Non-Invasive Periodontal Disease Classification Using Thermograpy and Machine Learning: A Clinical Decision Support Approach

Non-Invasive Periodontal Disease Classification Using Thermograpy and Machine Learning: A Clinical Decision Support Approach | 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 Non-Invasive Periodontal Disease Classification Using Thermograpy and Machine Learning: A Clinical Decision Support Approach Antony Morales-Cervantes, Gerardo Marx Chávez-Campos, Adriana del Carmen Téllez-Anguiano, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6538241/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 diseases, including gingivitis and periodontitis, remain prevalent health issues requiring improved early detection strategies. This study aimed to develop and evaluate a non-invasive diagnostic support system combining infrared thermography and clinical features, powered by machine learning, for the classification of periodontal health status. A cross-sectional study was conducted on 91 subjects categorized as healthy, gingivitis, or periodontitis. Gingival temperature features were extracted from thermographic images taken from three facial views, complemented with clinical variables such as plaque index, age, sex, smoking status, and systemic diseases. Multiple machine learning algorithms were trained and evaluated using 10-fold cross-validation, with and without dimensionality reduction. A two-phase classification strategy yielded the best performance: logistic regression identified periodontitis cases, and XGBoost distinguished gingivitis from healthy subjects. The combined thermal and clinical feature model achieved an accuracy of 94.51% and an F1-score of 94.49%, while relying solely on thermal features reduced accuracy to 75.82%. The results highlight the strong potential of gingival thermography, supplemented by clinical data, in supporting periodontal disease classification. This study demonstrates the feasibility of AI-assisted thermographic screening as a non-invasive, accurate tool to enhance diagnostic precision and facilitate timely, personalized treatment decisions in dental practice. Infrared thermography Periodontal disease Machine learning Periodontitis Non-invasive diagnosis Dental screening 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. 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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-6538241","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451536822,"identity":"5e720894-8d70-42c4-91cb-afc8e0d9b85d","order_by":0,"name":"Antony Morales-Cervantes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYFAD9gYQacFDhFJmKM1zAERK8CBECGqRSACTDAS18M/uP/i4sI0hmn/m68THFRUSMvwN/Acf/NyxjUF3RgJWLRJ3DjMbz2xjyJ1xO3ez4ZkzEjwSB5iZDXvP3GYwO3MAuzU3ktmkeYFaNkjnbpNsbAP65QAzmzRjG1DL8QasOuThWiTPArX8k+CRP8DM/hus5TB2SwzgWiR4gVoaJHgMgLYw47PF8EaysTHPOYncGWeAfmk4JsFjCPScZG/bbR5cfpG7kfjwMU+ZTW5/+9mNDxtqbOzljjc+/PCz7bac2Q3sIQYGjGwSSDxotBBIBn/wS4+CUTAKRsEIBwAUZFhWmzsNYAAAAABJRU5ErkJggg==","orcid":"","institution":"TecNM - Instituto Tecnológico de Morelia","correspondingAuthor":true,"prefix":"","firstName":"Antony","middleName":"","lastName":"Morales-Cervantes","suffix":""},{"id":451536823,"identity":"4d8e45fe-634c-4048-9c80-9e6c080d8515","order_by":1,"name":"Gerardo Marx Chávez-Campos","email":"","orcid":"","institution":"TecNM - Instituto Tecnológico de Morelia","correspondingAuthor":false,"prefix":"","firstName":"Gerardo","middleName":"Marx","lastName":"Chávez-Campos","suffix":""},{"id":451536824,"identity":"980cb738-241a-45c6-9325-e89406c73e05","order_by":2,"name":"Adriana del Carmen Téllez-Anguiano","email":"","orcid":"","institution":"TecNM - Instituto Tecnológico de Morelia","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"del Carmen","lastName":"Téllez-Anguiano","suffix":""},{"id":451536825,"identity":"5c22e560-3c3d-4da5-af66-040604a4f7ce","order_by":3,"name":"Ricardo Martínez-Parrales","email":"","orcid":"","institution":"TecNM - Instituto Tecnológico de Morelia","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Martínez-Parrales","suffix":""},{"id":451536826,"identity":"7d41e950-63be-4b80-b383-f23a7aff7e07","order_by":4,"name":"Francisco Javier González","email":"","orcid":"","institution":"Universidad Autónoma de San Luis Potosí","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Javier","lastName":"González","suffix":""}],"badges":[],"createdAt":"2025-04-27 05:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6538241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6538241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82063734,"identity":"821cb336-0cce-4e41-a1ca-1b87d7e8fb44","added_by":"auto","created_at":"2025-05-06 12:21:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2365106,"visible":true,"origin":"","legend":"","description":"","filename":"JMSJournalofMedicalSystemsEstoma.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6538241/v1_covered_f077e8ef-b1cd-4711-babc-ffd929d0b8e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-Invasive Periodontal Disease Classification Using Thermograpy and Machine Learning: A Clinical Decision Support Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Infrared thermography, Periodontal disease, Machine learning, Periodontitis, Non-invasive diagnosis, Dental screening","lastPublishedDoi":"10.21203/rs.3.rs-6538241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6538241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeriodontal diseases, including gingivitis and periodontitis, remain prevalent health issues requiring improved early detection strategies. 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