  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.
