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  1. Article ; Online: Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods.

    Üreten, Kemal / Maraş, Hadi Hakan

    Journal of digital imaging

    2022  Volume 35, Issue 2, Page(s) 193–199

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Arthritis, Rheumatoid/diagnostic imaging ; Deep Learning ; Humans ; Neural Networks, Computer ; Osteoarthritis/diagnostic imaging ; ROC Curve ; Retrospective Studies
    Language English
    Publishing date 2022-01-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1033897-4
    ISSN 1618-727X ; 0897-1889
    ISSN (online) 1618-727X
    ISSN 0897-1889
    DOI 10.1007/s10278-021-00564-w
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    Zs.A 2941: Show issues Location:
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  2. Article ; Online: The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.

    Atalar, Ebru / Üreten, Kemal / Kanatlı, Ulunay / Çiçeklidağ, Murat / Kaya, İbrahim / Vural, Abdurrahman / Maraş, Yüksel

    Joint diseases and related surgery

    2023  Volume 34, Issue 2, Page(s) 298–304

    Abstract: Objectives: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs.: Materials and ... ...

    Abstract Objectives: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs.
    Materials and methods: Between January 2010 and December 2020, pelvic radiographs of a total of 516 patients (270 males, 246 females; mean age: 39.1±3.8 years; range, 20 to 78 years) with hip pain were retrospectively analyzed. Based on inclusion and exclusion criteria, a total of 888 hip radiographs (308 diagnosed with FAI and 508 considered normal) were evaluated using deep learning methods. Pre-trained VGG-16, ResNet-101, MobileNetV2, and Inceptionv3 models were used for transfer learning.
    Results: As assessed by performance measures such as accuracy, sensitivity, specificity, precision, F-1 score, and area under the curve (AUC), the VGG-16 model outperformed other pre-trained networks in diagnosing FAI. With the pre-trained VGG-16 model, the results showed 86.6% accuracy, 82.5% sensitivity, 89.6% specificity, 85.5% precision, 83.9% F1 score, and 0.92 AUC.
    Conclusion: In patients with suspected FAI, pelvic radiography is the first imaging method to be applied, and deep learning methods can help in the diagnosis of this syndrome.
    MeSH term(s) Male ; Female ; Humans ; Adult ; Femoracetabular Impingement/diagnostic imaging ; Retrospective Studies ; Deep Learning ; Radiography ; Pelvis
    Language English
    Publishing date 2023-04-26
    Publishing country Turkey
    Document type Journal Article
    ISSN 2687-4792
    ISSN (online) 2687-4792
    DOI 10.52312/jdrs.2023.996
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  3. Article ; Online: Deep Learning Methods in the Diagnosis of Sacroiliitis from Plain Pelvic Radiographs.

    Üreten, Kemal / Maraş, Yüksel / Duran, Semra / Gök, Kevser

    Modern rheumatology

    2021  

    Abstract: 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. ... ...

    Abstract 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 (MRI).
    Language English
    Publishing date 2021-12-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2078157-X
    ISSN 1439-7609 ; 1439-7595
    ISSN (online) 1439-7609
    ISSN 1439-7595
    DOI 10.1093/mr/roab124
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  4. Article ; Online: Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.

    Maraş, Yüksel / Tokdemir, Gül / Üreten, Kemal / Atalar, Ebru / Duran, Semra / Maraş, Hakan

    Joint diseases and related surgery

    2022  Volume 33, Issue 1, Page(s) 93–101

    Abstract: 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 ...

    Abstract 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, sensitivity, specificity, and precision metrics. Pre-trained VGG-16 network outperformed other models in terms of accuracy (93.9%), sensitivity (95.8%), specificity (92.0%), and precision (92.0%) results.
    Conclusion: The results of this study suggest that the deep learning methods are promising support tool in automated control of cervical graphs using the DCNN and the exclusion of normal graphs. Such a supportive tool may reduce the diagnosis time and provide radiologists or clinicians to have more time to interpret abnormal graphs.
    MeSH term(s) Deep Learning ; Humans ; Intervertebral Disc Degeneration/diagnostic imaging ; Lordosis ; Radiography ; Retrospective Studies
    Language English
    Publishing date 2022-03-28
    Publishing country Turkey
    Document type Journal Article
    ISSN 2687-4792
    ISSN (online) 2687-4792
    DOI 10.52312/jdrs.2022.445
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  5. Article ; Online: Use of deep learning methods for hand fracture detection from plain hand radiographs.

