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Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning.

Ravi Patel1, Elizabeth H E Thong1, Vineet Batta1

  • 1Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.).

Radiology. Artificial Intelligence
|August 5, 2021
PubMed
Summary
This summary is machine-generated.

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A new artificial intelligence model accurately identifies orthopedic implant designs from radiographs, outperforming human experts. This technology aids in planning revision surgeries when implant records are missing.

Area of Science:

  • Orthopedic surgery
  • Medical imaging
  • Artificial intelligence

Background:

  • Accurate identification of orthopedic implant models is crucial for revision arthroplasty planning.
  • Surgical records of implant models are often unavailable, complicating preoperative assessments.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) for identifying orthopedic implant models using radiographs.
  • To compare the CNN's performance against senior orthopedic specialists.

Main Methods:

  • A retrospective study utilized 427 knee and 922 hip radiographs from 650 patients with 12 implant models.
  • A U-Net segmentation network was developed for implant masking, and classification networks processed original and masked radiographs.
  • An ensemble approach combined predictions for a consensus output, with accuracy compared to five specialists using McNemar's exact test.

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Main Results:

  • The final CNN achieved 98.9% accuracy and 100% top-three accuracy on an unseen dataset of 180 radiographs.
  • The network significantly outperformed all five specialists (median accuracy 76.1%, best accuracy 85.6%; P < .001).
  • The model demonstrated robustness to varying scan quality and challenging implant distinctions.

Conclusions:

  • A novel neural network model was developed that surpasses senior orthopedic specialists in identifying implant models from radiographs.
  • The developed AI tool shows potential for real-world application in orthopedic surgery.
  • Freely available code and radiographs support broader training and implementation for various implants and joints.