Project overview
Monitoring vital signs using wearable technologies is now commonplace in the comforts of our very homes. However, tapping into the rich in-body biochemistry for continuous monitoring (without drawing blood) has proven to be much more challenging. Although continuous glucose monitoring is well-established and commercially available, expanding such approaches to monitor other types of biochemicals has remained elusive. This is partly due to challenges in developing strategies which can couple the complex binding activity of a target biomarker with a sensing modality to spatiotemporally monitor the binding within tissue over time.
To address this challenge, we developed a novel methodology for remote biochemical sensing using optical coherence tomography (OCT). Unlike conventional in vivo optical methods like fluorescence, which suffer from photobleaching, tissue autofluorescence and low tissue penetration of visible light, OCT can offer significant advantages for spatiotemporally biomonitoring. The deep-penetrating infrared light utilized for OCT enables high-resolution, depth-resolved 3D cross-sectional imaging of tissue in a non-contact and non-invasive manner.
By combining the unique attributes of OCT imaging with biochemical-responsive polymeric microparticles and machine learning, we report one of the first demonstrations of OCT as a robust biosensing modality for continuous biochemical monitoring. Our parallel efforts to miniaturize the OCT system from benchtop to chip-scale further empowers this approach, with broader implications to improve future healthcare monitoring in the clinic, at the home and on-the-go.
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APA style publications
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Shah, S.*; Yu, C. -N.; Zheng, M.; Kim, H.; Eggleston, M.S. “Microparticle-based Biochemical Sensing using Optical Coherence Tomography and Deep Learning.” ACS Nano. (2021). 15: 9764-9774. (PDF)
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Chuang, C.-Y.; Eggleston, M.S.; Shah, S*. "Monitoring Human Blood Flow Dynamics with Quantitative Speckle Variance Optical Coherence Tomography." 2021 IEEE Photonics Conference (IPC), pp. 1-2. (PDF)
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Kim, H.; Yu, C.N.; Kennedy, W.; Eggleston, M.S.; Shah, S.* "Automated Monitoring for Optical Coherence Tomography-based Biosensing Using Deep Learning," 2020 IEEE Photonics Conference (IPC), pp. 1-2, doi: 10.1109/IPC47351.2020.9252523. (PDF)
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Shah, S.*; Zheng, M.; Eggleston, M.S. “Remote Monitoring of Microparticle Biosensors Using Optical Coherence Tomography.” 2020 IEEE Photonics Conference (IPC), pp. 1-2, doi: 10.1109/IPC47351.2020.9252484. (PDF)
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