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Öğe Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus(Wolters Kluwer Medknow Publications, 2024) Ciftci, R.; Secgin, Y.; Oner, Z.; Toy, S.; Oner, S.Background:Determination of bone age is a critical issue for forensics, surgery, and basic sciences.Aim:This study aims to estimate age with high accuracy and precision using Machine Learning (ML) algorithms with parameters obtained from calcaneus x-ray images of healthy individuals.Method:The study was carried out by retrospectively examining the foot X-ray images of 341 people aged 18-65 years. Maximum width of the calcaneus (MW), body width (BW), maximum length (MAXL), minimum length (MINL), facies articularis cuboidea height (FACH), maximum height (MAXH), and tuber calcanei width (TKW) parameters were measured from the images. The measurements were then grouped as 20-45 years of age, 46-64 years of age, 65 and older, and age estimation was made by using these at the input of ML models.Results:As a result of the ML input of the measurements obtained, a 0.85 Accuracy (Acc) rate was obtained with the Extra Tree Classifier algorithm. The accuracy rate of other algorithms was found to vary between 0.78 and 0.82. The contribution of parameters to the overall result was evaluated by using the shapley additive explanations (SHAP) analyzer of Random Forest algorithm and the MAXH parameter was found to have the highest contribution in age estimation.Conclusions:As a result of our study, calcaneus bone was found to have high accuracy and precision in age estimations.Öğe CSF flow patterns in the brain in patients with neuro-Behcet disease and Behcet disease(Verduci Publisher, 2017) Unlu, S.; Dogan, M.; Kapicioglu, Y.; Kamisli, S.; Oner, S.; Yildirim, I. O.; Ozturk, M.OBJECTIVE: In the etiopathogenesis of Behcet disease (BD) and Neuro-Behcet disease (NBD), vascular eclipse occurs in both the arteries and veins. The disease affects all vascular structures. The present study evaluates the use of Phase Contrast (PC) Cerebral Spinal Fluid (CSF) Flow Magnetic Resonance Imaging (MRI), a non-invasive technique for measuring CSF dynamics, for determining the level of aqueducts that are influenced in BD and NBD. PATIENTS AND METHODS: The quantitative evaluation of CSF flow in BD and NBD was performed using images obtained at the level of the cerebral aqueduct on the semi-axial plane. The PC-MRI angiography technique was used. RESULTS: There is no distinctive difference between BD and NBD that can be distinguished by the aqueduct diameters of both conditions. A clear increase in aqueduct diameter occurred BD and NBD group when compared to the control group. While there were no differences found between the BD group and the control group regarding peak velocity, average velocity, forward flow, reverse flow, net forward flow, and flow, there were distinctive increases in these various factors in the NBD group. CONCLUSIONS: Using the non-invasive PC-MRI technique, this study found that in BD and NBD patients, changes occurred in CSF flow figures. Increases in CSF parameters were also observed in NBD patients, a finding which may be helpful for future distinction between BD and NBD during diagnosis.Öğe Paranasal sinus anatomical differences in elderly patients(Geriatrics Society, 2020) Yilmaz, N.; Mülazimoglu, S.; Oner, S.; Nacar, E.; Yilmaz, O.Introduction: Endonasal endoscopic sinus surgeries performed on elderly patients can be challenging due to anatomical variations, and can be studied using preoperative computed tomography. The aim of the present study was to evaluate paranasal sinus anatomical differences in elderly patients compared to a younger control group. Materials and Methods: We retrospectively evaluated paranasal computed tomography scans of 47 elderly patients (>65 years old) (Elderly group) and 47 younger patients (Control group) for midfacial skeletal size (interzygomatic buttress distance, nasion-basion distance), anatomical variations, dimensions, and paranasal sinus volumes. Results: The mean age of the Elderly group was 69.89 years (65-81 years) and the mean age of the Control group was 33.15 years (20-49 years). There was no significant difference in midfacial size between the two groups. The prevalence of Keros Type III olfactory fossa was significantly higher in the Elderly group than in the Control group (p<0.05). The Elderly group had a significantly lower mean maxillary sinus volume (p<0.01) and mean anteroposterior diameter of the sphenoid sinus (p<0.01) compared to the Control group. Furthermore, there was no significant difference in the maxillary sinus volume between the elderly edentulous and dentulous patients(p>0.05). Conclusion: Elderly patients have more Keros Type III olfactory fossa, which confers a higher risk of iatrogenic cerebrospinal fluid leakage during endoscopic sinus surgery. The preoperative detailed evaluation of computed tomography scans of elderly patients should include, but not be limited to, the ethmoid roof for deep olfactory fossa, and the sphenoid sinus for its narrow anterioposterior dimention. © 2020, Geriatrics Society. All rights reserved.Öğe 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.Öğe Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms(Via Medica, 2023) Senol, D.; Bodur, F.; Secgin, Y.; Bakici, R. S.; Sahin, N. E.; Toy, S.; Oner, S.Background: The aim of this study is to predict sex with machine learning (ML) algorithms by making morphometric measurements on radiological images of the first and fifth metatarsal and phalanx bones.Materials and methods: In this study, radiologic images of 263 individuals (135 female, 128 male) between the ages of 27 and 60 were analysed retrospectively. The images in digital imaging and communications in medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer programme. Length and width measurements of the first and fifth metatarsal and foot phalanx bones were performed on the transferred images. In addition, the ratios of the total length of the first proximal and distal phalanx and length of the first metatarsal and total length of fifth proximal, middle, and distal phalanx and maximum length of fifth metatarsal were calculated.Results: As a result of machine learning algorithms, highest accuracy, specificity, sensitivity, and Matthews correlation coefficient values were found as 0.85, 0.86, 0.85, and 0.71, respectively with decision tree algorithm. It was found that accu racy rates of other algorithms varied between 0.74 and 0.83. Conclusions: As a result of our study, it was found that sex estimation was made with high accuracy rate by using machine learning algorithms on X-ray images of the first and fifth metatarsal and foot phalanx. We think that in cases when pelvis, cranium and long bones are harmed and examination is difficult, bones of the first and fifth metatarsal and foot phalanx can be used for sex estimation. (Folia Morphol 2023; 82, 3: 704-711)
