Estimation of Human Age using Machine Learning on Panoramic Radiographs: A Pilot Study for Brazilian Patients

Estimation of Human Age using Machine Learning on Panoramic Radiographs: A Pilot Study for Brazilian Patients | 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 Estimation of Human Age using Machine Learning on Panoramic Radiographs: A Pilot Study for Brazilian Patients Willian Oliveira, Mariana Albuquerque, Caio Augusto Pereira Burgardt, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4497849/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with cutting-edge machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach for age estimation. We also present a new dataset composed of 12,827 dental panoramic X-ray images that reflect the specific demographic characteristics of the Brazilian population. The proposed approach achieved robust and reliable results, achieving a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, including pulp chamber dimensions and stages of permanent teeth calcification, even in edentulous cases. The model also relies on information from the mandible, maxillary sinus, and vertebrae, indicating a wide range of anatomical features for decision-making. This study demonstrates the significant potential of AI to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution. Health sciences/Anatomy/Oral anatomy Health sciences/Biomarkers/Predictive markers Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviews received at journal 12 Jun, 2024 Reviewers agreed at journal 01 Jun, 2024 Reviewers invited by journal 30 May, 2024 Editor assigned by journal 30 May, 2024 Editor invited by journal 30 May, 2024 Submission checks completed at journal 30 May, 2024 First submitted to journal 29 May, 2024 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-4497849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":313295434,"identity":"fc825e9e-d789-40c4-9bca-b899f224e564","order_by":0,"name":"Willian Oliveira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYDAC5gMMDB8YJIAsxgdA4gARWtgSGBhngLUwGxCvhZkHYh+RWgyOMT98bFNjkccgkcz24APDnXwitLAZG+cckygGamE3nMHwzLKBkBaz+z1s0rkNEokNEvnHpHkYDhsQtMXsGA+btCVYSzKb9B+itTDCtDAQo8Ue6BfDnmMSiW08j9kNewyeEdYi2cb88MGPmrrEfnZgiP2ouENYCxywgREJGmC6RsEoGAWjYBRgAQBQqjRSxXk1fgAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":true,"prefix":"","firstName":"Willian","middleName":"","lastName":"Oliveira","suffix":""},{"id":313295435,"identity":"c2dd6e38-9af7-4d43-8e37-091dbf793b0b","order_by":1,"name":"Mariana Albuquerque","email":"","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Albuquerque","suffix":""},{"id":313295436,"identity":"746599b6-0a86-4d2e-8511-ebfd01eb16b5","order_by":2,"name":"Caio Augusto Pereira Burgardt","email":"","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Caio","middleName":"Augusto Pereira","lastName":"Burgardt","suffix":""},{"id":313295437,"identity":"00019b0d-4ad7-4fad-9da2-8a78316bb034","order_by":3,"name":"Maria Luiza dos Anjos Pontual","email":"","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Luiza dos Anjos","lastName":"Pontual","suffix":""},{"id":313295439,"identity":"b15dcee8-7be9-4332-9e60-621038032444","order_by":4,"name":"Cleber Zanchettin","email":"","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Cleber","middleName":"","lastName":"Zanchettin","suffix":""}],"badges":[],"createdAt":"2024-05-29 14:48:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4497849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4497849/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-70621-1","type":"published","date":"2024-08-24T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63300159,"identity":"613b61f3-f169-4f22-8cc8-4e1974c309a2","added_by":"auto","created_at":"2024-08-26 16:12:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":970310,"visible":true,"origin":"","legend":"","description":"","filename":"EstimationofAgeOriginal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4497849/v1_covered_090b238e-62bb-42ca-b3f4-1ec5ebbad39d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of Human Age using Machine Learning on Panoramic Radiographs: A Pilot Study for Brazilian Patients","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4497849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4497849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with cutting-edge machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach for age estimation. 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