Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying endotracheal tube position on plain chest X-ray: a multi-case multi-reader study.

Journal: Critical care (London, England)
Published Date: Jul 28, 2025

Abstract

BACKGROUND: Incorrectly placed endotracheal tubes (ETTs) can lead to serious clinical harm. Studies have demonstrated the potential for artificial intelligence (AI)-led algorithms to detect ETT placement on chest X-Ray (CXR) images, however their effect on clinician accuracy remains unexplored. This study measured the impact of an AI-assisted ETT detection algorithm on the ability of clinical staff to correctly identify ETT misplacement on CXR images.

Authors

  • Alex Novak
    Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, United Kingdom, Oxford.
  • Sarim Ather
    Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Abdala T Espinosa Morgado
    Oxford Clinical Artificial Intelligence Research (OxCAIR), Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Giles Maskell
    Royal Cornwall Hospitals NHS Trust, Truro, Cornwall, UK.
  • Gordon W Cowell
    Department of Imaging, Queen Elizabeth University Hospital, Glasgow, UK.
  • Douglas Black
    NHS Greater Glasgow and Clyde, Glasgow, UK.
  • Akshay Shah
    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • James S Bowness
    Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK. Electronic address: james.bowness@jesus.ox.ac.uk.
  • Amied Shadmaan
    GE Healthcare Diagnostic Imaging, Little Chalfont, Buckinghamshire, UK.
  • Claire Bloomfield
    National Consortium of Intelligent Medical Imaging (NCIMI), The University of Oxford, Big Data Institute, Oxford, UK.
  • Jason L Oke
    The University of Oxford, Oxford, UK.
  • Hilal Johnson
    University of Oxford, Oxford, Oxfordshire, UK.
  • Mark Beggs
    University of Oxford, Oxford, Oxfordshire, UK.
  • Fergus Gleeson
    Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Peter Aylward
    Report and Image Quality Control (RAIQC), London, UK, UK.
  • Aqib Hafeez
    Emergency Medicine Research Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Moustafa Elramlawy
    Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
  • Kin Lam
    Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China.
  • Benjamin Griffiths
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Mirae Harford
    Royal Berkshire NHS Foundation Trust, Reading, UK.
  • Louise Aaron
    Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
  • Claire Seeley
    Royal Berkshire NHS Foundation Trust, Reading, UK.
  • Matthew Luney
    Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
  • James Kirkland
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Louise Wing
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Zahi Qamhawi
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Indrajeet Mandal
    John Radcliffe Hospital, Oxford University Hospitals NHS Trust, Oxford, UK.
  • Thomas Millard
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Michelle Chimbani
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Athirah Sharazi
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Emma Bryant
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Wendy Haithwaite
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Aurora Medonica
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

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