Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks

Halil Şaban Erkartal; Melike Tatlı; Yusuf Secgin; Seyma Toy; Burak Suayip Duman

doi:10.58600/eurjther1606

Authors

Halil Şaban Erkartal Karabuk University, Faculty of Medicine, Graduate School of Education, Karabuk, Turkey, https://orcid.org/0000-0002-6558-3265
Melike Tatlı Karabuk University, Faculty of Medicine, Graduate School of Education, Karabuk, Turkey, https://orcid.org/0000-0001-8548-2421
Yusuf Secgin Karabuk University, Faculty of Medicine, Department of Anatomy, Karabuk, Turkey https://orcid.org/0000-0002-0118-6711
Seyma Toy Karabuk University, Faculty of Medicine, Department of Anatomy, Karabuk, Turkey https://orcid.org/0000-0002-6067-0087
Burak Suayip Duman Inonu University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Malatya, Turkey https://orcid.org/0000-0003-2552-0187

DOI:

https://doi.org/10.58600/eurjther1606

Keywords:

Upper dental arcade, cone-beamed computed tomography, estimation of gender, machine learning algorithms, artificial neural networks

Abstract

Objective: The aim of this study is to estimate gender with parameters obtained from the upper dental arcade by using machine learning algorithms and artificial neural networks.

Methods: The study was conducted on cone-beamed computed tomography images of 176 individuals between the ages of 18 and 55 who did not have any pathologies or surgical interventions in their upper dental arcade. The images obtained were transferred to RadiAnt DICOM Viewer program in Digital Imaging and Communications in Medicine format and all images were brought to orthogonal plane by applying 3D Curved Multiplanar Reconstruction. Length and curvature length measurements were performed on these images brought to orthogonal plane. The data obtained were used in machine learning algorithms (ML) and artificial neural networks input and rates of gender estimation were shown.

Results: In the study, an accuracy ratio of 0.86 was found with ML models linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) algorithm and an accuracy ratio of 0.86 was found with random forest (RF) algorithm. It was found with SHAP analyser of RF algorithm that the parameter of width at the level of 3rd molar teeth contributed the most to gender. An accuracy rate of 0.92 was found as a result of training for 500 times with multilayer perceptron classifier (MLCP), which is an artificial neural network (ANN) model.

Conclusion: As a result of our study, it was found that the parameters obtained from the upper dental arcade showed a high accuracy in estimation of gender. In this context, we believe that the present study will make important contributions to forensic sciences.

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References

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