The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms

Frontiers in Microbiology

Microbiology

Liang Hong¹  Hui Ye²  Feifei Su²  Xingguo Miao²  Yi Shi³  Guoqiang He⁴  Huanhuan Zhang⁵  Wenya Zhou⁵  Yanchan Wu⁶  Chengxi Zhang⁷  Shuo Pan⁸  Chenglong Liang⁸  Yi Chen⁸  Chujun Weng⁹  Dong Chen^1,10  Xiaojie Yang^1,11 

[1] Department of Infectious Diseases, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China;Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China;Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China;Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China;The First School of Medicine, Wenzhou Medical University, Wenzhou, China;Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China;Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China;School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China;School of Electrical and Information Engineering, Quzhou University, Quzhou, China;School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China;The First School of Medicine, Wenzhou Medical University, Wenzhou, China;The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu, China;Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China;Wenzhou Central Blood Station, Wenzhou, China;Wenzhou Medical University Renji College, Wenzhou, China;

关键词: tuberculous meningitis;    machine learning;    next-generation sequencing;    diagnosis;    infectious diseases;    mycobacterium tuberculosis;   

DOI  :  10.3389/fmicb.2023.1290746

 received in 2023-09-08, accepted in 2023-10-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

Tuberculous meningitis (TBM) poses a diagnostic challenge, particularly impacting vulnerable populations such as infants and those with untreated HIV. Given the diagnostic intricacies of TBM, there’s a pressing need for rapid and reliable diagnostic tools. This review scrutinizes the efficacy of up-and-coming technologies like machine learning in transforming TBM diagnostics and management. Advanced diagnostic technologies like targeted gene sequencing, real-time polymerase chain reaction (RT-PCR), miRNA assays, and metagenomic next-generation sequencing (mNGS) offer promising avenues for early TBM detection. The capabilities of these technologies are further augmented when paired with mass spectrometry, metabolomics, and proteomics, enriching the pool of disease-specific biomarkers. Machine learning algorithms, adept at sifting through voluminous datasets like medical imaging, genomic profiles, and patient histories, are increasingly revealing nuanced disease pathways, thereby elevating diagnostic accuracy and guiding treatment strategies. While these burgeoning technologies offer hope for more precise TBM diagnosis, hurdles remain in terms of their clinical implementation. Future endeavors should zero in on the validation of these tools through prospective studies, critically evaluating their limitations, and outlining protocols for seamless incorporation into established healthcare frameworks. Through this review, we aim to present an exhaustive snapshot of emerging diagnostic modalities in TBM, the current standing of machine learning in meningitis diagnostics, and the challenges and future prospects of converging these domains.

【 授权许可】

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Copyright © 2023 Shi, Zhang, Pan, Chen, Miao, He, Wu, Ye, Weng, Zhang, Zhou, Yang, Liang, Chen, Hong and Su.