An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs.

Journal: The British journal of radiology
PMID: 33904763

Abstract

OBJECTIVES: Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis, perforation and death. Plain abdominal X-rays are frequently used as a first-line test but the availability of immediate expert radiological review is variable. The aim was to investigate the feasibility of using a deep learning model for automated identification of small bowel obstruction.

Authors

  • D H Kim
    Medical Imaging Department, Royal Devon and Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK. Electronic address: Daniel.kim@nhs.net.
  • H Wit
    The Medical Imaging Department, University Hospitals Plymouth NHS Trust, Plymouth, UK.
  • M Thurston
    The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust, Truro, UK.
  • M Long
    The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust, Truro, UK.
  • G F Maskell
    The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust, Truro, UK.
  • M J Strugnell
    The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust, Truro, UK.
  • D Shetty
    The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust, Truro, UK.
  • I M Smith
    The Department of General Surgery, The Royal Cornwall Hospitals NHS Trust, Truro, UK.
  • N P Hollings
    The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust, Truro, UK.

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