Publication:
Application of machine learning for data analysis in paediatric dentistry: a systematic review

Simple item page
dc.contributor.authorGómez Ríos, Inmaculada
dc.contributor.authorSaura López, Virginia
dc.contributor.authorPérez Silva, Amparo
dc.contributor.authorSerna Muñoz, Clara
dc.contributor.authorOrtiz Ruiz, Antonio José
dc.contributor.departmentDermatología, Estomatología, Radiología y Medicina Física
dc.contributor.otherFacultad de Medicina
dc.date.accessioned2026-02-03T07:18:38Z
dc.date.available2026-02-03T07:18:38Z
dc.date.copyright© 2025, European Journal of Paediatric Dentistry
dc.date.issued2025-05
dc.description.abstractAim: The study aims to assess whether the application of machine learning (ML) for database analysis enhances the approach to oral diseases in the paediatric population. Materials: Dental caries affects 514 million children worldwide. Artificial intelligence (AI), particularly ML, has seen increased utilisation in medicine and dentistry, handling data beyond human capacity to discern patterns and make predictions. PubMed, Web of Science, Scopus, and Lilacs databases were searched. Topics covered include the impact of oral health on adolescents' quality of life, predictors of early childhood caries and of the need of second treatment under deep sedation, and the effectiveness of preventive dental services. Methods: Twenty articles meeting eligibility criteria were analyzed for quality using the QUADAS-2 scale. The systematic review adhered to the PRISMA statement, yielding 20 articles out of 1945 initially screened. Fourteen articles focused on caries prediction, highlighting socio-demographic, behavioural, and biological predictors. ML analysis revealed that children with early caries lesions incur higher costs for insurers, with those receiving sealants and fluoride demonstrating greater cost savings. Conclusion: ML algorithms can identify patterns in large datasets, enhancing approaches to paediatric oral diseases. Their integration into research and educational programs is recommended. Methodological guidelines and quality scales specific to such studies are necessary for improved scientific evidence.
dc.formatapplication/pdf
dc.format.extent11
dc.identifier.citationGómez-Ríos I, Saura-López V, Pérez-Silva A, Serna-Muñoz C, Ortiz-Ruiz AJ. Application of machine learning for data analysis in paediatric dentistry: a systematic review. Eur J Paediatr Dent. 2025 May 1:1. doi: 10.23804/ejpd.2025.2288.
dc.identifier.doihttps://doi.org/10.23804/ejpd.2025.2288
dc.identifier.eissn2035-648X
dc.identifier.urihttp://hdl.handle.net/10201/198269
dc.languageeng
dc.publisherSIOI, Italian Society of Paediatric Dentistry
dc.relationSin financiación externa a la Universidad
dc.relation.publisherversionhttps://www.ejpd.eu/abstract-pubmed/application-of-machine-learning-for-data-analysis-in-paediatric-dentistry-a-systematic-review/
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDentistry
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subject.odsObjetivo 3: Salud
dc.titleApplication of machine learning for data analysis in paediatric dentistry: a systematic review
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication5792e8a8-afbb-4c5c-bcca-6cb091a93088
relation.isAuthorOfPublication8f2b9ceb-be71-4257-a82d-eba42a375885
relation.isAuthorOfPublicationc42f35e8-5f95-4c41-b084-a18ea989caea
relation.isAuthorOfPublication.latestForDiscovery5792e8a8-afbb-4c5c-bcca-6cb091a93088
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Application of machine learning for data analysis in paediatric dentistry- a systematic review.pdf
Size:
932.34 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.37 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos