An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning
<|reference_start|>An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning: Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1-mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ~4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for re-alignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective phosphorescence lifetime imager makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers.<|reference_end|>
@article{goncharov2024an,
    title={An insertable glucose sensor using a compact and cost-effective
  phosphorescence lifetime imager and machine learning},
    author={Artem Goncharov, Zoltan Gorocs, Ridhi Pradhan, Brian Ko, Ajmal Ajmal,
  Andres Rodriguez, David Baum, Marcell Veszpremi, Xilin Yang, Maxime Pindrys,
  Tianle Zheng, Oliver Wang, Jessica C. Ramella-Roman, Michael J. McShane,
  Aydogan Ozcan},
    journal={ACS Nano (2024)},
    year={2024},
    doi={10.1021/acsnano.4c06527},
    archivePrefix={arXiv},
    eprint={2406.09442},
    primaryClass={physics.med-ph cs.LG physics.app-ph physics.bio-ph}
}