Generative AI-Enhanced Microcalcification Detection in Full-Field Digital Mammography: Reducing False Positives with High Sensitivity

Generative AI-Enhanced Microcalcification Detection in Full-Field Digital Mammography: Reducing False Positives with High Sensitivity | 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 Research Article Generative AI-Enhanced Microcalcification Detection in Full-Field Digital Mammography: Reducing False Positives with High Sensitivity Kyungsu Kim, Manisha Bahl, Adham Mahmoud Alkhadrawi, Young-Tak Kim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7839897/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 While generative AI shows promise across industries, its advantages over state-of-the-art segmentation AI in radiology remain unclear. This study addresses this gap by developing a generative AI model to refine calcification detection in full-field digital mammograms (FFDM), improving specificity while maintaining high sensitivity. A segmentation AI initially extracted calcification pixels, achieving 98.0% sensitivity but a low positive predictive value (PPV) of 3.2%. To enhance detection, our generative AI used the segmentation AI output as a structural prior, transforming calcification-positive pixels into calcification-free pixels and generating a corrected result by subtraction. Trained on true calcification-free regions, it categorized densities within and around each calcification as interior or exterior. Our approach improved PPV by 2.28-fold (from 3.2% to 7.3%), surpassing prior generative AI models by 146-fold (from 0.05% to 7.3%), while maintaining sensitivity above 95%. It also reduced patient-level detection errors for small calcifications (5.17-fold, from 27.43% to 5.31%), high-exterior density (4.20-fold, from 53.85% to 12.82%), and low-interior density (2.89-fold, from 63.95% to 22.09%). This study serves as a seminal reference, demonstrating generative AI’s radiological significance beyond the latest segmentation models, with potential to redefine screening accuracy and generate high-fidelity virtual normal FFDM references. Generative AI Unsupervised Anomaly Detection Refinement Full-Field Digital Mammography Microcalcification Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx 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-7839897","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548602154,"identity":"91ed62e1-418e-43b1-b456-11a4d26864b0","order_by":0,"name":"Kyungsu Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Kyungsu","middleName":"","lastName":"Kim","suffix":""},{"id":548602155,"identity":"8a55f220-e97a-4650-8e87-54e1699cca96","order_by":1,"name":"Manisha Bahl","email":"","orcid":"","institution":"Massachusetts General 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Sensitivity","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":"Generative AI, Unsupervised Anomaly Detection, Refinement, Full-Field Digital Mammography, 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This study addresses this gap by developing a generative AI model to refine calcification detection in full-field digital mammograms (FFDM), improving specificity while maintaining high sensitivity. A segmentation AI initially extracted calcification pixels, achieving 98.0% sensitivity but a low positive predictive value (PPV) of 3.2%. To enhance detection, our generative AI used the segmentation AI output as a structural prior, transforming calcification-positive pixels into calcification-free pixels and generating a corrected result by subtraction. Trained on true calcification-free regions, it categorized densities within and around each calcification as interior or exterior. Our approach improved PPV by 2.28-fold (from 3.2% to 7.3%), surpassing prior generative AI models by 146-fold (from 0.05% to 7.3%), while maintaining sensitivity above 95%. It also reduced patient-level detection errors for small calcifications (5.17-fold, from 27.43% to 5.31%), high-exterior density (4.20-fold, from 53.85% to 12.82%), and low-interior density (2.89-fold, from 63.95% to 22.09%). 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