Calibration, explainability and spatial uncertainty for YOLO-based detection in panoramic dental radiography

Calibration, explainability and spatial uncertainty for YOLO-based detection in panoramic dental radiography | 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 Calibration, explainability and spatial uncertainty for YOLO-based detection in panoramic dental radiography Hanan Alaskar, Nikolaos Nikolaou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8700851/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 Reliable computer-aided analysis of dental panoramic radiographs requires more than high average precision: predicted probabilities must be well-calibrated, explanations should be stable and clinically meaningful, and spatial uncertainty around detections should be explicit. Using a public panoramic X-ray dataset annotated for four common dental conditions–cavity, filling, implant, and impacted tooth–we train modern YOLOv11 variants and study trust-centred properties alongside standard performance. Our best model reaches strong detection metrics (e.g., F1 ≈ 0.81, [email protected] ≈ 0.812), while uncalibrated probabilities show modest overconfidence (ECE ≈ 0.033 at IoU 0.5) that improves with temperature scaling. We compare per-box visual explanations (Grad-CAM, LIME-WRaP, D-RISE, Adaptive Occlusion), revealing consistent patterns but also case-dependent instability that could mislead end-users if taken at face value. Finally, a lightweight “YOLO-σ” head estimating (σx,σy) provides spatial coverage guarantees with a small mAP trade-off, improving coverage–validity alignment for decision thresholds. Together, these results show that trustworthy deployment in dental workflows is feasible but requires explicit calibration, explanation auditing, and uncertainty-aware reporting rather than accuracy alone. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf 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. 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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-8700851","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586249778,"identity":"2e1aa622-a9a8-46ae-b70a-76c914f4a9d2","order_by":0,"name":"Hanan Alaskar","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hanan","middleName":"","lastName":"Alaskar","suffix":""},{"id":586249779,"identity":"7846eb7a-400d-45c6-b30f-15ea74d00482","order_by":1,"name":"Nikolaos Nikolaou","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Nikolaos","middleName":"","lastName":"Nikolaou","suffix":""}],"badges":[],"createdAt":"2026-01-26 13:39:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8700851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8700851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108771131,"identity":"e2c21996-af0b-4b65-85ea-59d4e030e971","added_by":"auto","created_at":"2026-05-08 08:27:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349930,"visible":true,"origin":"","legend":"","description":"","filename":"submission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8700851/v1_covered_686a6b2b-53cb-4d4a-89a7-97dc8ff2249c.pdf"},{"id":102272431,"identity":"69eb6aff-f513-4428-b649-a2b8ba053f9e","added_by":"auto","created_at":"2026-02-10 04:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":499797,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8700851/v1/fc8755fbd2089e74a7968871.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calibration, explainability and spatial uncertainty for YOLO-based detection in panoramic dental radiography","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-8700851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8700851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Reliable computer-aided analysis of dental panoramic radiographs requires more than high average precision: predicted probabilities must be well-calibrated, explanations should be stable and clinically meaningful, and spatial uncertainty around detections should be explicit. 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