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Öğe Analysis of the effects of total pneumatized turbinate volume on septum deviation, maxillary sinus volume, and maxillopalatal parameters: A multidetector computerized tomography study(Wolters Kluwer Medknow Publications, 2023) Senol, Deniz; Oner, Serkan; Secgin, Yusuf; Oner, Zulal; Toy, SeymaIntroduction: The aim of this study was to examine the effects of pneumatized turbinate volume (PTV) on nasal septum deviation (NSD), maxillary sinus volume (MSV), and maxillopalatal parameters with multidetector computed tomography (MDCT). Material and Methods: MDCT images of a total of 73 patients (35 females and 38 males) between the ages of 25 and 58 years were used in the study. PTV, MSV, and NSD angle and direction and interalveolar distance (IAD), maxillary spin distance (MSD), and maxillopalatal angle (MPA) measurements were made on images brought to the orthogonal plane in 3 plans. Results: Turbinate pneumatization (superior, middle, or inferior) was found in a total of 55 (75.3%) patients (28 females and 27 males). The number of patients with turbinate pneumatization on the right side was 14 (19.2%), while the number of patients with turbinate pneumatization on the left side was 15 (20.5%) and the number of bilateral pneumatization was 26 (35.6%). While no significant association was found between the presence of turbinate pneumatization and septal deviation angle, MSV, MPA, IAD, and MSD measurements, a significant difference was found between the groups in terms of PTV (P < 0.05). No significant association was found between NSD direction and all parameters. Discussion and Conclusion: In this study, we conducted with MDCT images, in addition to the highest incidence in turbinate pneumatization with 75.3%; it was found that PTV did not have an effect on NSD amount, MSV, and maxillopalatal parameters. Men were found to have higher NSD angle, higher maxillary sinus aeration, and larger IAD when compared with women.Öğe Can Typical Cervical Vertebrae Be Distinguished from One Another by Using Machine Learning Algorithms? Radioanatomic New Markers(Duzce Univ, Fac Medicine, 2023) Senol, Deniz; Secgin, Yusuf; Toy, Seyma; Oner, Serkan; Oner, ZulalObjective: The aim of this study is to distinguish the typical cervical vertebrae that cannot be separated from one another with the naked eye by using machine algorithms (ML) with measurements made on computerized tomography (CT) images and to show the differences of these vertebrae.Methods: This study was conducted by examining the 536 typical cervical vertebrae CT images of 134 (between the ages of 20 and 55) individuals. Measurements of cervical vertebrae were made on coronal, axial and sagittal section. 6 different combinations (Group 1: C3 - C4, Group 2: C3 - C5, Group 3: C3 - C6, Group 4: C4 - C5, Group 5: C4 - C6, Group 6: C5 - C6) were formed with parameters of each vertebrae and they were analyzed in ML algorithms. Accuracy (Acc), Matthews correlation coefficient (Mcc), Specificity (Spe), Sensitivity (Sen) values were obtained as a result of the analysis.Results: As a result of this study, the highest success was obtained with Linear Discriminant Analysis (LDA) and Logistic Regression (LR) algorithms. The highest Acc rate was found as 0.94 with LDA and LR algorithm in Groups 3 and Group 4, the highest Spe value was found as 0.95 with LDA and LR algorithm in Group 5, the highest Mcc value was found as 0.90 with LDA and LR algorithm in Group 5 and the highest Sen value was found as 0.94 with LDA and LR algorithm in Groups 3 and 5. Conclusions: As a conclusion, it was found that typical cervical vertebrae can be distinguished from each other with high accuracy by using ML algorithms.Öğe Determination of Sex Differences Using Machine Learning Algorithms and Artificial Neural Networks with Parameters Obtained from Basilar Artery(Universidad de la Frontera, 2024) Secgin, Yusuf; Erkartal, Halil Saban; Tatlı, Melike; Toy, Seyma; Oner, Zulal; Oner, SerkanThe determination of sex differences in anatomical structures is critical in establishing gold standard morphometric data in basic medical sciences, and in surgical and internal sciences in selecting the right area during invasive intervention and applying the correct intervention methodology appropriate to the area. The aim of this study is to determine the sex difference using Machine learning (ML) algorithms and Artificial neural networks (ANN) with parameters obtained from basilar artery. The study was performed on computed tomography angiography images of 63 women and 94 men. The following parameters were measured on the images: initial width of the right vertebral artery, initial width of the left vertebral artery, termination width of the right vertebral artery, termination width of the left vertebral artery, basilar artery width, and basilar artery length. The measurements were used in ML algorithms and ANN input to determine sex differences. As a result of the study, a sex difference rate of 0.84 was determined with the ML algorithms Random Forest (RF), Quadratic Discriminant Analysis (QDA), Extra Tree Classifier (ETC) and 0.84 with the Multilayer Perceptron Classifier (MLCP) algorithm of ANN. As a result of the study, sex difference was found with an accuracy rate of 0.84 using ML algorithms and ANN with parameters obtained from basilar artery. In this context, we think that this study will shed light on basic and clinical medical sciences. © 2024, Universidad de la Frontera. All rights reserved.Öğe Evaluation of Hand Morphometry in Healthy Young Individuals from Different Countries(Soc Chilena Anatomia, 2024) Sahin, Necati Emre; Bakici, Rukiye Sumeyye; Toy, Seyma; Oner, ZulalThis 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 (X-2 =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 (X-2 =76.964; df=18, p<0.05).