Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams

Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams | 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 Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams Ananya Subramaniam, Hao Wu, Sepideh Azarianpour-Esfahani, Naomi Joseph, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6630820/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Aortic valve stenosis (AS) is the most common valvular disease, with a growing impact in the aging population. AS can culminate in heart failure if left untreated. Early treatment with minimally-invasive transcatheter aortic valve replacement (TAVR) is being evaluated in clinical trials. We addressed an unmet clinical need for early detection, referral to echocardiography for AS evaluation, and possible treatments (e.g., lifestyle changes, drugs, or TAVR). As aortic valve calcification (AVC) is typically present in AS, we created a method to detect AVC in low-cost/no-cost non-contrast CT calcium score (CTCS) screening exam images. We developed a multi-task deep network to identify a cylindrical aortic valve region of interest (ROI) and applied Agatston criteria within the ROI to obtain calcifications. Predicted ROIs had good agreement with cardiologists’ labels and sometimes were better. Predicted Agatston scores agreed with cardiologists (r=1.00, paired t-test p=0.573, t=0.57). On a retrospective screening cohort of 2000+ patients, we found that 20.6% of men and 22.2% of women had some degree of AVC. According to guidelines, 3.53% and 9.04%, respectively, would have severe AS. These promising results indicate that further evaluation of this approach is warranted, with the potential for significant public health impact. Health sciences/Cardiology Physical sciences/Engineering/Biomedical engineering Physical sciences/Mathematics and computing/Computer science Aortic valve stenosis aortic valve calcification CT calcium score parameter regression Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers invited by journal 29 May, 2025 Editor assigned by journal 14 May, 2025 Submission checks completed at journal 13 May, 2025 First submitted to journal 09 May, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6630820","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463851084,"identity":"49b833a3-5499-4475-9224-2f0e41fa4be4","order_by":0,"name":"Ananya Subramaniam","email":"","orcid":"","institution":"Department of Biomedical Engineering, Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Ananya","middleName":"","lastName":"Subramaniam","suffix":""},{"id":463851085,"identity":"77244613-1c3f-4eb8-9c39-9c1699beb3f8","order_by":1,"name":"Hao Wu","email":"","orcid":"","institution":"Department of Biomedical 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