    Üreten, Kemal / Sevinç, Hüseyin Fatih / İğdeli, Ufuk / Onay, Aslıhan / Maraş, Yüksel

    Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES

    2022  Volume 28, Issue 2, Page(s) 196–201

    Abstract: 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 ... ...

    Title translation Düz el radyografilerinden el kırıklarının tespiti için derin öğrenme yöntemlerinin kullanılması.
    Abstract 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.
    MeSH term(s) Deep Learning ; Fractures, Bone/diagnostic imaging ; Hand/diagnostic imaging ; Humans ; Neural Networks, Computer ; Radiography
    Language English
    Publishing date 2022-01-28
    Publishing country Turkey
    Document type Journal Article
    ZDB-ID 2253739-9
    ISSN 1307-7945 ; 1306-696X
    ISSN (online) 1307-7945
    ISSN 1306-696X
    DOI 10.14744/tjtes.2020.06944
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    Zs.A 6718: Show issues Location:
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  6. Article ; Online: The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods.

    Atalar, Hakan / Ureten, Kemal / Tokdemir, Gul / Tolunay, Tolga / Çiçeklidağ, Murat / Atik, Osman Şahap

    Journal of pediatric orthopedics

    2022  

    Abstract: Background: Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making ... ...

    Abstract Background: Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making support to clinicians and improve the accuracy and efficiency of various diagnostic and treatment processes. This has encouraged new research and development efforts in computer-aided diagnosis. The aim of this study was to evaluate hip sonograms using computer-assisted deep-learning methods.
    Methods: The study included 376 sonograms evaluated as normal according to the Graf method, 541 images with dysplasia and 365 images with incorrect probe position. To classify the developmental hip dysplasia ultrasound images, transfer learning was applied with pretrained VGG-16, ResNet-101, MobileNetV2 and GoogLeNet networks. The performances of the networks were evaluated with the performance parameters of accuracy, sensitivity, specificity, precision, F1 score, and AUC (area under the ROC curve).
    Results: The accuracy, sensitivity, specificity, precision, F1 score, and AUC results obtained by testing the VGG-16, ResNet-101, MobileNetV2, and GoogLeNet models showed performance >80%. With the pretrained VGG-19 model, 93%, 93.5%, 96.7%, 92.3%, 92.6%, and 0.99 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained, respectively.
    Conclusion: In this study, in addition to the ultrasonography images of dysplastic and healthy hips, images were also included of probe malpositioning, and these images were able to be successfully evaluated with deep learning methods. On the sonograms, which provided criteria appropriate for evaluation, successful differentiation could be made of healthy hips and dysplastic hips.
    Level of evidence: Level-IV; diagnostic studies.
    Language English
    Publishing date 2022-11-04
    Publishing country United States
    Document type Journal Article
    ZDB-ID 604642-3
    ISSN 1539-2570 ; 0271-6798
    ISSN (online) 1539-2570
    ISSN 0271-6798
    DOI 10.1097/BPO.0000000000002294
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    Zs.A 1681: Show issues Location:
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  7. Article ; Online: Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network.

    Üreten, Kemal / Erbay, Hasan / Maraş, Hadi Hakan

    Clinical rheumatology

    2019  Volume 39, Issue 4, Page(s) 969–974

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Arthritis, Rheumatoid/diagnostic imaging ; Hand/diagnostic imaging ; Humans ; Neural Networks, Computer ; Radiography ; Sensitivity and Specificity
    Language English
    Publishing date 2019-03-08
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 604755-5
    ISSN 1434-9949 ; 0770-3198
    ISSN (online) 1434-9949
    ISSN 0770-3198
    DOI 10.1007/s10067-019-04487-4
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    Zs.A 1740: Show issues Location:
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  8. Article ; Online: Plasma thrombin-activatable fibrinolysis inhibitor (TAFI) antigen levels in acromegaly patients in remission

    Erdoğan, Mehmet / Özbek, Mustafa / Akbal, Erdem / Üreten, Kemal

    Turkish journal of medical sciences

    2019  Volume 49, Issue 5, Page(s) 1381–1385

    Abstract: Background/aim: Acromegaly is associated with increased morbidity andmortality, mostly due to cardiovascular complications.Plasma thrombin-activatable fibrinolysis inhibitor (TAFI) antigen levels are associated with coagulation/fibrinolysis and ... ...

