Real-Time Detection and Classification of Motile Microbes Using Machine Learning and Holographic Microscopy

Abstract

The tracks of motile pathogens can be used as a phenotypic `footprint' that can be used to predict the species of the pathogen. This has potential uses across many industries including healthcare, environment and manufacturing. Digital holographic microscopy (DHM) is a unique, high-throughput tool for obtaining the 3D time-series tracks of microbes for study and analysis. Here I show that the process of analysis DHM can be sped up to real-time speeds so that tracks can be obtained rapidly, and that these tracks can be used to predict the species using machine learning techniques. I also discuss how this work could be applied to the industries mentioned above