Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection.

Journal: Clinical neuroradiology
Published Date: Jun 1, 2023

Abstract

PURPOSE: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks.

Authors

  • Siddharth Agarwal
    School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
  • David Wood
    School of Biomedical Engineering & Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, SE1 7EH, London, UK.
  • Mariusz Grzeda
    School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Chandhini Suresh
    Leicester Medical School, University of Leicester, LE1 7RH, Leicester, UK.
  • Munaib Din
    School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • James Cole
    Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, London, UK.
  • Marc Modat
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Thomas C Booth
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom.

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