Do machine learning methods solve the main pitfall of linear regression in dental age estimation?

Abstract

Introduction

Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains deteriorate. Recently, Machine Learning algorithms have been used in age estimation, demonstrating high levels of accuracy. However, their precision with respect to the trend of age estimation error, typical in some traditional methods like linear regression, has not been thoroughly investigated.

Aim

To evaluate and compare the performance of frequently used Machine Learning-assisted methods against two traditional age estimation methods, linear regression and the Segmented Normal Bayesian Calibration model.

Methods

Overall, 1.949 orthopantomographs from black and white South African children aged 5–14 years, with 49 % males, were evaluated. The performance of Random Forest, Support Vector Regression, K-Nearest Neighbors and the Gradient Boosting Method were compared against traditional linear regression and the Segmented Normal Bayesian Calibration model. The comparison was based on accuracy measures, including Mean Absolute Error and Root Mean Squared Error, and precision measures, including the Inter-Quartile Range of the error distribution and the slope of the estimated age error relative to chronological age.

Results

The Machine Learning methods outperformed linear regression and the Segmented Normal Bayesian Calibration models in terms of accuracy, although the differences were small. Gradient Boosting Method and Support Vector Regression achieved the highest levels of accuracy (Mean Absolute Error: 0.69 years, Root Mean Squared Error: 0.85 years). All Machine Learning methods and linear regression exhibited significant bias in residuals, whereas the Segmented Normal Bayesian Calibration model showed no significant bias. Gender-stratified analyses revealed similar results in terms of the accuracy and precision of all considered models.

Conclusion

Although Machine Learning methods demonstrate high levels of accuracy, they may be prone to trends in error distribution when estimating dental age. Evaluating this error is crucial and should be an integral part of model performance evaluation. Future research should aim to improve accuracy while rigorously addressing systematic biases.