Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens

Abstract

Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients.