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The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population

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Abstract

Objective

Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray features extracted by deep learning to alert high-risk osteoporosis populations.

Materials and methods

We retrospectively used data obtained from health check-ups including spine X-ray and dual-energy X-ray absorptiometry (DXA). Consecutively, we selected people with normal and abnormal bone mineral density. From the regions of interest of X-ray images, deep convolutional networks were used to generate image features. We designed prediction models for abnormal BMD using the image features trained by machine learning classification algorithms. The performances of each model were evaluated.

Results

From 334 participants, 170 images of abnormal (T scores < − 1.0 standard deviations (SD)) and 164 of normal BMD (T scores > = − 1.0 SD) were used for analysis. We found that a combination of feature extraction by VGGnet and classification by random forest based on the maximum balanced classification rate (BCR) yielded the best performance in terms of the area under the curve (AUC) (0.74), accuracy (0.71), sensitivity (0.81), specificity (0.60), BCR (0.70), and F1-score (0.73).

Conclusion

In this study, we explored various machine learning algorithms for the prediction of BMD using simple spine X-ray image features extracted by three deep learning algorithms. We identified the combination for the best performance in predicting high-risk populations with abnormal BMD.

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Funding

This study received funding from the Seoul National University Hospital Research Fund, grant number 0420170720.

Author information

Authors and Affiliations

  1. Samsung Electronics, Suwon, South Korea

    Sangwoo Lee

  2. Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul, 135-984, South Korea

    Eun Kyung Choe

  3. Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea

    Eun Kyung Choe

  4. Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea

    Hae Yeon Kang & Ji Won Yoon

  5. Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea

    Hua Sun Kim

Authors
  1. Sangwoo Lee
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  2. Eun Kyung Choe
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  4. Ji Won Yoon
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  5. Hua Sun Kim
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Corresponding author

Correspondence to Eun Kyung Choe.

Ethics declarations

The study was conducted in accordance with the Declaration of Helsinki.

Conflict of interest

The authors declare that they have no conflicts of interest.

Informed consent

Informed consent was waived by the IRB.

Ethical approval

The Institutional Review Board (IRB) of the Seoul National University Hospital approved the study protocol (IRB number 1808-008-962).

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Lee, S., Choe, E.K., Kang, H.Y. et al. The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population. Skeletal Radiol 49, 613–618 (2020). https://doi.org/10.1007/s00256-019-03342-6

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  • DOI: https://doi.org/10.1007/s00256-019-03342-6

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