Öğe Evaluation of palmar creases of healthy young individuals of different countries(Cukurova Univ, Fac Medicine, 2024) Sahin, Necati Emre; Bakici, Rukiye Sumeyye; Oner, Zulal; Toy, SeymaPurpose: This study aims to evaluate the potential effects of gender and country factors on palmar creases by examining the palmar creases of young adults from various countries. Materials and Methods: The study involved a total of 220 volunteers, including 120 males and 100 females aged 18-30, from seven different countries (Jordan, Sudan, Somalia, Iran, Iraq, Tanzania and Turkey), as well as students from Karabuk University. Hand types were evaluated based on palmar creases and the number of origins for both hands. Total Degree of Transversality (TDoT) values for palmar creases were calculated. Classification of palmar creases and comparison of T -DoT values for both hands were performed between genders and countries. Results: The study analyzed 440 hands from 220 individuals, identifying 1 Simian, 8 Suwon, and 5 Sydneytype hands, while categorizing the remaining 426 hands as normal type. Regarding the number of palmar crease origins, it was observed that there was a single origin in 3 hands, two origins in 309 hands and three origins in 119 hands. Significant associations were found between genders and countries in the number of palmar crease origins. In addition, significant differences in right hand TDoT values were found between genders and countries. Conclusion: In spite of limitations in sample selection and size, these results are important in providing a basis for future in-depth research on palmar creases at later stages, although generalizability to the specific countries represented in the sample may be limited. Consequently, this study highlights variations among countries concerning both the number of palm crease origins and right-hand T -DoT values.Öğe Gender prediction using geometric morphometry with parameters of the cranium obtained from computed tomography images(Cukurova Univ, Fac Medicine, 2024) Secgin, Yusuf; Oner, Zulal; Oner, Serkan; Toy, SeymaPurpose:The gender difference of the cranium skeleton is of great importance in forensic anthropology and forensic medicine sciences. This study is based on this hypothesis and the gender prediction rate was obtained by processing cranium images obtained from computed tomography (CT) using geometric morphometry.Materials and Methods:CT images of 200 individuals between the ages of 25 and 65 were used in our study. The images were opened at the personal workstation Horos Medical Image Viewer (Version 3.0, USA) program and processed with 3D Curved Multiplanar Reconstruction (MPR). The line passing through the nasion and inion points of the images obtained as a result of the process was determined, and all images were brought to the orthogonal plane. Later, the images were overlapped and saved in JPEG format with 100% magnification. JPEG images saved were converted into TPS format, and 21 homologous landmarks were placed. Generalized Procrustes Analysis (GPA) and Principal Component Analysis (PCA) were applied to thecoordinates of landmarks, and shape variations and dimensionality were corrected by gathering the images to the center of gravity. Next, Linear Discriminant Analysis (LDA) was applied to the coordinates, the dimensionality of which was corrected. Results:The study found that 74.465% of the coordinates of 21 homologous landmarks gathered to the center of gravity could be explained with the first three PCs. As a result of the LDA applied to these coordinates, a gender prediction rate of 86.5% was obtained.In addition, a slight difference was found between the GPA sum of squares and the tangent sum of squares (0.57). Conclusion:The images of the cranium obtained from CT showed a high dimorphism by geometric morphometry analysisÖğe Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth(Int Assoc Law & Forensic Sciences, 2023) Senol, Deniz; Secgin, Yusuf; Duman, Burak Suayip; Toy, Seyma; Oner, ZulalBackgroundThe aim of this study is to obtain a highly accurate and objective sex and age estimation by using the parameters of maxillary molar and canine teeth obtained from cone beam computed tomography images in the input of machine learning algorithms. Cone beam computed tomography images of 240 people aged between 25 and 54 were randomly selected from the archive systems of the hospital and transferred to Horos Medikal. 3D curved multiplanar reconstruction was applied to these images and a 3D image was obtained. The resulting image was brought to the orthogonal plane and the measurements were made by superimposing them.ResultsThe results were grouped in four different age groups (25-30, 31-36, 37-49, 50-54) and recorded. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation with ADA Boost Classifier algorithm, while in age estimation, the highest accuracy rate was found as 0.84 between 25-30 and 31-36 age groups with random forest algorithm, as 0.74 between 25-30 and 37-49 age groups with random forest and ADA Boost Classifier algorithms and as 0.85 between 25-30 and 50-54 age groups with random forest algorithm.ConclusionsOur study differs from other studies in two aspects; the first is the selection of a sensitive method such as cone beam computed tomography, and the second is the selection of machine learning algorithms. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation and as 0.85 in age estimation with parameters of maxillary canine and molar teeth.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, Seyma; Secgin, Yusuf; Öner, Zülal; Turan, Muhammed Kamil; Oner, Serkan; Senol, 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.
