Intelligent image-based deformability assessment of red blood cells via dynamic shape classification

Red blood cells (RBC), the primary component of whole blood, play an essential role in maintaining blood fluidity and mass transport throughout the human body. RBC deformability is an important property that indicates RBC quality and diseases. Among the techniques for RBC deformability measurements, the hydrodynamic image-based approach represents the most advanced method. However, RBC have a complex shape and high deformability, they display a wide range of shapes during the transition in microvessels, making it a challenge for deformation measurement. Therefore, we propose an image-based RBC deformability assessment via a shape-classification approach (IRIS) to solve this issue, where six shapes are identified by deep learning technique. Then, the deformations of RBC stiffened by different glutaraldehyde (GA) concentrations are measured and compared. The results elucidate that IRIS is able to sensitively distinguish 0.001% GA treated RBC from the control. Moreover, it is superior to two-shape classification or zero-shape classification in terms of sensitivity for deformability quantification. IRIS demonstrates an improved sensitivity for deformability analysis, high automaticity and unprecedented large data volume by applying deep learning method, it has a vast potential to benefit the hemorheology field as a powerful tool for early detection and diagnosis of RBC related diseases.