Computing Sickle Erythrocyte Health Index on quantitative phase imaging and machine learning

Abstract

Sickle cell disease (SCD) is a genetic disorder characterized by abnormal hemoglobin and deformation of red blood cells (RBCs), leading to complications and reduced life expectancy. This study developed an in vitro assessment, the Sickle Erythrocyte Health Index, using quantitative phase imaging (QPI) and machine learning to model the health of RBCs in people with SCD. The health index combines assessment of cell deformation, sickle-shaped classification, and membrane flexibility to evaluate erythrocyte health. Using QPI and image processing, the percentage of sickle-shaped cells and membrane flexibility were quantified. Statistically significant differences were observed between individuals with and without SCD, indicating the impact of underlying pathophysiology on erythrocyte health. Additionally, sodium metabisulfite led to an increase in sickle-shaped cells and a decrease in flexibility in the sickle cell blood samples. Based on these findings, two approaches were used to calculate the Sickle Erythrocyte Health Index: one using hand-crafted features and one using learned features from deep learning models. Both indices showed significant differences between non-SCD and SCD groups and sensitivity to changes induced by sodium metabisulfite. The Sickle Erythrocyte Health Index has important clinical implications for SCD management and could be used by providers when making treatment decisions. Further research is warranted to evaluate the clinical utility and applicability of the Sickle Erythrocyte Health Index in diverse patient populations.