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    Deep learning methods in the diagnosis of sacroiliitis from plain pelvic radiographs
    (Oxford Univ Press, 2023) Ureten, Kemal; Maras, Yuksel; Duran, Semra; Gok, Kevser
    Objectives The aim of this study is to develop a computer-aided diagnosis method to assist physicians in evaluating sacroiliac radiographs. Methods Convolutional neural networks, a deep learning method, were used in this retrospective study. Transfer learning was implemented with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. Normal pelvic radiographs (n = 290) and pelvic radiographs with sacroiliitis (n = 295) were used for the training of networks. Results The training results were evaluated with the criteria of accuracy, sensitivity, specificity and precision calculated from the confusion matrix and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. Pre-trained VGG-16 model revealed accuracy, sensitivity, specificity, precision and AUC figures of 89.9%, 90.9%, 88.9%, 88.9% and 0.96 with test images, respectively. These results were 84.3%, 91.9%, 78.8%, 75.6 and 0.92 with pre-trained ResNet-101, and 82.0%, 79.6%, 85.0%, 86.7% and 0.90 with pre-trained inception-v3, respectively. Conclusions Successful results were obtained with all three models in this study where transfer learning was applied with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. This method can assist clinicians in the diagnosis of sacroiliitis, provide them with a second objective interpretation and also reduce the need for advanced imaging methods such as magnetic resonance imaging.
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    Detection of hand osteoarthritis from hand radiographs using convolutional neural networks with transfer learning
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2020) Ureten, Kemal; Erbay, Hasan; Maras, Hadi Hakan
    Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an original set of 420 and were validated with the remaining 88. Multilayer convolutional neural network models were designed based on a transfer learning method using pretrained AlexNet, GoogLeNet, and VGG-19 networks. The accuracies of the models were 93.2% for AlexNet, 94.3% for GoogLeNet, and 96.6% for VGG-19. The sensitivities of these models were 0.9167 for AlexNet, 0.9184 for GoogLeNet, and 0.9574 for VGG-19, while the specificity values were 0.9500, 0.9744, and 0.9756, respectively. The performance metrics, including accuracy, sensitivity, specificity, and precision, of our newly developed automated diagnosis methods are promising in the diagnosis of hand osteoarthritis. Our computer-aided detection systems may help physicians in interpreting hand radiography images, diagnosing osteoarthritis, and saving time.
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    Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods
    (SPRINGER, 2020) Ureten, Kemal; Arslan, Tayfun; Gultekin, Korcan Emre; Demir, Ayse Nur Demirgoz; Ozer, Hafsa Feyza; Bilgili, Yasemin
    Objective The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging method in the diagnosis of hip osteoarthritis. In this study, we aimed to develop a computer-aided diagnosis method that will help physicians for the diagnosis of hip osteoarthritis by interpreting plain pelvic radiographs. Materials and methods In this retrospective study, convolutional neural networks were used and transfer learning was applied with the pre-trained VGG-16 network. Our dataset consisted of 221 normal hip radiographs and 213 hip radiographs with osteoarthritis. In this study, the training of the network was performed using a total of 426 hip osteoarthritis images and a total of 442 normal pelvic images obtained by flipping the raw data set. Results Training results were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated by using the confusion matrix. We achieved accuracy, sensitivity, specificity and precision results at 90.2%, 97.6%, 83.0%, and 84.7% respectively. Conclusion We achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.
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    Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network
    (SPRINGER LONDON LTD, 2020) Ureten, Kemal; Erbay, Hasan; Maras, Hadi Hakan
    Introduction Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis. Methods A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA. Results The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500. Conclusion Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.
