Microparticle-based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning

We report a novel methodology for remote biochemical monitoring using microparticle-based biosensors and optical coherence tomography (OCT). Stimuli-responsive, polymeric microparticles were designed to serve as freely-dispersible biorecognition units, wherein biochemical binding induced volumetric changes of the microparticle. Analytical approaches to detect the sub-micron changes from the OCT signal were devised based on modeling the microparticle as an optical cavity, which enabled estimations far below the resolution of the OCT system. As a proof-of-concept, we demonstrate the 3D spatial tracking of glucose-responsive microparticles biosensors distributed throughout a hydrogel-based tissue-mimic and the temporal monitoring of their response to dynamically-fluctuating levels of glucose. A deep learning-based approach using 3D convolutional neural networks was further implemented to automate the vast processing of the continuous stream of three-dimensional time series data, resulting in a robust end-to-end pipeline with immense potential for continuous in vivo biochemical monitoring.