Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

Journal: European radiology
Published Date: Aug 22, 2023

Abstract

OBJECTIVES: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

Authors

  • Quinlan D Buchlak
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia. quinlan.buchlak1@my.nd.edu.au.
  • Cyril H M Tang
    Annalise.ai, Sydney, NSW, Australia.
  • Jarrel C Y Seah
    From the Department of Radiology, Alfred Hospital, 55 Commercial Rd, Melbourne, Victoria 3004, Australia (J.C.Y.S., A.F.D.); Department of Radiology, Royal Melbourne Hospital, Melbourne, Australia (J.S.N.T., F.G.); Melbourne, Australia (A.K.); and Department of Radiology, Melbourne University, Melbourne, Australia (F.G.).
  • Andrew Johnson
    Annalise-AI, Sydney, New South Wales, Australia.
  • Xavier Holt
    annalise.ai, Sydney, New South Wales, Australia.
  • Georgina M Bottrell
    Annalise.ai, Sydney, NSW, Australia.
  • Jeffrey B Wardman
    Annalise.ai, Sydney, NSW, Australia.
  • Gihan Samarasinghe
    School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.
  • Leonardo Dos Santos Pinheiro
    Annalise.ai, Sydney, NSW, Australia.
  • Hongze Xia
    Annalise.ai, Sydney, NSW, Australia.
  • Hassan K Ahmad
    Annalise.ai, Sydney, NSW, Australia.
  • Hung Pham
    VA Palo Alto Health Care System, Livermore, CA, USA; Stanford University, CA, USA.
  • Jason I Chiang
    Annalise.ai, Sydney, NSW, Australia.
  • Nalan Ektas
    Annalise.ai, Sydney, NSW, Australia.
  • Michael R Milne
    Annalise-AI, Sydney, New South Wales, Australia michael.milne@annalise.ai.
  • Christopher H Y Chiu
    Annalise.ai, Sydney, NSW, Australia.
  • Ben Hachey
    Annalise.ai, Sydney, New South Wales, Australia.
  • Melissa K Ryan
    Annalise.ai, Sydney, NSW, Australia.
  • Benjamin P Johnston
    Annalise.ai, Sydney, NSW, Australia.
  • Nazanin Esmaili
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
  • Christine Bennett
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
  • Tony Goldschlager
    Department of Neurosurgery, Monash Health, Melbourne, VIC, Australia.
  • Jonathan Hall
    Annalise.ai, Sydney, NSW, Australia.
  • Duc Tan Vo
    Department of Radiology, University Medical Center, University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam.
  • 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.
  • Jean-Christophe Leveque
    Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.
  • Farrokh Farrokhi
    Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.
  • Richard G Abramson
    Radiology and Radiological Science, Vanderbilt University, Nashville, TN 37235, USA.
  • Catherine M Jones
    I-MED Radiology Network, Brisbane, Queensland, Australia.
  • Simon Edelstein
    Annalise.ai, Sydney, NSW, Australia; Department of Radiology, Monash Health, Melbourne, VIC, Australia; I-MED Radiology Network, Brisbane, QLD, Australia.
  • Peter Brotchie
    St Vincent's Hospital, Melbourne, Victoria, Australia.

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