Dental Caries Type and Severity Detection: A Comparative Study of YOLOv10 and YOLOv11

Dental Caries Type and Severity Detection: A Comparative Study of YOLOv10 and YOLOv11 | 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 Dental Caries Type and Severity Detection: A Comparative Study of YOLOv10 and YOLOv11 Parsa ForouzeshFar, Ali Asghar Safaei, Afagh Tavassoli, Sedighe Sadat Hashemi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8313891/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 Background Accurate diagnosis of dental caries on radiographs is often tricky and varies among clinicians. Advances in deep learning have made automated detection feasible, but few studies have examined detailed classification across multiple stages of the disease. Methods We compared YOLOv10 and YOLOv11 for detecting and classifying caries on bitewing radiographs. A dataset of 1,187 cases was labeled into ten clinically defined categories, covering both severity (sound, mild, moderate, severe) and lesion type (primary, recurrent, crown-related, occlusal). Models were trained with standardized parameters, preprocessing, and augmentation, and performance was evaluated using precision, recall, F1-score, and mean Average Precision at IoU thresholds of 0.5 and 0.5–0.95. Results Both models achieved high overall accuracy. YOLOv10 provided the most consistent performance, reaching a mAP50 of 98.9, mAP50-95 of 98.8%, Precision of 97.6%, Recall of 97% and F1-score of 97.3%. It was especially effective at detecting mild and moderate lesions. YOLOv11, although less stable overall (mAP50–95 of 80.4%), achieved perfect recall for crown-recurrent and occlusal-recurrent lesions, categories that are rare but clinically important. Conclusion YOLOv10 is better suited for routine diagnostic use due to its balanced accuracy and stability, while YOLOv11 maximizes sensitivity for uncommon caries types, although it can also detect common caries well. Compared to earlier YOLO and CNN-based studies, these models achieved higher accuracy and covered a wider range of clinically meaningful categories. Fine-grained, real-time caries classification is therefore achievable and has clear potential to aid decision-making in preventive and restorative dentistry. Dental Caries Severity Detection Segmentation YOLO algorithm Full Text Additional Declarations No competing interests reported. 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-8313891","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584684723,"identity":"e4c62ed3-ba1c-41e4-9d15-0bfe66bc5478","order_by":0,"name":"Parsa ForouzeshFar","email":"","orcid":"","institution":"University of Windsor","correspondingAuthor":false,"prefix":"","firstName":"Parsa","middleName":"","lastName":"ForouzeshFar","suffix":""},{"id":584684724,"identity":"8e7248a8-d9e0-4db6-a080-8f3cc3915698","order_by":1,"name":"Ali Asghar 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YOLOv11","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":"Dental Caries, Severity Detection, Segmentation, YOLO algorithm","lastPublishedDoi":"10.21203/rs.3.rs-8313891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8313891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate diagnosis of dental caries on radiographs is often tricky and varies among clinicians. Advances in deep learning have made automated detection feasible, but few studies have examined detailed classification across multiple stages of the disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe compared YOLOv10 and YOLOv11 for detecting and classifying caries on bitewing radiographs. A dataset of 1,187 cases was labeled into ten clinically defined categories, covering both severity (sound, mild, moderate, severe) and lesion type (primary, recurrent, crown-related, occlusal). Models were trained with standardized parameters, preprocessing, and augmentation, and performance was evaluated using precision, recall, F1-score, and mean Average Precision at IoU thresholds of 0.5 and 0.5\u0026ndash;0.95.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth models achieved high overall accuracy. YOLOv10 provided the most consistent performance, reaching a mAP50 of 98.9, mAP50-95 of 98.8%, Precision of 97.6%, Recall of 97% and F1-score of 97.3%. It was especially effective at detecting mild and moderate lesions. YOLOv11, although less stable overall (mAP50\u0026ndash;95 of 80.4%), achieved perfect recall for crown-recurrent and occlusal-recurrent lesions, categories that are rare but clinically important.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eYOLOv10 is better suited for routine diagnostic use due to its balanced accuracy and stability, while YOLOv11 maximizes sensitivity for uncommon caries types, although it can also detect common caries well. Compared to earlier YOLO and CNN-based studies, these models achieved higher accuracy and covered a wider range of clinically meaningful categories. Fine-grained, real-time caries classification is therefore achievable and has clear potential to aid decision-making in preventive and restorative dentistry.\u003c/p\u003e","manuscriptTitle":"Dental Caries Type and Severity Detection: A Comparative Study of YOLOv10 and YOLOv11","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 19:41:35","doi":"10.21203/rs.3.rs-8313891/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"e9147f04-efb6-41d1-a59d-1c5c74f0b6f5","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-13T07:50:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T08:00:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 19:41:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8313891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8313891","identity":"rs-8313891","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}