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

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ABSTRACT

Aim

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.

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.

Material and methods

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.

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.

Clinical Significance

Machine learning’s application in paediatric dentistry offers vital insights, enhancing early disease detection and personalised treatment planning. By analysing complex datasets, clinicians can identify key predictors, optimise resource allocation, and tailor interventions, ultimately improving oral health outcomes for children.

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