Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.

Journal: The British journal of radiology
Published Date: Mar 24, 2022

Abstract

OBJECTIVES: Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network - Annalise CXR V1.2 (Annalise.ai) - for detection of traumatic injuries on supine chest radiographs.

Authors

  • Jacob Gipson
    Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.
  • Victor Tang
    Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.
  • Jarrel Seah
    Department of Neuroscience, Monash University, Melbourne, Australia; Radiology and Nuclear Medicine, Alfred Health, Melbourne, Australia.
  • Helen Kavnoudias
    Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.
  • Adil Zia
    Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.
  • Robin Lee
    Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.
  • Biswadev Mitra
    Trauma Service Center, Alfred Hospital, 55 Commercial Road, Melbourne, VIC 3004, Australia. Electronic address: biswadev.mitra@monash.edu.
  • Warren Clements
    Department of Radiology, Alfred Hospital, Melbourne, Victoria, Australia.

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