Ultra-early detection of S100B biomarkers using a nanophotonic biosensor with deep learning quantification: A clinical model based on EDAS patients.

Background: Ultra-early detection of brain injury biomarkers within the critical first hour post-injury remains a major clinical challenge in mild traumatic brain injury (mTBI) management. Conventional platforms (e.g., ELISA) suffer from limited sensitivity, prolonged turnaround times, and narrow dynamic range, hindering timely diagnosis.

Methods: We developed an integrated nanophotonic biosensor and deep learning platform for ultrasensitive S100B quantification. The biosensor employs capillary-assembled heterochain arrays of 500-nm polystyrene and 250-nm selenium nanoparticles, enabling red-channel optical amplification. A dedicated deep learning framework (ProSpect), incorporating ResNet-50 backbone and probabilistic patch aggregation, was engineered for automated image analysis. Encephaloduroarteriosynangiosis (EDAS) patients (n = 25) were leveraged as a time-controlled human model of mild cortical injury. Matched biospecimens (serum, urine, saliva, CSF) were collected at preoperative, immediate postoperative (≤15 min), and 1-h postoperative time points for parallel biosensor and ELISA analysis.

Results: The biosensor achieved a detection limit of 1 pg/mL, a dynamic range spanning 1 pg/mL-100 ng/mL (R = 0.9995), and intra-assay CV of 4.21 %. ProSpect-based quantification attained 98.08 % accuracy in semi-quantitative classification and near-perfect ROC-AUC (0.999). Clinically, 47.6 % of samples were undetectable by ELISA (LOD = 18.75 pg/mL) but successfully quantified by the biosensor. Strong inter-method correlation was observed (R = 0.9884). Significant postoperative S100B elevations occurred in serum, while salivary levels decreased and urinary concentrations remained stable.

Conclusion: This biosensor-AI platform enables rapid (<30 min), matrix-flexible, and ultrasensitive S100B detection, overcoming limitations of conventional assays. Validated through the EDAS clinical model, it holds significant potential for point-of-care deployment in mTBI screening, neurotrauma triage, and real-time neurocritical monitoring.