Q-Bone System: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis – model development and validation

Q-Bone System: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis – model development and validation | 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 Q-Bone System: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis – model development and validation Wei Li, Jingyi Liu, Gepeng Ji, Zhuotao Yao, Dengping Fan, Jiang Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8425991/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objectives To develop and validate the Q-Bone system for precise alveolar bone loss quantification and intelligent periodontitis diagnosis across multiple clinical centers and imaging devices. Methods This study included 1,273 periodontitis cases from four clinical centers using diverse imaging devices. A multitask deep learning model, DGNet, was employed for tooth segmentation and anatomical key points localization, integrated with an anatomically-driven quantification algorithm. Performance was assessed using several validation datasets. Results The Q-Bone system demonstrated strong performance: tooth segmentation achieved an S-measure of 0.929, and key point localization reached a [email protected] of 0.994 in internal validation. The system showed high consistency with expert measurements, with an ICC of 0.973. It quantified alveolar bone loss with minimal bias (-0.238%) and assisted in periodontitis diagnosis, achieving a Kappa value of 0.955 for tooth-level diagnosis. Conclusions The Q-Bone system provides accurate, automated alveolar bone loss quantification and intelligent periodontitis diagnosis. It showed excellent generalization across multicenter and cross-device settings, making it a reliable tool for periodontitis diagnosis. Deep Learning Multitask Learning Anatomically-Driven Alveolar Bone Loss Periodontitis Diagnosis Multicenter Imaging Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 23 Dec, 2025 First submitted to journal 22 Dec, 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. 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-8425991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577747326,"identity":"b126191e-46c5-4bb4-bc41-57871cc2e618","order_by":0,"name":"Wei 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Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Multitask Learning, Anatomically-Driven, Alveolar Bone Loss, Periodontitis Diagnosis, Multicenter Imaging","lastPublishedDoi":"10.21203/rs.3.rs-8425991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8425991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo develop and validate the Q-Bone system for precise alveolar bone loss quantification and intelligent periodontitis diagnosis across multiple clinical centers and imaging devices.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 1,273 periodontitis cases from four clinical centers using diverse imaging devices. A multitask deep learning model, DGNet, was employed for tooth segmentation and anatomical key points localization, integrated with an anatomically-driven quantification algorithm. Performance was assessed using several validation datasets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe Q-Bone system demonstrated strong performance: tooth segmentation achieved an S-measure of 0.929, and key point localization reached a [email protected] of 0.994 in internal validation. The system showed high consistency with expert measurements, with an ICC of 0.973. It quantified alveolar bone loss with minimal bias (-0.238%) and assisted in periodontitis diagnosis, achieving a Kappa value of 0.955 for tooth-level diagnosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe Q-Bone system provides accurate, automated alveolar bone loss quantification and intelligent periodontitis diagnosis. It showed excellent generalization across multicenter and cross-device settings, making it a reliable tool for periodontitis diagnosis.\u003c/p\u003e","manuscriptTitle":"Q-Bone System: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis – model development and validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 10:14:16","doi":"10.21203/rs.3.rs-8425991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-21T00:02:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T22:47:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-24T04:06:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-12-22T09:22:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be84a7df-5d2b-4bf5-bb6a-a14f04bf89a3","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T19:33:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 10:14:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8425991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8425991","identity":"rs-8425991","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}