Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning

Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) bioconju-gates with human plasma, deposited on a simple aluminum foil substrate and utilizing Sur-face-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the de-velopment of a low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differ-entiating hemorrhagic from ischemic stroke. Bioconjugates were prepared by combining AgNS with plasma, spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemor-rhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized bioconjugate formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 minutes. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay's potential for immediate clinical decision-making.