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

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.

Methods

We pooled data from two randomised control trials of HIV-positive and HIV-negative adults with clinical TBM in Vietnam to predict the occurrence of death or new neurological complications in the first two months after the subject's first MRI session. We developed and compared three models: a logistic regression with clinical, demographic and labo

SUBMITTER: Dong THK 

PROVIDER: S-EPMC12101703 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature