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Artificial intelligence (AI) software improved distal radius fracture (DRF) detection accuracy for physicians. AI assistance enhanced sensitivity and specificity, significantly reducing overall error rates in fracture diagnosis.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Orthopedic Surgery

Background:

  • Distal radius fractures (DRFs) are common orthopedic injuries.
  • Accurate detection of DRFs is crucial for effective patient management.
  • Physician diagnostic accuracy can be influenced by various factors, necessitating supportive tools.

Purpose of the Study:

  • To develop artificial intelligence (AI) software to aid physicians in detecting distal radius fractures (DRFs).
  • To evaluate the impact of AI software support on the diagnostic accuracy of physicians for DRFs.
  • To compare the performance of physicians with and without AI assistance in identifying wrist fractures.

Main Methods:

  • A convolutional neural network (CNN) model was trained on a large dataset of wrist radiographs (26,121 images).
  • Eleven physicians (six trainees, five hand surgeons) assessed 200 wrist radiograph pairs twice: once unaided and once with AI support.
  • Diagnostic accuracy metrics, including sensitivity, specificity, and error rates, were compared between unaided and AI-assisted assessments.

Main Results:

  • The CNN model achieved an area under the receiver operating curve of 0.97.
  • AI assistance improved physician sensitivity for DRF detection from 80% to 87%.
  • AI support increased specificity from 91% to 95%, reducing the overall error rate from 14% to 9%.

Conclusions:

  • AI software, used as a second opinion, can enhance the diagnostic accuracy of distal radius fracture detection.
  • The integration of AI tools shows promise in improving orthopedic diagnostic workflows.
  • AI-assisted diagnosis may lead to more accurate and efficient fracture identification in clinical settings.