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    Evaluation of Hand Morphometry in Healthy Young Individuals from Different Countries
    (Universidad de la Frontera, 2024) Sahin, N.E.; Bakici, R.S.; Toy, S.; Oner, Z.
    This study aims to examine the hand morphometry of healthy young individuals from different countries and investigate the differences between countries in typing of hand based on the morphometric values obtained. In the study, 16 different parameters, including two surface areas and 14 lengths, were measured from the right hand of 579 volunteers (250 females, 329 males) from 7 different countries (Turkey, Chad, Morocco, Gabon, Kazakhstan, Senegal and Syria). Factor analysis was performed on the parameters, cluster analysis was performed according to the factor score obtained, and the hand types in the study were determined. As a result of the study, four different hand types were defined, and the distribution of these types according to countries was analyzed. All parameters showed significant differences between countries in both genders (p<0.05). According to the results of the study, there was a difference between male and female hand types between countries. In females, the type 1 hand type was found only in Gabon, the type 2 hand type was found only in Senegal, the type 3 hand type was found in Turkey, Morocco and Kazakhstan, while the type 4 hand type was significantly distributed in Senegal and Gabon (X2 =104.62; df=18, p<0.05). In males, type 1 hand type was found in Turkey, type 2 hand type in Senegal and Gabon, type 3 hand type in Turkey, while type 4 hand type was significantly distributed in Morocco and Kazakhstan (X2 =76.964; df=18, p<0.05). © 2024, Universidad de la Frontera. All rights reserved.
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    Sex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Model
    (Universidad de la Frontera, 2024) Bakici, R.S.; Cakmak, M.; Oner, Z.; Oner, S.
    The study aims to demonstrate the success of deep learning methods in sex prediction using hyoid bone. The images of people aged 15-94 years who underwent neck Computed Tomography (CT) were retrospectively scanned in the study. The neck CT images of the individuals were cleaned using the RadiAnt DICOM Viewer (version 2023.1) program, leaving only the hyoid bone. A total of 7 images in the anterior, posterior, superior, inferior, right, left, and right-anterior-upward directions were obtained from a patient's cut hyoid bone image. 2170 images were obtained from 310 hyoid bones of males, and 1820 images from 260 hyoid bones of females. 3990 images were completed to 5000 images by data enrichment. The dataset was divided into 80 % for training, 10 % for testing, and another 10 % for validation. It was compared with deep learning models DenseNet121, ResNet152, and VGG19. An accuracy rate of 87 % was achieved in the ResNet152 model and 80.2 % in the VGG19 model. The highest rate among the classified models was 89 % in the DenseNet121 model. This model had a specificity of 0.87, a sensitivity of 0.90, an F1 score of 0.89 in women, a specificity of 0.90, a sensitivity of 0.87, and an F1 score of 0.88 in men. It was observed that sex could be predicted from the hyoid bone using deep learning methods DenseNet121, ResNet152, and VGG19. Thus, a method that had not been tried on this bone before was used. This study also brings us one step closer to strengthening and perfecting the use of technologies, which will reduce the subjectivity of the methods and support the expert in the decision-making process of sex prediction. © 2024, Universidad de la Frontera. All rights reserved.