Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia

Abstract

Combining microfuidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen fltering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfuidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfuidic unit is designed to evaluate RBC deformability by maintaining them fxed in planar orientation, allowing the visual inspection of RBC's capacity to restore their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efciency of 91%, but also to distinguish between RHHA subtypes, with an efciency of 82%.