Surface-enhanced Raman spectroscopy for rapid sepsis recognition and pathogen identification from blood cultures using super operational neural networks

Surface-enhanced Raman spectroscopy for rapid sepsis recognition and pathogen identification from blood cultures using super operational neural networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Surface-enhanced Raman spectroscopy for rapid sepsis recognition and pathogen identification from blood cultures using super operational neural networks Manal Hassan, Md. Sakib Bin Islam, Sakib Mahmud, Mahmoud Elgamal, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7431074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sepsis, a critical medical emergency driven by a dysregulated host response to infection, remains a leading cause of global morbidity and mortality. Current diagnostic methods are slow, blood culture-dependent, and often lack sensitivity or specificity, delaying timely intervention and contributing to poor outcomes. Recent advances in surface-enhanced Raman spectroscopy (SERS) and artificial intelligence (AI) offer promising solutions. Yet, existing machine learning studies have either failed to achieve clinical-grade performance or have not directly targeted rapid sepsis detection from blood cultures. In this study, we collected an extensive set of blood culture samples from a diverse patient cohort attended tertiary level hospital in Qatar, including both clinically confirmed sepsis-positive and control cases, then constructed a large SERS spectral dataset with additional external validation from an independent cohort. We propose SuperRamanNet, a novel deep learning framework based on lightweight, one-dimensional super generative neuron operational neural networks (Super-ONNs), for rapid sepsis recognition and multiclass pathogen identification directly from SERS spectra. The system demonstrates robust performance, achieving 99.67% accuracy for sepsis recognition and 98.84% accuracy for pathogen identification on the primary dataset, with similarly high results on external validation. Comparative analysis confirms that SuperRamanNet consistently outperforms benchmark models and previous literature, supported by ablation studies highlighting the impact of data augmentation and architectural innovations. In conclusion, this work establishes SuperRamanNet as a clinically viable, high-throughput, and portable diagnostic tool, capable of transforming sepsis detection and pathogen identification at the point of care and potentially reducing the global burden of sepsis. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Rapid sepsis recognition Pathogen classification Surface-enhanced Raman spectroscopy Super generative operational ‎neural networks SuperRamanNet Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7431074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511624670,"identity":"31c45c9a-db28-4541-b388-86b26a59785d","order_by":0,"name":"Manal Hassan","email":"","orcid":"","institution":"Qatar University","correspondingAuthor":false,"prefix":"","firstName":"Manal","middleName":"","lastName":"Hassan","suffix":""},{"id":511624671,"identity":"e33da2d8-377d-4388-a63c-d139af15ca36","order_by":1,"name":"Md. 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Current diagnostic methods are slow, blood culture-dependent, and often lack sensitivity or specificity, delaying timely intervention and contributing to poor outcomes. Recent advances in surface-enhanced Raman spectroscopy (SERS) and artificial intelligence (AI) offer promising solutions. Yet, existing machine learning studies have either failed to achieve clinical-grade performance or have not directly targeted rapid sepsis detection from blood cultures. In this study, we collected an extensive set of blood culture samples from a diverse patient cohort attended tertiary level hospital in Qatar, including both clinically confirmed sepsis-positive and control cases, then constructed a large SERS spectral dataset with additional external validation from an independent cohort. 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