Deep learning-based GQDs/AgNPs composite enhanced electrochemiluminescence portable biosensor for ultrasensitive point-of-care methimazole monitoring

This study presents an electrochemiluminescence (ECL) sensing platform based on graphene quantum dots/silver nanoparticles (GQDs/AgNPs) composites, combined with a lightweight deep learning model within a portable detection system to achieve highly sensitive point-of-care monitoring of methimazole (MMI). By utilizing the synergistic effect of GQDs interface modification and AgNPs localized surface plasmon resonance (LSPR), the sensor significantly enhances the ECL signal intensity of tris(2,2′-bipyridyl)ruthenium(II) (Ru(bpy) 3 2+ ) on paper-based electrodes, while exhibiting excellent stability. In addition, for the problem of uneven luminescent spot distribution and edge blurring in ECL images, we use an improved U-Net neural network model to achieve accurate extraction of the center bright luminescent spot through sub-pixel level segmentation and background noise suppression. The detection system utilizes a Raspberry Pi core with a 3D printing module to significantly reduce the time and cost per test, and achieves a detection limit of 0.327 nM (linear range: 10 −9 – 10 −3  M). Through the design strategy of “material-algorithm-system”, this study provides a highly sensitive, stable and practical solution for monitoring drug concentrations in the urine of hyperthyroid patients, which promotes the implementation of precision medicine in primary care.