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Fluorescence Lifetime Macro Imager for Biomedical Applications
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Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Machine Learning.

Artem Goncharov1,2,3, Zoltan Gorocs1,2,3, Ridhi Pradhan4

  • 1Electrical & Computer Engineering Department, University of California, Los Angeles, California 90095, United States.

ACS Nano
|August 13, 2024
PubMed
Summary
This summary is machine-generated.

A new optical continuous glucose monitoring (CGM) system uses a phosphorescence lifetime imager (PLI) to accurately track glucose levels through the skin. This wearable technology demonstrates resilience to misalignment, offering a promising tool for personalized glucose management.

Keywords:
continuous glucose monitoringdeep learninginsertable biosensorsmobile readerneural network-based sensingphosphorescence lifetime

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Area of Science:

  • Biomedical Engineering
  • Optical Sensing
  • Wearable Technology

Background:

  • Optical continuous glucose monitoring (CGM) offers advantages over electrochemical methods, including lower cost and enhanced durability.
  • Existing optical CGM systems face challenges with signal interference and precise sensor alignment.
  • Phosphorescence-based sensing provides a unique approach for glucose detection due to its long signal lifetime.

Purpose of the Study:

  • To develop and evaluate a computational CGM system integrating a phosphorescence-based biosensor and a novel phosphorescence lifetime imager (PLI).
  • To assess the accuracy and robustness of the PLI system for glucose level inference through skin.
  • To investigate the system's resilience to sensor misalignment for practical wearable applications.

Main Methods:

  • Development of a compact, cost-effective PLI designed for capturing phosphorescence lifetime images through skin.
  • Integration of a biocompatible phosphorescence-based biosensor whose signal is modulated by glucose concentration.
  • Utilizing neural network models for misalignment-tolerant inference of glucose levels from acquired lifetime images.
  • In vitro testing using a human skin phantom and glucose-spiked samples to evaluate accuracy and misalignment tolerance.

Main Results:

  • The PLI system achieved 88.8% inference accuracy in a 1 mm thick skin phantom.
  • Demonstrated resilience to random misalignments up to ~4.7 mm, maintaining accurate glucose level inference.
  • Successfully identified larger misalignments (>5 mm), triggering alerts for user realignment.
  • The phosphorescence signal's long lifetime effectively bypassed noise from tissue autofluorescence and excitation leakage.

Conclusions:

  • The developed computational CGM system, featuring a phosphorescence lifetime imager (PLI), provides accurate and misalignment-resilient glucose monitoring.
  • This technology shows significant potential as a cost-effective and durable wearable diagnostic tool for real-time glucose tracking.
  • The system's ability to function through skin and tolerate misalignment addresses key challenges in developing practical optical CGM devices.