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

Andrea Faragalli; Luigi Ferrante; Nikolaos Angelakopoulos; Roberto Cameriere; Edlira Skrami

doi:10.1016/j.forsciint.2024.112353

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

Forensic Sci Int. 2024 Dec 20:367:112353. doi: 10.1016/j.forsciint.2024.112353. Online ahead of print.

Authors

Andrea Faragalli¹, Luigi Ferrante², Nikolaos Angelakopoulos³, Roberto Cameriere⁴, Edlira Skrami²

Affiliations

¹ Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona 60126, Italy. Electronic address: a.faragalli@staff.univpm.it.
² Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona 60126, Italy.
³ Department of Orthodontics and Dentofacial Orthopedics, University of Bern, Freiburgstrasse 7, Bern 3010, Switzerland.
⁴ AgEstimation Project, Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.

PMID: 39733693
DOI: 10.1016/j.forsciint.2024.112353

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.

Keywords: Accuracy; Age estimation; Dental maturation; Estimation bias; Machine learning.