    Abstract Background/aim: Acromegaly is associated with increased morbidity andmortality, mostly due to cardiovascular complications.Plasma thrombin-activatable fibrinolysis inhibitor (TAFI) antigen levels are 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.
    MeSH term(s) Acromegaly/blood ; Acromegaly/immunology ; Adult ; Antigens/blood ; Blood Glucose/analysis ; Carboxypeptidase B2/blood ; Carboxypeptidase B2/immunology ; Case-Control Studies ; Cholesterol/blood ; Enzyme-Linked Immunosorbent Assay ; Female ; Homocysteine/blood ; Humans ; Lipoproteins, LDL/blood ; Male ; Prolactin/blood ; Thyrotropin/blood ; Triglycerides/blood
    Chemical Substances Antigens ; Blood Glucose ; Lipoproteins, LDL ; Triglycerides ; Homocysteine (0LVT1QZ0BA) ; Prolactin (9002-62-4) ; Thyrotropin (9002-71-5) ; Cholesterol (97C5T2UQ7J) ; CPB2 protein, human (EC 3.4.17.20) ; Carboxypeptidase B2 (EC 3.4.17.20)
    Language English
    Publishing date 2019-10-24
    Publishing country Turkey
    Document type Journal Article
    ZDB-ID 1183461-4
    ISSN 1303-6165 ; 1300-0144
    ISSN (online) 1303-6165
    ISSN 1300-0144
    DOI 10.3906/sag-1812-231
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    Zs.A 3242: Show issues Location:
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  9. Article: Osteopoikilosis in a patient with rheumatoid arthritis complicated with dry eyes.

    Ureten, Kemal

    Rheumatology international

    2007  Volume 27, Issue 11, Page(s) 1079–1082

    Abstract: Osteopoikilosis is an uncommon sclerosing bone dysplasia of unknown etiology. It is usually detected as a coincidental finding at radiographic examination. Mild joint pain and swelling may be seen in 15-20% of cases. Osteopoikilosis is rarely associated ... ...

    Abstract Osteopoikilosis is an uncommon sclerosing bone dysplasia of unknown etiology. It is usually detected as a coincidental finding at radiographic examination. Mild joint pain and swelling may be seen in 15-20% of cases. Osteopoikilosis is rarely associated with rheumatoid arthritis. In this case report a young man with osteopoikilosis who was diagnosed as having rheumatoid arthritis complicated with dry eyes is presented. Although patients with osteopoikilosis may have articular symptoms, those patients should be carefully examined for a possible association with a rheumatic condition.
    MeSH term(s) Adult ; Arthritis, Rheumatoid/complications ; Dry Eye Syndromes/complications ; Humans ; Magnetic Resonance Imaging ; Male ; Osteopoikilosis/complications ; Osteopoikilosis/diagnostic imaging ; Radiography ; Radionuclide Imaging
    Language English
    Publishing date 2007-09
    Publishing country Germany
    Document type Case Reports ; Journal Article
    ZDB-ID 8286-7
    ISSN 1437-160X ; 0172-8172
    ISSN (online) 1437-160X
    ISSN 0172-8172
    DOI 10.1007/s00296-007-0334-5
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  10. Article ; Online: Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

    Üreten, Kemal / Arslan, Tayfun / Gültekin, Korcan Emre / Demir, Ayşe Nur Demirgöz / Özer, Hafsa Feyza / Bilgili, Yasemin

    Skeletal radiology

    2020  Volume 49, Issue 9, Page(s) 1369–1374

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Deep Learning ; Humans ; Neural Networks, Computer ; Osteoarthritis, Hip/diagnostic imaging ; Radiography ; Retrospective Studies
    Language English
    Publishing date 2020-04-04
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 527592-1
    ISSN 1432-2161 ; 0364-2348
    ISSN (online) 1432-2161
    ISSN 0364-2348
    DOI 10.1007/s00256-020-03433-9
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