[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125493-en":3,"doc-seo-125493-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125493,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Predicting restoration failures in primary and permanent teeth - A machine learning approach","Machine learning predictive models can manage complex medical data and produce accurate risk estimates. This study developed ML models to predict failures of posterior dental restorations in both primary and permanent teeth. Data were drawn from two clinical datasets: an RCT for permanent teeth (CaCIA Trial) and an RCT for primary teeth (CARDEC 3). Five algorithms were trained with cross-validation and calibration, evaluated with accuracy, precision, recall, F1-score, ROC AUC, and SHAP for interpretability. Primary-tooth models performed acceptably (AUC ~0.67–0.75), whereas permanent-tooth models showed lower performance (AUC ~0.53–0.62).","King’s Research Portal  \nDOI:  \n[10.1016/j.dental.2025.09.009](10.1016/j.dental.2025.09.009)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nDigmayer Romero, V. H. , Chaves, E. T. , Vinayahalingam, S. , Schuch, H. S. , Chen, X. , Li, Y. , Schwendicke, F. , Braga, M. M. , Raggio, D. P. , Signori, C. , Freitas, R. D. , Mendes, F. M. , Huysmans, M. C. , & Cenci, M. S. (2026) . Predicting restoration failures in primary and permanent teeth – A machine learning approach. Dental Materials, 42(1), 100-108 . [https://doi.org/10.1016/j.dental.2025.09.009](https://doi.org/10.1016/j.dental.2025.09.009)  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.  \n•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.  \n•You may not further distribute the material or use it for any profit-making activity or commercial gain  \n•You may freely distribute the URL identifying the publication in the Research Portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [librarypure@kcl.ac.uk](librarypure@kcl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 18. Mar. 2026  \nDental Materials 42 (2026) 100–108  \nContents lists available at ScienceDirect  \nDental Materials  \njournal [homepage:](homepage: www.elsevier.com/locate/dental)[ www.elsevier.com/locate/dental](homepage: www.elsevier.com/locate/dental)  \n| Predicting restoration failures in primary and permanent teeth – A machine   learning approach\u003Cbr>Vitor Henrique Digmayer Romero a,b,*, Eduardo Trota Chavesa,b, Shankeeth Vinayahalingamc, Helena Silveira Schuchb,d, Xiongjie Chene, Yunpeng Lie, Falk Schwendickef,\u003Cbr>Mariana Minatel Braga g , Daniela Pr´ocida Raggiog, C´acia Signori h , Raiza Dias Freitas g,i, Fausto Medeiros Mendes a,g , Marie-Charlotte Huysmans a , Maximiliano S´ergio Cencia\u003Cbr>a Department of Dentistry, Radboud Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands\u003Cbr>b Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil c Department of Oral and Maxillofacial Surgery, Radboud university medical center, Nijmegen, Netherlands d School of Dentistry, The University of Queensland, Brisbane, Australia\u003Cbr>e Centre for Oral, Clinical & Translational Sciences, King’s College London, London, UK f Clinic for Conservative Dentistry and Periodontology, LMU Klinikum, Munich, Germany g Department of Pediatric Dentistry, University of S˜ao Paulo, S˜ao Paulo, Brazil\u003Cbr>h Department of Restorative Dentistry, School of Dentistry, Uniavan University Center, Balnea´rio Camboriú, Brazil i Department of Social and Pediatric Dentistry, Federal University of Bahia, Salvador, Brazil |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Dental caries\u003Cbr>Clinical diagnosis Permanent dental restoration Machine learning |  | Objective: Machine learning (ML) predictive models promise to handle complex data and deliver accurate predictions in t","cbCaiiAgfK2oRDlb","https://ap.wps.com/l/cbCaiiAgfK2oRDlb","pdf",2192313,1,10,"English","en",105,"# Abstract\n## Objectives\n## Methods\n## Results\n## Conclusion\n## Clinical significance\n# Introduction\n## Background on restoration longevity\n## Study rationale","[{\"question\":\"What is the objective of this study?\",\"answer\":\"To develop machine learning predictive models for posterior dental restoration failures in both primary and permanent teeth.\"},{\"question\":\"Which datasets and algorithms were used?\",\"answer\":\"The study used two clinical datasets from RCTs (CaCIA Trial for permanent teeth and CARDEC 3 for primary teeth) and tested Decision Tree, Random Forest, XGBoost, CatBoost, and Neural Network with cross-validation and calibration.\"},{\"question\":\"How did model performance differ between primary and permanent teeth?\",\"answer\":\"For primary teeth, models showed acceptable predictive performance with AUC around 0.67–0.75; for permanent teeth, predictive ability was lower with AUC around 0.53–0.62.\"}]","Predicting restoration failures in primary and permanent teeth - A machine learning approach | PDF",1785899322,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-restoration-failures-in-primary-and-permanent-teeth-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-restoration-failures-in-primary-and-permanent-teeth-a-machine-learning-approach/125493/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the objective of this study?","Question",{"text":75,"@type":76},"To develop machine learning predictive models for posterior dental restoration failures in both primary and permanent teeth.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets and algorithms were used?",{"text":80,"@type":76},"The study used two clinical datasets from RCTs (CaCIA Trial for permanent teeth and CARDEC 3 for primary teeth) and tested Decision Tree, Random Forest, XGBoost, CatBoost, and Neural Network with cross-validation and calibration.",{"name":82,"@type":73,"acceptedAnswer":83},"How did model performance differ between primary and permanent teeth?",{"text":84,"@type":76},"For primary teeth, models showed acceptable predictive performance with AUC around 0.67–0.75; 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