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The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods

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dc.contributor.author Atalar, Ebru
dc.contributor.author Üreten, Kemal
dc.contributor.author Çiçeklidağ, Murat
dc.contributor.author Kaya, İbrahim
dc.contributor.author Vural, Abdurrahman
dc.contributor.author Maraş, Yüksel
dc.date.accessioned 2024-01-23T13:35:37Z
dc.date.available 2024-01-23T13:35:37Z
dc.date.issued 2023
dc.identifier.citation Atalar, Ebru;...et.al. (2023). "The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods", Joint Diseases and Related Surgery, Vol.34, No.2, pp.298-304. tr_TR
dc.identifier.issn 26874792
dc.identifier.uri http://hdl.handle.net/20.500.12416/6963
dc.description.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. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.52312/jdrs.2023.996 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Computer-Assisted Image Processing tr_TR
dc.subject Deep Learning tr_TR
dc.subject Femoroacetabular Impingement tr_TR
dc.subject Hip tr_TR
dc.title The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods tr_TR
dc.type article tr_TR
dc.relation.journal Joint Diseases and Related Surgery tr_TR
dc.identifier.volume 34 tr_TR
dc.identifier.issue 2 tr_TR
dc.identifier.startpage 298 tr_TR
dc.identifier.endpage 304 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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