Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods

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dc.contributor.authorUreten, Kemal
dc.contributor.authorArslan, Tayfun
dc.contributor.authorGultekin, Korcan Emre
dc.contributor.authorDemir, Ayse Nur Demirgoz
dc.contributor.authorOzer, Hafsa Feyza
dc.contributor.authorBilgili, Yasemin
dc.date.accessioned2021-01-14T18:10:42Z
dc.date.available2021-01-14T18:10:42Z
dc.date.issued2020
dc.departmentKKÜ
dc.description.abstractObjective 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.en_US
dc.identifier.citationBu makale açık erişimli değildir.en_US
dc.identifier.doi10.1007/s00256-020-03433-9
dc.identifier.endpage1374en_US
dc.identifier.issn0364-2348
dc.identifier.issn1432-2161
dc.identifier.issue9en_US
dc.identifier.pmid32248444
dc.identifier.scopus2-s2.0-85084652939
dc.identifier.scopusqualityQ2
dc.identifier.startpage1369en_US
dc.identifier.urihttps://doi.org/10.1007/s00256-020-03433-9
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12738
dc.identifier.volume49en_US
dc.identifier.wosWOS:000523403800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSPRINGERen_US
dc.relation.ispartofSKELETAL RADIOLOGY
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHip osteoarthritisen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional neural networksen_US
dc.subjectVGG-16 networken_US
dc.subjectTransfer learningen_US
dc.titleDetection of hip osteoarthritis by using plain pelvic radiographs with deep learning methodsen_US
dc.typeArticle

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