Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.

Journal: Clinical neuroradiology
PMID: 39546007

Abstract

PURPOSE: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk.

Authors

  • Karan Daga
    School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK.
  • Siddharth Agarwal
    School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
  • Zaeem Moti
    Guy's and St. Thomas' NHS Foundation Trust, Westminster Bridge Road, UK SE1 7EH, London, UK.
  • Matthew B K Lee
    University College London Hospital NHS Foundation Trust, 235 Euston Rd, UK NW1 2BU, London, UK.
  • Munaib Din
    School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • David Wood
    School of Biomedical Engineering & Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, SE1 7EH, 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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