
JO - Biorheology
VL - 58
IS - 1-2
N2 - BACKGROUND: Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by measuring the deformability of RBCs from laser diffraction patterns under varying shear stress. In addition to estimations from model comparisons, use of maximum Elongation Index differences (ΔEImax) at different laser intensity levels was recently proposed for the estimation of SC fractions. OBJECTIVE: Implement a convolutional neural network to accurately estimate rigid-cell fraction and RBC concentration from laser diffraction patterns without using a theoretical model and eliminating the ektacytometer dependency for deformability measurements. METHODS: RBCs were collected from control patients. Rigid-cell fraction experiments were performed using varying concentrations of glutaraldehyde. Serial dilutions were used for varying the concentration of RBC. A convolutional neural network was constructed using Python and TensorFlow. RESULTS AND CONCLUSIONS: Measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. The proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.
SN - 1878-5034
UR - https://www.unboundmedicine.com/medline/citation/34219708/Applications_of_deep_learning_to_the_assessment_of_red_blood_cell_deformability_
L2 - https://content.iospress.com/openurl?genre=article&id=doi:10.3233/BIR-201016
DB - PRIME
DP - Unbound Medicine
ER -
