Non-Invasive Screening of Alzheimer’s Disease via Label-Free Multispectral Retinal Imaging

Non-Invasive Screening of Alzheimer’s Disease via Label-Free Multispectral Retinal Imaging | 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 Non-Invasive Screening of Alzheimer’s Disease via Label-Free Multispectral Retinal Imaging Zita Salajkova, Gabriele Ciasca, Francesco Di Lorenzo, Riccardo Reale, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6172703/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 Alzheimer’s disease (AD) is the most prevalent form of dementia, yet its early detection remains challenging due to the invasiveness, cost, and limited accessibility of current diagnostics. Increasing evidence suggests that retinal changes mirror cerebral pathology in AD, making the eye a promising site for non-invasive biomarker discovery. Here, we present a technique employing a custom-built multispectral imaging module, designed to be integrated with existing fundus imaging systems, that captures retinal reflectance across three optimized spectral bands to quantify spectral alterations linked to AD. We validate the system in a case-control study of 38 mild AD patients and 28 age-matched controls, revealing spatially resolved differences in a fundus map derived from the blue-to-green ratiometric channel (p < 0.001). Our analysis identifies specifically the fovea-to-optic disc region as the most discriminative for AD, with an AUC of 0.74. Building on this, we developed a biologically informed machine-learning classification model incorporating spectral, clinical, and demographic data. On an independent validation test, the model achieved an AUC of 0.91, matching or slightly outperforming the most advanced spectral retinal measurements, yet using a simpler, more stable, and cost-effective setup that further facilitates clinical translation. The demonstrated technology, thanks to its non-invasiveness and its integrability with both existing medical technologies and advanced quantitative statistical methods, holds the potential to drive a significant leap forward in the early detection of AD, opening a window for timely intervention and thus profoundly impacting patient care. Health sciences/Biomarkers Physical sciences/Engineering/Biomedical engineering Alzheimer’s disease Multispectral retinal imaging Retinal biomarkers Fundus photography Non-invasive diagnostics Optical biosensors Dementia screening Machine learning XGBoost SHAP analysis Full Text Additional Declarations There is NO Competing Interest. 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-6172703","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":428491522,"identity":"16f11d81-b7c3-46ed-8dff-2f6595ca0973","order_by":0,"name":"Zita Salajkova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PPYoCMRTA8RcepHo4bQQxV1AEsRC9isPAnkFQcEQYu93W40QCsZHdVthmwQuMzWIxiHFGLZMpBfMv8pr88gEQCr1iWI0WgLnNIXG7KjepDN3JB3FrlMc8yO140NV0EbmOtjkUI4qWJj7S/KfVkCnTuYOwFaJgWUJCGd0j8+t/GNq/CJYigdplzQ2vQbg1ZygWJEty+fYTQuQCuKaOMkacMuUnArE/iLMddZVJOqfPxJI4VXsHkV/b4yEvZu32wXT/Jv+jsVxpnU8dpGxS3vd8DEt94F5Ud2MoFAq9XVfqzUcwb1glDgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5085-9698","institution":"Istituto Italiano di Tecnologia","correspondingAuthor":true,"prefix":"","firstName":"Zita","middleName":"","lastName":"Salajkova","suffix":""},{"id":428491523,"identity":"29b06951-e49d-40a4-92b4-e2b4cd7fd6a5","order_by":1,"name":"Gabriele Ciasca","email":"","orcid":"","institution":"Catholic University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Gabriele","middleName":"","lastName":"Ciasca","suffix":""},{"id":428491524,"identity":"6307b68f-dfb5-47b2-b5f4-6272d5734786","order_by":2,"name":"Francesco Di Lorenzo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"Di","lastName":"Lorenzo","suffix":""},{"id":428491525,"identity":"6b52bb0b-737e-4147-9b89-aaf8e545c796","order_by":3,"name":"Riccardo Reale","email":"","orcid":"","institution":"University of Rome Tor Vergata","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Reale","suffix":""},{"id":428491526,"identity":"12a89ef9-e389-4a3e-9cab-c2dadf0bf774","order_by":4,"name":"Vincenzo Ricco","email":"","orcid":"","institution":"D-Tails s.r.l. 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Increasing evidence suggests that retinal changes mirror cerebral pathology in AD, making the eye a promising site for non-invasive biomarker discovery. Here, we present a technique employing a custom-built multispectral imaging module, designed to be integrated with existing fundus imaging systems, that captures retinal reflectance across three optimized spectral bands to quantify spectral alterations linked to AD. We validate the system in a case-control study of 38 mild AD patients and 28 age-matched controls, revealing spatially resolved differences in a fundus map derived from the blue-to-green ratiometric channel (p \u003c 0.001). Our analysis identifies specifically the fovea-to-optic disc region as the most discriminative for AD, with an AUC of 0.74. Building on this, we developed a biologically informed machine-learning classification model incorporating spectral, clinical, and demographic data. On an independent validation test, the model achieved an AUC of 0.91, matching or slightly outperforming the most advanced spectral retinal measurements, yet using a simpler, more stable, and cost-effective setup that further facilitates clinical translation. The demonstrated technology, thanks to its non-invasiveness and its integrability with both existing medical technologies and advanced quantitative statistical methods, holds the potential to drive a significant leap forward in the early detection of AD, opening a window for timely intervention and thus profoundly impacting patient care.","manuscriptTitle":"Non-Invasive Screening of Alzheimer’s Disease via Label-Free Multispectral Retinal Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 02:21:27","doi":"10.21203/rs.3.rs-6172703/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a79f776-1429-4829-91cd-71af503fd43f","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45652871,"name":"Health sciences/Biomarkers"},{"id":45652872,"name":"Physical sciences/Engineering/Biomedical engineering"}],"tags":[],"updatedAt":"2025-04-15T02:21:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-15 02:21:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6172703","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6172703","identity":"rs-6172703","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}