Kemal Üreten1,2, Tayfun Arslan3, Korcan Emre Gültekin3, Ayşe Nur Demirgöz Demir4, Hafsa Feyza Özer5, Yasemin Bilgili6. 1. Department of Rheumatology, Faculty of Medicine, Kırıkkale University, Kırıkkale, Turkey. kemalureten@yahoo.com. 2. Computer Engineering Department, MSc, Çankaya University, Ankara, Turkey. kemalureten@yahoo.com. 3. Department of Internal Medicine, Faculty of Medicine, Kırıkkale University, Kırıkkale, Turkey. 4. Department of Physical Therapy and Rehabilitation, Afyonkarahisar City Hospital, Afyonkarahisar, Turkey. 5. Department of Physical Therapy and Rehabilitation, Bartın City Hospital, Bartın, Turkey. 6. Department of Radiology, Faculty of Medicine, Kırıkkale University, Kırıkkale, Turkey.
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.
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.
Entities:
Keywords:
Convolutional neural networks; Deep learning; Hip osteoarthritis; Transfer learning; VGG-16 network
Authors: R K Gebre; J Hirvasniemi; R A van der Heijden; I Lantto; S Saarakkala; J Leppilahti; T Jämsä Journal: Osteoporos Int Date: 2021-09-02 Impact factor: 5.071