An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study

Otso Tirkkonen; Henna Tiensuu; Elina Väyrynen; Jaakko Suutala; Ville Vuollo; Marja-Liisa Laitala; Saujanya Karki

doi:10.1186/s12903-026-07660-9

January 17, 2026BMC Oral HealthOpen Access

An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study

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Key Points

The aim is to evaluate the performance of a machine learning model for predicting dental caries risk using explainable AI approaches.
Conducted internal and external validation of a machine learning model.
Applied explainable artificial intelligence techniques for insights.
Compared model performance across different populations.
The model's performance significantly declined during external validation compared to internal validation.
The XAI methodology showed promise for personalized risk assessment of dental caries.

Implication

Cross-national validation shows model potential for individualized dental caries risk assessment, indicating future applicability.

Abstract

The performance of our ML model during external validation degraded notably compared to the internal validation. However, the XAI methodology exhibited great potential to be used in the future for individualized dental caries risk assessment.

Discussion

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Cite This Study

Tirkkonen et al. (2026) studied this question.

synapsesocial.com/papers/696f1a849e64f732b51eec7d https://doi.org/10.1186/s12903-026-07660-9

View Full Paper

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An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study

Key Points

Implication

Abstract

Discussion

Cite This Study

An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study

Key Points

Implication

Abstract

Discussion

Cite This Study

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