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    Atraumatic Osteonecrosis After Estrogen Replacement Therapy Associated with Low Protein S Level in a Patient with Turner Syndrome
    (Sage Publications Inc, 2010) Üreten, Kemal; Öztürk, M. Akif; Bostancı, Ahmet; Çeneli, Özcan; Özbek, Mustafa; Haznedaroğlu, İbrahim C.
    Atraumatic osteonecrosis has been associated with a variety of clinical conditions including corticosteroid usage, alcoholism, infections, hyperbaric events, storage disorders, marrow-infiltrating diseases, coagulation defects, and some autoimmune diseases. Osteonecrosis due to thrombophilia is an extremely rare condition with only few cases reported previously in the literature. Hormone-replacement therapies cause increased risk of venous thrombosis, probably by causing a synergistic effect with inherited clotting defects. In this article, we report a young female with Turner syndrome, who developed avascular necrosis of the femoral head during treatment with oral estrogen, which was associated with low protein S levels.
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    Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods
    (Springer, 2022) Üreten, Kemal; Maraş, Hadi Hakan
    Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.
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    Corneal thickness and endothelial changes in long-term hydroxychloroquine use
    (Taylor & Francis Ltd, 2019) Oğurel, Tevfik; Özer, Murat Atabey; Akbulut, Yaprak; Gökçınar, Nesrin Büyüktortop; Onaran, Zafer; Üreten, Kemal
    Objective: To determine possible associations between long-term HCQ use and corneal changes in patients who used HCQ for at least 3 years. Materials and methods: The study included 62 healthy controls and 62 consecutive patients who used HCQ for the treatment of rheumatologic disease and were referred to the ophthalmology department between August 2018 and November 2018 for HCQ retinal toxicity screening. Central corneal thickness (CCT), corneal endothelial cell density (ECD), the coefficient of variation (CV) of cell size, and the percentage of hexagonal cells (HEX%) were measured to evaluate changes in the cornea. Results: The mean age of the patient group and control group was 50.10 +/- 10.91 and 50.53 +/- 10.67 years, respectively. The mean ECD was 2742 +/- 347 (cells/mm(2)) in the patient group and 2875 +/- 188 cells/mm(2) in the control group. There was a significant difference between groups (p = 0.01). The mean CCT was 567.05 +/- 32.35 mu m in the patient group and 540.15 +/- 38.50 mu m in the control group. CCT was significantly higher in the patient group compared with control group (p < 0.001). There was no significant difference between groups in terms of mean CV and HEX values (p > 0.05). Conclusions: Patients using long-term HCQ demonstrated lower ECD and higher CCT than the control group. However, the CV of cell sizes and the HEX % values were not significantly different from the controls.
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    Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods
    (Turkish Joint Diseases Foundation, 2022) Maraş, Yüksel; Tokdemir, Gül; Üreten, Kemal; Atalar, Ebru; Duran, Semra; Maraş, Hakan
    Objectives: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results: We compared the performances of the classification models in terms of performance metrics such as accuracy,
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    Use of deep learning methods for hand fracture detection from plain hand radiographs
    (Turkish Assoc Trauma Emergency Surgery, 2022) Üreten, Kemal; Sevinç, Hüseyin Fatih; İğdeli, Ufuk; Onay, Aslıhan; Maraş, Yüksel
    BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physicians in the diagnosis of hand fractures using deep learning methods. METHODS: In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks. RESULTS: The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7% , respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet. CONCLUSION: We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.