Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study.

Journal: The Lancet. Digital health
Published Date: Apr 5, 2022

Abstract

BACKGROUND: Proximal femoral fractures are an important clinical and public health issue associated with substantial morbidity and early mortality. Artificial intelligence might offer improved diagnostic accuracy for these fractures, but typical approaches to testing of artificial intelligence models can underestimate the risks of artificial intelligence-based diagnostic systems.

Authors

  • Lauren Oakden-Rayner
    School of Public Health, University of Adelaide, Adelaide, SA, Australia; Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia. Electronic address: lauren.oakden-rayner@adelaide.edu.au.
  • William Gale
    Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Computer Science, University of Adelaide, Adelaide, SA, Australia.
  • Thomas A Bonham
    Stanford University School of Medicine, Department of Radiology, Stanford, CA, USA.
  • Matthew P Lungren
  • Gustavo Carneiro
    Australian Centre for Visual Technologies, The University of Adelaide, Australia. Electronic address: gustavo.carneiro@adelaide.edu.au.
  • Andrew P Bradley
    ITEE, The University of Queensland, Australia.
  • Lyle J Palmer
    School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.

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