Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x

Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x | 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 Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x Rina Putri Noer Fadilah, Rasmi Rikmasari, Saiful Akbar, Arlette Suzy Puspa Pertiwi Setiawan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5657504/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background Dental caries is one of the most common chronic diseases in school-aged children, with a prevalence above 80% in Indonesia. Traditional diagnostic practices are time-consuming and dependent on the number of healthcare professionals available, and as such, have led to the creation of AI-based alternatives, such as the HI Bogi application. This research used the YOLO-v8 model to assist in the detection of dental caries, which is faster, more efficient, and highly accurate, and can be developed to enhance existing dental health programs in schools in Indonesia Materials and Methods A dataset of 3,221 JPG images labeled using the ICDAS D0 to D1 method was prepared and processed using Roboflow labeling software, and the images were resized to 640×640 pixels to standardize the input for model training. The dataset was divided into training (2,266 images), validation (635 images), and testing (320 images) subsets. The YOLOv8x algorithm was used for deep learning and the performance of the model was evaluated using confusion matrix analysis to calculate True Positive (TP), False Positive (FP), and False Negative (FN) values. Statistical Mann–Whitney tests were conducted to compare the classification accuracy between the AI model and dentists across the ICDAS categories, whereas a diagnostic speed test assessed the efficiency of the AI model relative to dentists. Results The YOLO-v8 model showed encouraging results, with a recall of 41.1%, accuracy of 72.6%, and mAP of 45.8%. P-values of 0.301 for D1, 0.690 for D2, 0.621 for D3, 0.693 for D4, 0.634 for D5, and 0.302 for D6 were obtained from comparative testing with dentists. These results showed no significant variations in the sensitivity, specificity, PPV, or NPV among the ICDAS categories (p > 0.05). Furthermore, AI performed much faster than the dentists during the examinations (p = 0.000). AI may improve the efficiency and efficacy of early caries diagnosis as demonstrated by these findings. Conclusion The YOLO-v8 model integrated into the application showed promising results in early caries detection, comparable to those of dentists across all ICDAS criteria. AI significantly outperformed dentists in terms of examination speed and completed tasks four times faster. Future research should explore transformer-based models to improve accuracy and expand the datasets to enhance the ability of the model to identify diverse caries categories, including rare lesions. Prevalent chronic disease Dental Caries YOLO-V8 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 25 Dec, 2024 Editor assigned by journal 23 Dec, 2024 Submission checks completed at journal 20 Dec, 2024 First submitted to journal 16 Dec, 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. 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-5657504","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392907723,"identity":"412684af-a1c1-44de-89f1-f520f9f5a2bc","order_by":0,"name":"Rina Putri Noer Fadilah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGUlEQVRIiWNgGAWjYDACZiD+YCDBw8/ewMCQUAARlAARbAwJOLUwziiwkZPsOQDUYkCMFpAung9pxgY3QAqQtTDg0GLeznvwA4/B4cSGm68TPzwwYEjcLt388AZDjR0DHzt2LTKH+ZIlJIBaGmfnbpYAOixx55xjxhYMx5IZ2HgeYNUiwcxjIGEA1NIsnbsBrGXDjQQzCQa2AwxsEthtAWox/pEA1NImeXbzD4iW9G8SDP/wajGTOGCQZswjwbsNakuOmQRjG34tlg0GNnISPLnbLBIMJIw33DlTbJHYl8yD0y/8Z4xv//kjwWN//Ozmmz8qbGQ33G7feOPDNzs5+XbstmAYAYkUoGIeotTDdY2CUTAKRsEoQAYAfXZZHCpx8qEAAAAASUVORK5CYII=","orcid":"","institution":"Padjadjaran University","correspondingAuthor":true,"prefix":"","firstName":"Rina","middleName":"Putri Noer","lastName":"Fadilah","suffix":""},{"id":392907725,"identity":"63726fae-77b5-4a92-be96-e9f2e71c7ef2","order_by":1,"name":"Rasmi Rikmasari","email":"","orcid":"","institution":"Padjadjaran University","correspondingAuthor":false,"prefix":"","firstName":"Rasmi","middleName":"","lastName":"Rikmasari","suffix":""},{"id":392907728,"identity":"a2361d27-985d-4c52-b28e-e290150a6af9","order_by":2,"name":"Saiful Akbar","email":"","orcid":"","institution":"Bandung Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Saiful","middleName":"","lastName":"Akbar","suffix":""},{"id":392907730,"identity":"e8e8011a-efc4-4130-bbbd-4387423872c5","order_by":3,"name":"Arlette Suzy Puspa Pertiwi Setiawan","email":"","orcid":"","institution":"Padjadjaran University","correspondingAuthor":false,"prefix":"","firstName":"Arlette","middleName":"Suzy Puspa Pertiwi","lastName":"Setiawan","suffix":""}],"badges":[],"createdAt":"2024-12-17 02:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5657504/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5657504/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12903-025-07486-x","type":"published","date":"2025-12-20T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98814835,"identity":"997a2160-4867-446c-9d49-4c6248b54751","added_by":"auto","created_at":"2025-12-22 16:12:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":797437,"visible":true,"origin":"","legend":"","description":"","filename":"drafpaper218Desember2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5657504/v1_covered_925b1650-0ce8-4179-b7b4-9deebe77e5c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x","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":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prevalent chronic disease, Dental Caries, YOLO-V8","lastPublishedDoi":"10.21203/rs.3.rs-5657504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5657504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDental caries is one of the most common chronic diseases in school-aged children, with a prevalence above 80% in Indonesia. Traditional diagnostic practices are time-consuming and dependent on the number of healthcare professionals available, and as such, have led to the creation of AI-based alternatives, such as the HI Bogi application. This research used the YOLO-v8 model to assist in the detection of dental caries, which is faster, more efficient, and highly accurate, and can be developed to enhance existing dental health programs in schools in Indonesia\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterials and Methods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA dataset of 3,221 JPG images labeled using the ICDAS D0 to D1 method was prepared and processed using Roboflow labeling software, and the images were resized to 640\u0026times;640 pixels to standardize the input for model training. The dataset was divided into training (2,266 images), validation (635 images), and testing (320 images) subsets. The YOLOv8x algorithm was used for deep learning and the performance of the model was evaluated using confusion matrix analysis to calculate True Positive (TP), False Positive (FP), and False Negative (FN) values. Statistical Mann\u0026ndash;Whitney tests were conducted to compare the classification accuracy between the AI model and dentists across the ICDAS categories, whereas a diagnostic speed test assessed the efficiency of the AI model relative to dentists.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe YOLO-v8 model showed encouraging results, with a recall of 41.1%, accuracy of 72.6%, and mAP of 45.8%. P-values of 0.301 for D1, 0.690 for D2, 0.621 for D3, 0.693 for D4, 0.634 for D5, and 0.302 for D6 were obtained from comparative testing with dentists. These results showed no significant variations in the sensitivity, specificity, PPV, or NPV among the ICDAS categories (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, AI performed much faster than the dentists during the examinations (p\u0026thinsp;=\u0026thinsp;0.000). AI may improve the efficiency and efficacy of early caries diagnosis as demonstrated by these findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe YOLO-v8 model integrated into the application showed promising results in early caries detection, comparable to those of dentists across all ICDAS criteria. AI significantly outperformed dentists in terms of examination speed and completed tasks four times faster. Future research should explore transformer-based models to improve accuracy and expand the datasets to enhance the ability of the model to identify diverse caries categories, including rare lesions.\u003c/p\u003e","manuscriptTitle":"Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-23 06:39:45","doi":"10.21203/rs.3.rs-5657504/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-25T06:51:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-24T04:40:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-20T14:51:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2024-12-17T02:12:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07f6f4d4-24f8-4f11-a3bc-5e82c8674315","owner":[],"postedDate":"December 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:08:57+00:00","versionOfRecord":{"articleIdentity":"rs-5657504","link":"https://doi.org/10.1186/s12903-025-07486-x","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2025-12-20 15:58:08","publishedOnDateReadable":"December 20th, 2025"},"versionCreatedAt":"2024-12-23 06:39:45","video":"","vorDoi":"10.1186/s12903-025-07486-x","vorDoiUrl":"https://doi.org/10.1186/s12903-025-07486-x","workflowStages":[]},"version":"v1","identity":"rs-5657504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5657504","identity":"rs-5657504","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}