Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study.

Journal: Abdominal radiology (New York)
Published Date: Aug 10, 2024

Abstract

BACKGROUND: In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Currently this is performed manually, generally by trained radiologists, who are seeing an inexorable growth in their workload. Integrating artificial intelligence (AI) and non-radiologist personnel into the workflow potentially addresses the increasing workload without sacrificing accuracy. This study evaluated the accuracy of non-radiologist technicians in liver cancer imaging compared with radiologists, both assisted by AI.

Authors

  • Luis Núñez
    Perspectum Ltd., Gemini One, 5520 John Smith Drive, Oxford, OX4 2LL, UK.
  • Carlos Ferreira
  • Amirkasra Mojtahed
    Division of Abdominal Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Hildo Lamb
    Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.
  • Stefano Cappio
    Clinica Di Radiologia EOC, Istituto Di Imaging Della Svizzera Italiana (IIMSI), Lugano, Switzerland.
  • Mohammad Ali Husainy
    Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Andrea Dennis
    Perspectum Ltd., Gemini One, 5520 John Smith Drive, Oxford, OX4 2LL, UK.
  • Michele Pansini
    Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

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