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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) Maras, Yuksel; Tokdemir, Gul; Ureten, Kemal; Atalar, Ebru; Duran, Semra; Maras, 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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    Plasma thrombin-activatable fibrinolysis inhibitor (TAFI) antigen levels in acromegaly patients in remission
    (Tubitak Scientific & Technical Research Council Turkey, 2019) Erdogan, Mehmet; Ozbek, Mustafa; Akbal, Erdem; Ureten, Kemal
    Background/aim: Acromegaly is associated with increased morbidity and mortality, mostly due to cardiovascular complications. Plasma thrombin-activatablc fibrinolysis inhibitor (TAFI) antigen levels arc associated with coagulation/fibrinolysis and inflammation. Plasma TAFI may play a role in arterial thrombosis in cardiovascular diseases. In this study, it was aimed to evaluate the thrombin-activatable fibrinolysis inhibitor (TAFI) antigen and homocysteine levels in patients with acromegaly and healthy control subjects. Materials and methods: Plasma TAFI antigen and homocysteine levels in 29 consecutive patients with acromegaly and 26 age-matched healthy control subjects were measured. All patients included in the study were in remission. The TAFIa/ai antigen in the plasma samples was measured using a commercially available ELISA kit. Results: Routine biochemical parameters, fasting blood glucose, prolactin, thyroid stimulating hormone, total-cholesterol, low density lipoprotein cholesterol, triglyceride, and homocysteine levels were similar in the 2 groups (P > 0.05), whereas the plasma TAFI antigen levels were significantly elevated in the acromegalic patients (154.7 +/- 94.0%) when compared with the control subjects (107.2 +/- 61.6%) (P = 0.033). No significant correlation was identified by Pearson's correlation test between the plasma TAFI antigen and homocysteine levels (r = 0.320, P = 0.250). Conclusion: A significant alteration in the plasma TAFI antigen levels was detected in acromegaly. Increased plasma TAFI antigen levels might aggravate prothrombotic and thrombotic events in patients with acromegaly.
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    Relationship of paraoxonase-1, malondialdehyde and mean platelet volume with markers of atherosclerosis in familial Mediterranean fever: an observational study
    (Turkish Soc Cardiology, 2013) Ariturk, Ozlem Karakurt; Ureten, Kemal; Sari, Munevver; Yazihan, Nuray; Ermis, Ezgi; Erguder, Imge
    Objective: There are many studies demonstrating deteriorated ventricle and endothelium functions in familial Mediterranean fever (FMF) patients. As FMF is an autoinflammatory disease with an ongoing inflammatory activity and inflammation plays an important role in the development and progression of atherosclerosis in some of the rheumatic diseases, we aimed to investigate the early markers of atherosclerosis in patients with FMF by the measurements of serum paraoxonase-1 (PON-1) activity mean platelet volume (MPV) and malondialdehyde (MDA) level. Methods: This study is a cross-sectional, observational study. Forty consecutive patients with FMF and twenty healthy volunteers were selected to form the study population. The diagnosis of FMF was based on Tel-Hashomer criteria. Serum PON-1 activity, MPV and MDA level were determined to examine their association with FMF. Student's t-test, Mann-Whitney U test, Pearson correlation analysis were used for statistical analysis. Results: The mean PON-1 activity in FMF patients was significantly lower than in the healthy population (141.46 +/- 38.29 vs. 179.62 +/- 10.73 U/I, p<0.01). Serum MDA levels were the same between the groups (1.08 +/- 0.66 vs. 1.08 +/- 0.33 nmol/mL, p=0.99). MPV was higher in FMF patients than in the control I group (8.87 +/- 0.99 vs. 8.22 +/- 0.45 fl, p=0.04). PUN, MPV and MDA levels were the same in FMF patients with acute attack and attack -free period. Conclusion: Our results show that PON-1 activity is lower in patients with FMF. Reduced PON-1 activity and increased MPV, independent of the oxidative stress status of these patients, may lead to increased atherosclerotic propensity in FMF.
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    Use of deep learning methods for hand fracture detection from plain hand radiographs
    (Turkish Assoc Trauma Emergency Surgery, 2022) Ureten, Kemal; Sevinc, Huseyin Fatih; Igdeli, Ufuk; Onay, Aslihan; Maras, Yuksel
    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.