ArticleBMC oral health2024
Machine learning to predict untreated dental caries in adolescents.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.European journal of dentistry · 2026Article
- Machine Learning and Clustering Analysis of Class II and III Malocclusions.Clinical and experimental dental research · 2026Observational
- An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study.BMC oral health · 2026Article
- A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study.JMIR medical informatics · 2025Article
- Early childhood caries risk prediction using machine learning approaches in Bangladesh.BMC oral health · 2025Article
- Article
- Artificial intelligence in Brazilian Primary Health Care: scoping review.Revista brasileira de enfermagem · 2025Article
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Authors and funding
1 author.
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Abstract
objectiveThis study aimed to predict adolescents with untreated dental caries through a machine-learning approach using three different algorithms
methodsData came from an epidemiological survey in the five largest cities in Mato Grosso do Sul, Brazil. Data on sociodemographic characteristics, consumption of unhealthy foods and behaviours (use of dental floss and toothbrushing) were collected using Sisson's theoretical model, in 615 adolescents. For the machine learning, three different algorithms were used: (1) XGboost; (2) decision tree and (3) logistic regression. The epidemiological baseline was used to train and test predictions to detect individuals with untreated dental caries, through eight main predictor variables. Analyzes were performed using the R software (R Foundation for Statistical Computing, Vienna, Austria). The Ethics Committee approved the study..
resultsFor the 615 adolescents, xgboost performed better with an area under the curve (AUC) of 84% versus 81% for the decision tree algorithm. The most important variables were the use of dental floss, unhealthy food consumption, self-declared race and exposure to fluoridated water.
conclusionsFamily health teams can improve the work process and use artificial intelligence mechanisms to predict adolescents with untreated dental caries, and, in this way, schedule dental appointments for the treatment of adolescents earlier.
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Registered trials
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