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Öğe Associating craniofacial morphometry determined by photo analysis with somatotype in healthy young individuals(2023) Toy, Şeyma; Şenol, Deniz; Öner, ZülalObjectives: Evaluation of the relationship between craniofacial parameters and somatotype provides important contributions to specialist physicians and anatomists in determining diseases and obtaining objective results of anthropometric measurements. The study was designed in line with this hypothesis and the aim was to find out how this relationship changed in healthy individuals. Methods: The study was conducted by examining 191 healthy individuals between the ages of 18 and 30. The individuals’ faces were photographed from a distance of 1 meter and craniofacial parameters were measured in Image J program. Somatotype analysis was conducted by using Heath-Carter somatotype method. Results: As a result of our study, the individuals were found to be grouped in four classes according to Heath-Carter somatotype method: (1) mesomorph endomorph, (2) endomorph ectomorph, (3) endomorph mesomorph and (4) central. Significant correlation was found between the second and first somatotype groups in terms of total nasal length, while significant correlation was found between second and first/second and third/fourth and third/first and third somatotype groups in terms of body mass index (BMI) parameter (p < 0.05). Craniofacial parameters were also evaluated and a very high correlation was found between total facial height and mandibular height, while there was a high correlation between total facial height and the other 16 parameters. Conclusions: As a result of our study, a relationship was found between somatotype groups and craniofacial parameters, within craniofacial parameters, and between somatotype and BMI. We believe that this relationship will guide morphological studies in basic medical sciences and surgical interventions in clinical sciences.Öğe A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium(Nature Portfolio, 2022) Toy, Şeyma; Seçgin, Yusuf; Öner, Zülal; Turan, Muhammed Kamil; Öner, Serkan; Şenol, DenizThe aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p <= 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy.