Explainable Deep Learning Framework for Periodontal Diagnosis in Dental Imaging

Explainable Deep Learning Framework for Periodontal Diagnosis in Dental Imaging | 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 Explainable Deep Learning Framework for Periodontal Diagnosis in Dental Imaging Yusra Fadhillah, Muhammad Noor Hasan Siregar, Ade Ismail Abdul Kodir, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7458110/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 Periodontal disease is a chronic inflammatory condition impacting tooth-supporting structures. Early detection via radiographic analysis is essential to prevent progression and tooth loss. However, radiograph interpretation remains subjective and reliant on clinical expertise. This study presents an automated diagnostic framework using a Convolutional Neural Network (CNN) combined with Explainable Artificial Intelligence (XAI) through Gradient-weighted Class Activation Mapping (Grad-CAM). The system classifies periapical dental radiographs into periodontal and normal categories. The proposed CNN includes four convolutional layers with ReLU activation, max-pooling, and two fully connected layers. Data augmentation improved generalizability, and training employed the Stochastic Gradient Descent with Momentum (SGDM) algorithm. The model achieved a 94.17% classification accuracy with stable convergence. Grad-CAM heatmaps highlighted relevant regions influencing the model's decisions. In periodontal cases, alveolar bone loss was consistently identified, while normal images showed no pathological patterns. This visualization enhances interpretability and clinical trust. The integration of CNN and Grad-CAM offers a reliable, transparent diagnostic tool for dental radiology and supports the ethical adoption of AI in healthcare. Periodontal diagnosis Deep learning Explainable AI Dental imaging Medical image analysis 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-7458110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522813849,"identity":"9125038e-5d15-4623-81f7-4bca2605f869","order_by":0,"name":"Yusra 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Imaging","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":"Periodontal diagnosis, Deep learning, Explainable AI, Dental imaging, Medical image analysis","lastPublishedDoi":"10.21203/rs.3.rs-7458110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7458110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Periodontal disease is a chronic inflammatory condition impacting tooth-supporting structures. Early detection via radiographic analysis is essential to prevent progression and tooth loss. However, radiograph interpretation remains subjective and reliant on clinical expertise. This study presents an automated diagnostic framework using a Convolutional Neural Network (CNN) combined with Explainable Artificial Intelligence (XAI) through Gradient-weighted Class Activation Mapping (Grad-CAM). The system classifies periapical dental radiographs into periodontal and normal categories. The proposed CNN includes four convolutional layers with ReLU activation, max-pooling, and two fully connected layers. Data augmentation improved generalizability, and training employed the Stochastic Gradient Descent with Momentum (SGDM) algorithm. The model achieved a 94.17% classification accuracy with stable convergence. Grad-CAM heatmaps highlighted relevant regions influencing the model's decisions. In periodontal cases, alveolar bone loss was consistently identified, while normal images showed no pathological patterns. This visualization enhances interpretability and clinical trust. The integration of CNN and Grad-CAM offers a reliable, transparent diagnostic tool for dental radiology and supports the ethical adoption of AI in healthcare.","manuscriptTitle":"Explainable Deep Learning Framework for Periodontal Diagnosis in Dental Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 14:24:35","doi":"10.21203/rs.3.rs-7458110/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":"9aea8ce9-075f-4d90-b34b-98fe947fb5b5","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T13:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 14:24:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7458110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7458110","identity":"rs-7458110","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}