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Deep neural network improves fracture detection by clinicians.

Robert Lindsey1,2, Aaron Daluiski3,4, Sumit Chopra3

  • 1Imagen Technologies, New York, NY 10012; rob@imagen.ai.

Proceedings of the National Academy of Sciences of the United States of America
|October 24, 2018
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A new deep learning tool significantly improved fracture detection in X-rays for emergency clinicians. This AI model enhanced diagnostic accuracy, reducing misinterpretation rates and improving patient care for suspected fractures.

Keywords:
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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Orthopedic Diagnostics

Background:

  • Suspected fractures are common emergency department (ED) visits, with X-rays being the primary diagnostic tool.
  • Misdiagnosed fractures in EDs lead to delayed treatment and poor patient outcomes, often due to lack of specialized orthopedic expertise.
  • Diagnostic errors in fracture detection are a significant concern in emergency medicine settings.

Purpose of the Study:

  • To develop and evaluate a deep neural network (DNN) for detecting and localizing fractures in radiographs.
  • To assess the impact of the DNN on the diagnostic accuracy of emergency medicine clinicians.
  • To determine if the DNN can effectively transfer specialized orthopedic expertise to generalist clinicians.

Main Methods:

  • A deep neural network was trained on 135,409 radiographs annotated by 18 senior orthopedic surgeons.
  • A controlled experiment was conducted where emergency medicine clinicians interpreted wrist radiographs with and without AI assistance.
  • Key performance metrics including sensitivity, specificity, and misinterpretation rates were compared between aided and unaided groups.

Main Results:

  • Clinician sensitivity improved from 80.8% (unaided) to 91.5% (aided).
  • Clinician specificity improved from 87.5% (unaided) to 93.9% (aided).
  • The AI tool resulted in a 47.0% relative reduction in the misinterpretation rate for fractures.

Conclusions:

  • Deep learning models can significantly enhance the diagnostic accuracy of fracture detection in radiographs.
  • AI assistance empowers generalist clinicians with specialized expertise, improving patient care in emergency settings.
  • This technology offers a scalable solution for delivering expert-level diagnostic support to frontline medical professionals.