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Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12928)

Abstract

Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 1651 CXR cases with ground truth BMD measured by gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.853). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.928). As the first effort in the field using CXR scans to predict the BMD, the proposed algorithm holds a strong potential in early osteoporosis screening and public health promotion.

Keywords

  • Bone mineral density estimation
  • Chest X-ray
  • Multi-ROI model

This work was done when Fakai Wang interned at PAII Inc.

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Wang, F. et al. (2021). Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. In: Rekik, I., Adeli, E., Park, S.H., Schnabel, J. (eds) Predictive Intelligence in Medicine. PRIME 2021. Lecture Notes in Computer Science(), vol 12928. Springer, Cham. https://doi.org/10.1007/978-3-030-87602-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-87602-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87601-2

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