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Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network

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Abstract

Objective

To compare performances in diagnosing intertrochanteric hip fractures from proximal femoral radiographs between a convolutional neural network and orthopedic surgeons.

Materials and methods

In total, 1773 patients were enrolled in this study. Hip plain radiographs from these patients were cropped to display only proximal fractured and non-fractured femurs. Images showing pseudarthrosis after femoral neck fracture and those showing artificial objects were excluded. This yielded a total of 3346 hip images (1773 fractured and 1573 non-fractured hip images) that were used to compare performances between the convolutional neural network and five orthopedic surgeons.

Results

The convolutional neural network and orthopedic surgeons had accuracies of 95.5% (95% CI = 93.1–97.6) and 92.2% (95% CI = 89.2–94.9), sensitivities of 93.9% (95% CI = 90.1–97.1) and 88.3% (95% CI = 83.3–92.8), and specificities of 97.4% (95% CI = 94.5–99.4) and 96.8% (95% CI = 95.1–98.4), respectively.

Conclusions

The performance of the convolutional neural network exceeded that of orthopedic surgeons in detecting intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. The convolutional neural network has a significant potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.

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Acknowledgements

The authors express their appreciation to Kamimura K, Fujita Y, and Wakui J for reviewing images and would like to thank Editage (www.editage.jp) for English language editing.

Funding

The study was conducted without any external funding or grant.

Author information

Authors and Affiliations

  1. Department of Orthopedic Surgery, Tsuruoka Municipal Shonai Hospital, 4-20 Izumi-machi, Tsuruoka-shi, Yamagata, 997-8515, Japan

    Takaaki Urakawa, Yuki Tanaka & Shinichi Goto

  2. Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi-Dori, Niigata, 951-8510, Japan

    Takaaki Urakawa, Kei Watanabe & Naoto Endo

  3. Center for Integrated Human Brain Science, Brain Research Institute, University of Niigata, 1-757 Asahimachi, Niigata, 951-8585, Japan

    Hitoshi Matsuzawa

Authors
  1. Takaaki Urakawa
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  2. Yuki Tanaka
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  3. Shinichi Goto
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  4. Hitoshi Matsuzawa
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  5. Kei Watanabe
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  6. Naoto Endo
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Corresponding author

Correspondence to Takaaki Urakawa.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

Conflict of interest

The authors declare that they have no conflicts of interest.

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Urakawa, T., Tanaka, Y., Goto, S. et al. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol 48, 239–244 (2019). https://doi.org/10.1007/s00256-018-3016-3

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  • DOI: https://doi.org/10.1007/s00256-018-3016-3

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