Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

Journal: PloS one
Published Date: Jan 1, 2025

Abstract

INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MRI) has not been well investigated. We used a convolutional neural network (CNN) to explore the complementary contribution of brain MRI to the conventional prognostic determinants.

Authors

  • Trinh Huu Khanh Dong
    Oxford University Clinical Research Unit, Viet Nam.
  • Liane S Canas
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. Electronic address: liane.dos_santos_canas@kcl.ac.uk.
  • Joseph Donovan
    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam.
  • Daniel Beasley
    School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Nguyen Thuy Thuong-Thuong
    Oxford University Clinical Research Unit, Viet Nam.
  • Nguyen Hoan Phu
    Oxford University Clinical Research Unit, Viet Nam.
  • Nguyen Thi Ha
    Oxford University Clinical Research Unit, Viet Nam.
  • Sébastien Ourselin
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Reza Razavi
  • Guy E Thwaites
    Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Marc Modat
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.

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