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Related Experiment Video

Updated: Sep 5, 2025

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Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network.

Takashi Nakamoto1, Akira Taguchi2, Naoya Kakimoto1

  • 1Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.

Dento Maxillo Facial Radiology
|July 11, 2022
PubMed
Summary

This study developed computer-aided systems using deep convolutional neural networks (CNNs) to predict osteoporosis from panoramic radiographs in women over 50. The AI systems showed promising accuracy, aiding early detection and fracture prevention.

Keywords:
artificial intelligencediagnostic screening programsosteoporosispanoramic radiography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Osteoporosis Research

Background:

  • Osteoporosis is a significant public health concern, particularly in postmenopausal women.
  • Early detection of osteoporosis is crucial for preventing debilitating fractures.
  • Conventional screening methods can be invasive or costly.

Purpose of the Study:

  • To develop and evaluate computer-aided screening systems for osteoporosis prediction.
  • To utilize deep convolutional neural networks (CNNs) for analyzing panoramic radiographs.
  • To assess the diagnostic performance of AI models in identifying osteoporosis risk.

Main Methods:

  • Three deep CNNs (Alexnet, VGG-16, GoogLeNet) were trained on panoramic radiographs from women aged ≥ 50 years.
  • Radiographs were classified based on cortical bone characteristics indicative of osteoporosis risk.
  • The performance of the CNN-based systems was validated against bone mineral density measurements.

Main Results:

  • The CNN models demonstrated strong agreement with expert radiologist classifications (86.0%-90.7%).
  • Predictive accuracies for lumbar spine osteoporosis ranged from 74.0% to 79.0%.
  • Predictive accuracies for femoral neck osteoporosis ranged from 70.0% to 75.0%.

Conclusions:

  • The developed AI systems show high potential for accurate osteoporosis prediction.
  • These systems can assist in identifying individuals who require further diagnostic evaluation.
  • Early prediction facilitated by AI may lead to timely interventions and fracture prevention.