MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.

Journal: Frontiers in microbiology
Published Date: Dec 11, 2023

Abstract

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.

Authors

  • Xingyu Chen
    Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China.
  • Fanxuan Chen
    School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Chenglong Liang
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Guoqiang He
    Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China.
  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Yanchan Wu
    School of Electrical and Information Engineering, Quzhou University, Quzhou, China.
  • Yinda Chen
    School of Electrical and Information Engineering, Quzhou University, Quzhou, China.
  • Jincen Shuai
    Baskin Engineering, University of California, Santa Cruz, CA, United States.
  • Yilei Yang
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Chenyue Dai
    Wenzhou Medical University, Wenzhou, China.
  • Luhuan Cao
    Wenzhou Medical University, Wenzhou, China.
  • Xian Wang
    Wenzhou Medical University, Wenzhou, China.
  • Enna Cai
    Wenzhou Medical University, Wenzhou, China.
  • Jiamin Wang
    Wenzhou Medical University, Wenzhou, China.
  • Mengjing Wu
    Wenzhou Medical University, Wenzhou, China.
  • Li Zeng
    Wenzhou Medical University, Wenzhou, China.
  • Jiaqian Zhu
    Wenzhou Medical University, Wenzhou, China.
  • Darong Hai
    Wenzhou Medical University, Wenzhou, China.
  • Wangzheng Pan
    Renji College of Wenzhou Medical University, Wenzhou, China.
  • Shuo Pan
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Chengxi Zhang
    School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China.
  • Shichao Quan
    Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Feifei Su
    Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China.

Keywords

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