TY  - JOUR
TI  - Gender Estimation with Parameters Obtained From the Upper Dental Arcadeby Using Machine Learning Algorithms and Artificial Neural Networks
AB  - Objective: The aim of this study is to predict gender with parameters obtained from the upper dental arch by using machine learning algorithms (ML) machine learning algorithms and artificial neural networks to provide optimum aesthetics, functionality, long-term stability, diagnosis and treatment intervention in orthodontics, forensic medicine and anthropology. Methods: The study was conducted on cone-beam computed tomography (CBCT) 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 ML algorithms 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 multi-layer classifier perceptron (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.
AU  - Erkartal, Halil Şaban
AU  - Tatlı, Melike
AU  - SEÇGIN, YUSUF
AU  - toy, şeyma
AU  - DUMAN, SUAYIP BURAK
DO  - 10.58600/eurjther1606
PY  - 2023
JO  - European Journal of Therapeutics
VL  - 29
IS  - 3
SN  - 2564-7784
SP  - 352
EP  - 358
DB  - TRDizin
UR  - http://search/yayin/detay/1199952
ER  -