On the Feasibility of Machine Learning Algorithms Towards Low-Cost Flow Cytometry

Abstract

Utilization low cost, scalable architectures for detection of specific cells for both mass flow and minute incidence analysis is something that is attractive for the clinical researcher, in order to expand access to otherwise costly devices. We demonstrate the use of a low-cost microfluidics device that performs detection of beads and cells, both for cell counting and for discrete cell type identification. This was accomplished using polymer technology via implementation of polydimethylsiloxane microfluidics, which were created by using a 3-D printed mold, and machine learning technologies with algorithms that can inference and track analyte particles within the microfluidic of interest. Our demonstration of our microfluidics device is proof that creating low cost instruments for analyte detection using current machine learning models and hardware is possible. We foresee the scalability of this design to be immense, in terms of throughput rate, inexpensiveness of product, and multiple different parameters and classes that can be searched for.