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

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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.

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Authors and Affiliations

  1. Department of Rheumatology, Faculty of Medicine, Kırıkkale University, Kırıkkale, Turkey

    Kemal Üreten

  2. Computer Engineering Department, MSc, Çankaya University, Ankara, Turkey

    Kemal Üreten

  3. Department of Internal Medicine, Faculty of Medicine, Kırıkkale University, Kırıkkale, Turkey

    Tayfun Arslan & Korcan Emre Gültekin

  4. Department of Physical Therapy and Rehabilitation, Afyonkarahisar City Hospital, Afyonkarahisar, Turkey

    Ayşe Nur Demirgöz Demir

  5. Department of Physical Therapy and Rehabilitation, Bartın City Hospital, Bartın, Turkey

    Hafsa Feyza Özer

  6. Department of Radiology, Faculty of Medicine, Kırıkkale University, Kırıkkale, Turkey

    Yasemin Bilgili

Authors
  1. Kemal Üreten
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  2. Tayfun Arslan
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  3. Korcan Emre Gültekin
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  4. Ayşe Nur Demirgöz Demir
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  5. Hafsa Feyza Özer
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  6. Yasemin Bilgili
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Corresponding author

Correspondence to Kemal Üreten.

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Conflict of interest

The authors declare that they have no conflicting interests.

Ethics approval

Ethical approval certificate was obtained from the Non-interventional Clinical Researches Ethics Board in Kırıkkale University. Certificate date: Aug 7th, 2019 Certificate no: 2019.07.09.

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Üreten, K., Arslan, T., Gültekin, K.E. et al. Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods. Skeletal Radiol 49, 1369–1374 (2020). https://doi.org/10.1007/s00256-020-03433-9

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  • DOI: https://doi.org/10.1007/s00256-020-03433-9

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