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Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.

Youngjune KimDongjun ChoiKyong Joon LeeYusuhn KangJoong Mo AhnEugene LeeJoon Woo LeeHeung Sik Kang
Published in: European radiology (2020)
• The deep learning algorithm can rule out significant rotator cuff tear with a negative likelihood ratio of 0.06 and a negative predictive value of 96.6%. • The deep learning algorithm can guide patients with significant rotator cuff tear to additional shoulder ultrasound or MRI with a sensitivity of 97.3%. • The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients with clinically suspected rotator cuff tear.
Keyphrases
  • rotator cuff
  • deep learning
  • artificial intelligence
  • convolutional neural network
  • machine learning
  • magnetic resonance imaging
  • magnetic resonance
  • contrast enhanced
  • computed tomography
  • diffusion weighted imaging