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    Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks
    (Pera Yayincilik Hizmetleri, 2023) Erkartal, Halil Saban; Tatli, Melike; Secgin, Yusuf; Toy, Seyma; Duman, Suayip Burak
    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.
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    Gender Prediction Using Cone-Beam Computed Tomography Measurements from Foramen Incisivum: Application of Machine Learning Algorithms and Artificial Neural Networks
    (Wolters Kluwer Medknow Publications, 2024) Senol, Deniz; Secgin, Yusuf; Harmandaoglu, Oguzhan; Kaya, Seren; Duman, Suayip Burak; Oner, Zuelal
    Introduction: This study aims to predict gender using parameters obtained from images of the foramen (for.) incisivum through cone-beam computed tomography (CBCT) and employing machine learning (ML) algorithms and artificial neural networks (ANN).Materials and Methods: This study was conducted on 162 individuals in total. Precise measurements were meticulously extracted, extending from the foramen incisivum to the arcus alveolaris maxillaris, through employment of CBCT. The ML and ANN models were meticulously devised, allocating 20% for rigorous testing and 80% for comprehensive training.Results: All parameters that are evaluated, except for the angle between foramen palatinum majus and foramen incisivum-spina nasalis posterior (GPFIFPNS-A), exhibited a significant gender difference. ANN and among the ML algorithms, logistic regression (LR), linear discriminant analysis (LDA), and random rorest (RF) demonstrated the highest accuracy (Acc) rate of 0.82. The Acc rates for other algorithms ranged from 0.76 to 0.79. In the models with the highest Acc rates, 14 out of 17 male individuals and 13 out of 16 female individuals in the test set were correctly predicted.Conclusion: LR, LDA, RF, and ANN yielded high gender prediction rates for the measured parameters, while decision tree, extra tree classifier, Gaussian Naive Bayes, quadratic discriminant analysis, and K-nearest neighbors algorithm methods provided lower predictions. We believe that the evaluation of measurements extending from foramen incisivum to arcus alveolaris maxillaris through CBCT scanning proves to be a valuable method in gender prediction.