Deep neural network improves fracture detection by clinicians

Abstract

Significance Historically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of deep learning approaches to CAD, there is great excitement about its application to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applications. We trained a deep learning model to detect fractures on radiographs with a diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. We demonstrate that when emergency medicine clinicians are provided with the assistance of the trained model, their ability to accurately detect fractures significantly improves.

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Affiliated Institutions

• McGill University CA
• University of Washington US
• University of Colorado Boulder US
• Mount Sinai Health System US
• Northwell Health US
• Harborview Medical Center US
• Stanford University US
• Hospital for Special Surgery US

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