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In this study, the authors explored the application of deep learning in patients with primary osteoporosis. Furthermore, they aimed to develop a fully automatic method based on a deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. The authors were able to determine that a deep learning-based method could achieve full automatic identification of osteoporosis, osteopenia, and normal BMD calculation in CT images.

Key points

  • Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images.
  • The average BMDs obtained by deep learning highly correlates with ones derived from QCT.
  • The deep learning-based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans.

Article: Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

Authors: Yijie Fang, Wei Li, Xiaojun Chen, Keming Chen, Han Kang, Pengxin Yu, Rongguo Zhang, Jianwei Liao, Guobin Hong & Shaolin Li

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