Multiplexed nanophotonic biosensing and deep learning-driven protein quantification for traumatic brain injury...

Traumatic brain injury (TBI) triage and monitoring demand rapid, sensitive, and deployable biomarker assays. We present an AI-integrated nanophotonic biosensor enabling ultrasensitive multiplexed quantification of S100B, GFAP, and UCH-L1 across diverse human biofluids. The sensing substrate consists of polystyrene single chains formed via liquid-confinement self-assembly and functionalized with antibodies. Selenium nanoparticles are pre-incubated with specimens to form SeNP-antigen complexes that co-localize on the chains, generating high-contrast elastic scattering. A standardized four-zone layout employs hydrophilic/hydrophobic patterning to passively isolate reaction domains without physical barriers. A deep learning pipeline facilitates device-agnostic quantification from images captured using various microscopy systems, including professional setups and smartphone-based adaptations. Validation with 195 clinical specimens from 75 individuals (TBI patients and controls) spanning serum, urine, saliva, and cerebrospinal fluid demonstrated a detection limit of 1 pg mL^-1 and strong agreement with ELISA (R² > 0.93) for all biomarkers, with consistent performance across imaging modalities. The workflow is completed in ∼30 min and requires only a smartphone in portable modes, demonstrating a proof-of-concept for point-of-care neurotrauma diagnostics and highlighting a potential pathway toward AI-assisted decentralized TBI management.