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Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

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Published on: April 14, 2016

Machine Learning Models for Identifying Dental Pain in Adolescents.

Luiz Alexandre Chisini¹, Luana Carla Salvi², Francine Dos Santos Costa³

¹Graduate Program in Dentistry, Federal University of Pelotas, Rio Grande do Sul, Pelotas, Brazil; Radboud University Medical Center, Department of Dentistry, Nijmegen, The Netherlands.

International Dental Journal

|March 14, 2026

Summary

This summary is machine-generated.

Machine learning (ML) shows potential for identifying dental pain in Brazilian adolescents for public health screening. However, modest performance and fairness issues require further development before widespread use.

Keywords:

Dental pain Diagnostic models Oral diseases

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Area of Science:

Public Health
Machine Learning
Adolescent Health

Background:

Dental pain is a significant public health concern among adolescents.
Screening tools are needed to identify at-risk populations for early intervention.

Purpose of the Study:

To evaluate the effectiveness of machine learning (ML) algorithms in identifying dental pain in Brazilian adolescents.
To assess the potential of ML for public health screening of dental pain.

Main Methods:

Utilized data from the Brazilian National Survey of School Health (PeNSE) from 2015 and 2019, including 259,833 adolescents aged 11-18.
Trained and tested nine ML models on the 2015 dataset and validated externally using the 2019 dataset.
Evaluated model performance using metrics like Area Under the Curve (AUC) and Recall, alongside fairness assessments.

Main Results:

The Extra Trees (ET) model demonstrated the best performance, with an AUC of 0.64 and Recall of 0.57 in the test set.
The model achieved a Recall of 0.57 in the external validation set, indicating a modest ability to identify adolescents with dental pain.
Fairness analysis revealed disparities in accuracy and recall across different demographic groups (sex, race), with sex, alcohol consumption, and family violence identified as key predictive variables.

Conclusions:

Machine learning holds promise for identifying dental pain in adolescent populations.
Current predictive performance and fairness limitations necessitate further research and model refinement for practical public health application.