Microparticle-Based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning
| Autor: | Shreyas Shah, Chun-Nam Yu, Michael S. Eggleston, Heejong Kim, Mingde Zheng |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Computer science General Physics and Astronomy 02 engineering and technology 010402 general chemistry 01 natural sciences Convolutional neural network Deep Learning Optical coherence tomography medicine General Materials Science Microparticle medicine.diagnostic_test business.industry Deep learning General Engineering 021001 nanoscience & nanotechnology 0104 chemical sciences Biophotonics Proof of concept Neural Networks Computer sense organs Artificial intelligence 0210 nano-technology business Biosensor Tomography Optical Coherence Biomedical engineering |
| Zdroj: | ACS Nano. 15:9764-9774 |
| ISSN: | 1936-086X 1936-0851 |
| DOI: | 10.1021/acsnano.1c00497 |
| Popis: | Advancing continuous health monitoring beyond vital signs to biochemistry will revolutionize personalized medicine. Herein, we report a biosensing platform to achieve 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 binding with a target biochemical induces volumetric changes of the microparticle. Analytical approaches to detect these submicron changes in 3D using OCT were devised by modeling the microparticle as an optical cavity, enabling estimations far below the resolution of the OCT system. As a proof of concept, we demonstrated the 3D spatiotemporal monitoring of glucose-responsive microparticles distributed throughout a tissue mimic in response to dynamically fluctuating levels of glucose. Deep learning was further implemented using 3D convolutional neural networks 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. |
| Databáze: | OpenAIRE |
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