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Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation

Miso Jang1,2, Mingyu Kim2, Sung Jin Bae3

  • 1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Journal of Bone and Mineral Research : the Official Journal of the American Society for Bone and Mineral Research
|November 23, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning models can now screen for osteoporosis using chest X-rays, a previously unexplored area. This OsPor-screen model shows high accuracy, offering potential for opportunistic osteoporosis screening in clinical settings.

Keywords:
ANALYSIS/QUANTITATION OF BONEDISEASES AND DISORDERS OF/RELATED TO BONEDXAOSTEOPOROSISPRACTICE/POLICY-RELATED ISSUESSCREENING

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Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model
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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Bone Metabolism and Disease

Background:

  • Osteoporosis is a silent disease, often diagnosed only after fractures occur.
  • Current osteoporosis screening methods are limited, and deep learning applications for this disease using chest radiographs are underexplored.
  • Fractures associated with osteoporosis lead to significant morbidity and mortality.

Purpose of the Study:

  • To develop and evaluate a deep learning model (OsPor-screen) for osteoporosis screening using chest radiographs.
  • To assess the performance of the OsPor-screen model in both internal and external test datasets.
  • To explore the potential of using readily available chest X-rays for opportunistic osteoporosis screening.

Main Methods:

  • A deep learning model was trained using a supervised learning approach on a dataset of 13,026 paired chest radiographs and dual-energy X-ray absorptiometry (DXA) results.
  • The model was validated on an internal test set and an external cohort dataset (1089 chest radiographs).
  • Model performance was evaluated using sensitivity, specificity, and area under the curve (AUC); visual explanations were generated using Grad-CAMs.

Main Results:

  • The OsPor-screen model achieved an AUC of 0.91 in the internal test set and 0.88 in the external test set.
  • The model demonstrated promising performance in identifying osteoporosis based on DXA-defined criteria (T-score ≤ -2.5).
  • Substudies investigated the impact of anatomical subregions and image sizes on model performance.

Conclusions:

  • Deep learning models, like OsPor-screen, show potential for opportunistic automated screening of osteoporosis using chest radiographs.
  • This approach could enhance early detection and management of osteoporosis in clinical settings.
  • Further research may clarify the clinical relevance of visual explanations (Grad-CAMs) for model predictions.