Deep Learning-Enhanced Lateral-Flow Microarray Biosensor for Multiplexed Inflammatory Biomarker Analysis

This is the authors' abstract. We don't add key points for this paper.

Beyond simply functional integration, achieving synergistic enhancements in multiplex detection, quantitative robustness, and intelligent analysis remains a key challenge for next-generation biosensing platforms in precision diagnostics. Lateral flow biosensors are attractive paper-based platforms because of their simplicity, low cost, and rapid operation; however, conventional line-type formats and visual readouts may limit their multiplex quantitative capability. Here, we present a surface-enhanced Raman scattering-based lateral flow microarray chip (LF-SERS-MAC) platform that integrates spatially encoded microarray patterns with AI-assisted analysis to enable sensitive, specific, and stable detection of three inflammatory proteins on a single paper device. The platform employs AuNPDTNB@antibody nanoprobes that simultaneously provide strong Raman signals and specific recognition of target proteins. The 4 × 4 microarray architecture enables spatially separated detection channels and predefined replicate microdots, supporting standardized SERS sampling and replicate-based signal averaging. More importantly, a 2D transformation-based deep learning model was developed for inflammation classification and concentration prediction, which demonstrates higher accuracy than conventional AI models. This platform can be further scaled to high-throughput paper chips, facilitating its clinical translation as a next-generation multiplexed intelligent biosensing strategy.