Augmenting Early Stroke Diagnosis With an Eye-Tracker

Augmenting Early Stroke Diagnosis With an Eye-Tracker | 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 Augmenting Early Stroke Diagnosis With an Eye-Tracker Mohamed Abul Hassan, Yan Zhuang, Mohammed E-Rabbi, Chad Aldridge, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4656842/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 Posterior circulation stroke (PCS) presents significant diagnostic challenges due to poorly localizing and non-specific symptoms, such as dizziness, nausea, and headache, which are often misattributed to benign conditions. This study introduces an innovative diagnostic tool that utilizes a machine learning algorithm-driven eye tracker to enhance early diagnosis of PCS. Our approach involves analyzing eye movements during three standard neurological eye examinations: the Dot Test, H Test, and Optokinetic Nystagmus (OKN) Test. The Discrete Radon Cumulative Distribution Transform (DRCDT) and nearest subspace (NS) classification methods were employed to distinguish between PCS patients and healthy controls by identifying specific eye movement patterns. Results demonstrate that the ensemble model combining the three tests achieved the highest sensitivity and accuracy, with a sensitivity of 96% and an accuracy of 88%, in diagnosing PCS. This study's findings underscore the potential of an eye-tracker-based diagnostic tool to support a more accurate and efficient diagnosis, particularly for non-neurology trained providers, which would improve patient outcomes with more timely and appropriate treatment. The proposed tool offers a practical solution to the limitations of current diagnostic methods, such as the need for calibration and reliance on highly trained specialists, and can be seamlessly integrated into clinical settings to support emergency medical services (EMS) and emergency department (ED) triage. Health sciences/Neurology/Neurological disorders/Stroke Health sciences/Diseases/Neurological disorders/Stroke Full Text Additional Declarations (Not answered) Competing interests: The authors declare no competing interests. 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-4656842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329808388,"identity":"67444563-1b11-4808-ac01-18e7da23465e","order_by":0,"name":"Mohamed Abul Hassan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACAziLvQHEtSBeiwQDzwEQV4IULRIJEJogMGc//vjDzx2H68wln1/d8KNAgoG/vTsBrxbLnhwzyd4zhyUsZ+eU3ewBOkzizNkN+B12IIeNgbftsITB7Zy0GzxALQYSuQS0nH/++ONfkJabZ9Ju/iFKy40EA2mwLTfYj90mzpYbb8ykZdvSJXf25LDdljGQ4CHsl/Ppjz++bbPmBwbds5tv/tjI8bf34teC0MvAA44jHuKUQ7SwPyBe9SgYBaNgFIwoAAAWkkkajjiz3wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3076-8075","institution":"University California Davis","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"Abul","lastName":"Hassan","suffix":""},{"id":329808389,"identity":"fe666bd1-41bd-4c24-95f4-d6b33b825c7a","order_by":1,"name":"Yan Zhuang","email":"","orcid":"","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhuang","suffix":""},{"id":329808390,"identity":"6ea6e3c5-a461-4cfe-b3cb-2acdd41dfb97","order_by":2,"name":"Mohammed E-Rabbi","email":"","orcid":"","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"E-Rabbi","suffix":""},{"id":329808391,"identity":"4187f384-be09-47e7-ae51-16b151f6ed3e","order_by":3,"name":"Chad Aldridge","email":"","orcid":"","institution":"University of Virginia Health System","correspondingAuthor":false,"prefix":"","firstName":"Chad","middleName":"","lastName":"Aldridge","suffix":""},{"id":329808392,"identity":"ba67aeb6-1ee7-4878-a354-af67e0d0057e","order_by":4,"name":"Andrew Southerland,","email":"","orcid":"","institution":"University of Virginia Health System","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"Andrew","lastName":"Southerland","suffix":""},{"id":329808393,"identity":"57f6f7a7-8503-446c-944d-550dd8403a37","order_by":5,"name":"Gustavo Rohde","email":"","orcid":"","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Rohde","suffix":""}],"badges":[],"createdAt":"2024-06-28 22:15:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4656842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4656842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69995561,"identity":"e1879653-473f-497e-8b0e-a9ce1ba816da","added_by":"auto","created_at":"2024-11-27 10:09:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1619818,"visible":true,"origin":"","legend":"","description":"","filename":"PCSMLPaperForNature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4656842/v1_covered_788ae5ae-0939-4df2-bae1-9022c53570c6.pdf"}],"financialInterests":"\u003cp\u003e(Not answered)\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e","formattedTitle":"Augmenting Early Stroke Diagnosis With an Eye-Tracker","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4656842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4656842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Posterior circulation stroke (PCS) presents significant diagnostic challenges due to poorly localizing and non-specific symptoms, such as dizziness, nausea, and headache, which are often misattributed to benign conditions. This study introduces an innovative diagnostic tool that utilizes a machine learning algorithm-driven eye tracker to enhance early diagnosis of PCS. Our approach involves analyzing eye movements during three standard neurological eye examinations: the Dot Test, H Test, and Optokinetic Nystagmus (OKN) Test. The Discrete Radon Cumulative Distribution Transform (DRCDT) and nearest subspace (NS) classification methods were employed to distinguish between PCS patients and healthy controls by identifying specific eye movement patterns. Results demonstrate that the ensemble model combining the three tests achieved the highest sensitivity and accuracy, with a sensitivity of 96\\% and an accuracy of 88\\%, in diagnosing PCS. This study's findings underscore the potential of an eye-tracker-based diagnostic tool to support a more accurate and efficient diagnosis, particularly for non-neurology trained providers, which would improve patient outcomes with more timely and appropriate treatment. 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