AI-driven Preclinical Disease Risk Assessment Using Imaging in UK Biobank

AI-driven Preclinical Disease Risk Assessment Using Imaging in UK Biobank | 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 AI-driven Preclinical Disease Risk Assessment Using Imaging in UK Biobank Dmitrii Seletkov, Sophie Starck, Tamara T. Mueller, Yundi Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6029287/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jul, 2025 Read the published version in npj Digital Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Identifying disease risk and detecting disease before clinical symptoms appear are essential for early intervention and improving patient outcomes. In this context, the integration of medical imaging in a clinical workflow offers a unique advantage by capturing detailed structural and functional information. Unlike non-image data, such as lifestyle, sociodemographic, or prior medical conditions, which often rely on self-reported information susceptible to recall biases and subjective perceptions, imaging offers more objective and reliable insights. Although the use of medical imaging in artificial intelligence (AI)-driven risk assessment is growing, its full potential remains underutilized. In this work, we demonstrate how imaging can be integrated into routine screening workflows, in particular by taking advantage of neck-to-knee whole-body magnetic resonance imaging (MRI) data available in the large prospective study UK Biobank. Our analysis focuses on three-year risk assessment for a broad spectrum of diseases, including cardiovascular, digestive, metabolic, inflammatory, degenerative, and oncologic conditions. We evaluate AI-based pipelines for processing whole-body MRI and demonstrate that using image-derived radiomics features provides the best prediction performance, interpretability, and integration capability with non-image data. Health sciences/Diseases Health sciences/Risk factors Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationAIdrivenPreclinicalDiseaseRiskAssessmentUsingImaginginUKBiobank.pdf SupplementaryData1.xlsx Cite Share Download PDF Status: Published Journal Publication published 26 Jul, 2025 Read the published version in npj Digital Medicine → Version 1 posted Editorial decision: Revision requested 30 Apr, 2025 Reviews received at journal 29 Apr, 2025 Reviews received at journal 26 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Submission checks completed at journal 17 Apr, 2025 First submitted to journal 07 Apr, 2025 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. 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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-6029287","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444536096,"identity":"127ab522-2120-44b7-8dfb-1ac6ba16d171","order_by":0,"name":"Dmitrii Seletkov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PMQrCMBSA4SdCp6JrXNorBERxKJ7FINTVqThJQ6FdvICTVxAE50igU3CUgIOKF6ibg6BJ1cGhzeqQf3oJ74MEwGb7x1gjBsBITU01MIB2ee3UEhq/ibJMkU5qJHq17ENwbiCtA8+us+kA/IzSayECsjktemuIgkrSORJKhXoYFrsEMxmSbe72JezDSoI1iTVBJEWs4N2+Jo2Um4m/umR3TbppSZ5mAmoNmOQedkrCav+yVMTFgiRIiNBDeRjJ0X5cSVrHyfkWP+aen/FdMcsDt53wrSyiYSX55v4eR0Zgs9lstrpeWztfX+AsB8IAAAAASUVORK5CYII=","orcid":"","institution":"Rechts der Isar Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dmitrii","middleName":"","lastName":"Seletkov","suffix":""},{"id":444536097,"identity":"44451aed-dcb5-4235-8b08-dd2ce7bcf12b","order_by":1,"name":"Sophie Starck","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Starck","suffix":""},{"id":444536098,"identity":"bd148d2b-01d9-4ce5-afd0-2a1abea9ab2b","order_by":2,"name":"Tamara T. 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