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Artificial intelligence for analyzing orthopedic trauma radiographs.

Jakub Olczak1, Niklas Fahlberg2, Atsuto Maki3

  • 1a Department of Clinical Sciences , Karolinska Institutet , Danderyd Hospital.

Acta Orthopaedica
|July 7, 2017
PubMed
Summary

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Artificial intelligence (deep learning) shows human-level performance in classifying orthopedic radiographs, achieving 83% accuracy for fractures. This technology is feasible for skeletal imaging, with potential for future enhancements.

Area of Science:

  • Orthopedic imaging
  • Artificial intelligence in medicine
  • Deep learning applications

Background:

  • Deep learning has advanced non-medical image classification.
  • AI has not yet been applied in orthopedic settings.
  • Skeletal radiographs are a key diagnostic tool.

Purpose of the Study:

  • To assess the feasibility of deep learning for orthopedic radiographs.
  • To evaluate AI performance in classifying skeletal images.
  • To benchmark AI against human expert performance.

Main Methods:

  • Extracted 256,000 wrist, hand, and ankle radiographs.
  • Utilized 5 deep learning networks adapted for medical images.
  • Benchmarked the best network against fractures and senior orthopedic surgeons.

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Main Results:

  • AI networks achieved >90% accuracy for laterality, body part, and exam view.
  • The best network achieved 83% accuracy for fracture identification.
  • AI performance was comparable to senior orthopedic surgeons at similar resolutions (Cohen's kappa = 0.76).

Conclusions:

  • Artificial intelligence is a feasible tool for orthopedic radiographs.
  • AI demonstrates human-level performance in classifying skeletal images.
  • Future technical solutions can address current AI limitations for surgical needs.