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

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dc.contributor.authorFreitas, Cristina
dc.contributor.authorEleutério, João
dc.contributor.authorSoares, Gabriela
dc.contributor.authorEnea, Maria
dc.contributor.authorNunes, Daniela
dc.contributor.authorFortunato, Elvira
dc.contributor.authorMartins, Rodrigo
dc.contributor.authorÁguas, Hugo
dc.contributor.authorPereira, Eulália
dc.contributor.authorVieira, Helena L.A.
dc.contributor.authorFerreira, Lúcio Studer
dc.contributor.authorFranco, Ricardo
dc.contributor.institutionCOPELABS - Cognitive and People-centric Computing
dc.date.accessioned2025-07-28T12:25:01Z
dc.date.available2025-07-28T12:25:01Z
dc.date.issued2025-02-22
dc.descriptionPublisher Copyright: © 2025 by the authors.
dc.description.abstractStroke 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) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-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 development of 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, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. 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.en
dc.description.sponsorshipThis research was funded by Portuguese funds from Fundação para a Ciência e a Tecnologia, I.P., in the scope of projects UIDB/04111/2020 of COPELABS; UIDP/04378/2020, and UIDB/04378/2020 of UCIBIO, UIDB/50006/2020 and UIDP/50006/2020 of LAQV and LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy- i4HB and LA/P/0037/2020, UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication—i3N. M.E. acknowledges LAQV for her Post-Doc grant ref. REQUIMTE 2022-06.
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dc.identifier.citationFreitas, C, Eleutério, J, Soares, G, Enea, M, Nunes, D, Fortunato, E, Martins, R, Águas, H, Pereira, E, Vieira, H L A, Ferreira, L S & Franco, R 2025, 'Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning', Biosensors, vol. 15, no. 3, 136. https://doi.org/10.3390/bios15030136
dc.identifier.doihttps://doi.org/10.3390/bios15030136
dc.identifier.issn2079-6374
dc.identifier.urihttp://hdl.handle.net/10437/15449
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=105001094584&partnerID=8YFLogxK
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofBiosensors
dc.rightsopenAccess
dc.subjectMACHINE LEARNING
dc.subjectPLASMA
dc.subjectSTROKE
dc.subjectHEALTH
dc.subjectHEALTH TECHNOLOGIES
dc.subjectAPRENDIZAGEM COMPUTACIONAL
dc.subjectPLASMA
dc.subjectAVC
dc.subjectSAÚDE
dc.subjectTECNOLOGIAS DA SAÚDE
dc.subjectCOPELABS - Artigos de Revistas Internacionais com Arbitragem Científica
dc.titleTowards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learningen
dc.typearticle

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