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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 Cavitating Mesenteric Lymph Node Syndrome in a Patient with Celiac Disease: Differential Diagnosis Based on Radiological Findings(Pera Yayincilik Hizmetleri, 2024) Oner, Serkan; Kiran, Hazal; Harman, MustafaMesenteric lymph node syndrome is a rare condition characterized by cystic or cavitating changes in mesenteric lymph nodes. It is commonly associated with celiac disease. We present a case of a 59-year-old woman with known celiac disease who presented with abdominal pain, abdominal swelling, and weight loss, ultimately diagnosed with mesenteric lymph node syndrome. This case highlights the importance of radiological findings when considering rare complications of celiac disease in the differential diagnosis.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 Estimation of Gender by Costochondral Calcification Model Obtained from Computed Tomography Image(2022) Al-Samanee, Albaraa Rıyad Mohammmed; Oner, Zulal; Oner, SerkanBackground: Gender estimation plays a key role in human identification. Between the various meas-urement methods of gender estimation from skeletal remains, the use of the calcification patterns of costal cartilages is highly suggested especially when the skull and pelvic bones are not available. The purpose of this study is to determine the patterns of costal cartilage calcifications in the Turkish popula-tion and to predict gender accordingly.Materials and Methods: Our study was performed by using the Computed Tomography (CT) images of 200 individuals (100 female, 100 male) in the 20-60 age group who applied to Karabük University Train-ing and Research Hospital and had no costal pathology or surgery history. The classification of Rejta-rova et al. (2004) was used for the patterns of costal cartilage calcifications, and it was calculated the number and percentage of each pattern in male and female to estimate the gender.Results: The results showed 193 (96.5%) individuals with calcification in the costal cartilages and 7 (3.5%) individuals without calcification in their costal cartilages, which 3 females and 4 males. Peripher-al pattern (Type I) showed 100% male gender prediction, while central pattern (Type II) showed female gender prediction with 92.3%. Type III was the most common pattern with 66.8% in the Turkish popula-tion.Conclusions: As a result of this study, costal cartilage calcification models were obtained in the Turkish population using the method of Rejtarova et al (2004). Type I and Type II patterns showed high accuracy in terms of the usability of these models in predicting gender.Öğe Estimation of sex from femoral bone using radiological imaging methods in Turkish population(Schweizerbart Science Publishers, 2024) Bakici, Rukiye Sumeyye; Ocal, Zeynep Ayvat; Meral, Orhan; Oner, Zulal; Oner, SerkanSex estimation is leading to determine the biological profile in forensic medicine. The aim of this study is to research the effectiveness of logistic regression (LogR) and discriminant function analysis (DFA) to create sex estimation models on femur images obtained with Computed Tomography (CT) angiography and to address the differences of femur, which show sexual dimorphism, among populations. All parameters were measured on three planes by adjusting the 300 CT angiography images from 150 women and 150 men that focused on the left femur to the orthogonal plane with standard magnification. The subgroup, which included 30 images randomly generated from these images, was measured twice with an interval of 3 weeks by the first radiologist and once by the second radiologist. According to the Fisher’s Linear Discriminant analysis, which was evaluated with ten parameters in the study, it was concluded that the power of discriminating women was 96.7%, the power of discriminating men was 98.7%, and the total discrimination power was 97.7%; these results were 98%, 99.3%, and 98.7%, respectively according to LogR. In this study, DFA and LogR analysis showed that femur provided a very good rate of sexual dimorphism. A database belonging to the Turkish population was created for the femur, allowing for comparison between populations. © 2024 E. Schweizerbart’sche Verlagsbuchhandlung, 70176 Stuttgart, Germany.Öğe Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms(Wolters Kluwer Medknow Publications, 2022) Secgin, Yusuf; Oner, Zulal; Turan, Muhammed Kamil; Oner, SerkanIntroduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.Öğe An MRI Analysis of the Lumbar Lordosis Angle and Lumbar Muscle Thicknesses in Patients with Non-Specific Low Back Pain(Duzce Univ, Fac Medicine, 2023) Dagli, Ali Cihan; Oner, Serkan; Oner, Zulal; Dagli, Beyza YazganObjective: This study aimed to examine the relationship of lumbar lordosis angle and lumbar muscle thickness with non-specific low back pain (LBP) through magnetic resonance imaging (MRI) images.Methods: The study included 96 individuals (43 men/53 women) aged between 18-65 with non-specific LBP that is not explained by disc pathology based on MRI, who applied to affiliated Training and Research Hospital with the complaint of LBP between March-June 2019. Sociodemographic information was recorded using an LBP assessment form. The Oswestry LBP Disability Questionnaire was used for LBP disability. The thicknesses of muscle (m.) psoas major, m. multifidus, m. quadratus lumborum and m. erector spinae were measured corresponding to the L3-L4 vertebral level by using Radiant DICOM viewer program. The Cobb Angle method was used for lumbar lordosis angle determination. Measurements were made in three repetitions using the Radiant DICOM viewer program. Results: The results showed that an inverse relationship was found between the Oswestry Disability Index (ODI) and m. psoas major thickness (p<0.05). Given the comparison of right -left side muscle thicknesses, left side muscles were thicker (p<0.05). There were no significant differences observed between males and females in terms of lumbar lordosis angle (LLA). However, in terms of muscle thickness, males exhibited higher values, except for the transverse measurements of the right quadratus lumborum and left erector spinae muscles, which showed no significant differences (p < 0.05). Furthermore, a positive correlation was found between LLA and the transverse thickness of the left psoas major muscle (p = 0.034) and the anterior-posterior thickness of the bilateral erector spinae muscles (p < 0.001).Conclusions: In regard to inverse relationship between m. psoas major thickness and ODI, m. psoas major should be taken into consideration to alleviate the disability caused by LBP. Additionally, the difference on both sides is likely one of the causes of muscle imbalance, and this might be one of the reasons for LBP, thereby causing disability in daily tasks due to LBP.Öğe Sex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Model(Soc Chilena Anatomia, 2024) Bakici, Rukiye Sumeyye; Cakmak, Muhammet; Oner, Zulal; Oner, SerkanThe 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.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.Öğe A study on the correlation between spleen volume estimated via cavalieri principle on computed tomography images with basic hemogram and biochemical blood parameters(Medrang, 2022) Sahin, Necati Emre; Öner, Zülal; Oner, Serkan; Turan, Muhammed KamilConsidering its hematological and immunological functions, spleen is a very important organ. A change occurs in its size as the spleen performs these functions. This study aims to examine the possible relationships between spleen volume and the basic hemogram and biochemical parameters in serum. Multidetector computed tomography images and basic hemogram and biochemical parameters of 74 adult individuals, 34 male and 40 female, who were found to be healthy, were used in the study. Spleen volume was estimated using the Cavalieri method on multidetector computed tomography images and the correlations between the volume value with basic hemogram and biochemistry parameters were researched. While negative significant correlations were found between the estimated spleen volume and lymphocyte percentage (r=-0.224) and platelet level (r=-0.271); positive significant correlations were found between hemoglobin level (r=0.228), hematocrit level (r=0.237), alanine aminotransferase (r=0.345), and erythrocyte level (r=0.375). As a result of this study, a relationship was found between spleen volume and lymphocyte percentage, hematocrit level, erythrocyte level, platelet level, and alanine aminotransferase level in serum. We believe that the results of the study will provide a larger perspective to clinicians in the diagnosis of diseases associated with spleen.
