The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms
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-...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1290746/full |
| _version_ | 1797650667529043968 |
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| author | Yi Shi Yi Shi Chengxi Zhang Shuo Pan Yi Chen Xingguo Miao Xingguo Miao Xingguo Miao Guoqiang He Guoqiang He Yanchan Wu Hui Ye Hui Ye Hui Ye Chujun Weng Huanhuan Zhang Wenya Zhou Xiaojie Yang Chenglong Liang Dong Chen Dong Chen Liang Hong Feifei Su Feifei Su Feifei Su |
| author_facet | Yi Shi Yi Shi Chengxi Zhang Shuo Pan Yi Chen Xingguo Miao Xingguo Miao Xingguo Miao Guoqiang He Guoqiang He Yanchan Wu Hui Ye Hui Ye Hui Ye Chujun Weng Huanhuan Zhang Wenya Zhou Xiaojie Yang Chenglong Liang Dong Chen Dong Chen Liang Hong Feifei Su Feifei Su Feifei Su |
| author_sort | Yi Shi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T16:04:24Z |
| format | Article |
| id | doaj.art-bfc65f16d49049a4836ec0c297a7d9f4 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-03-11T16:04:24Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-bfc65f16d49049a4836ec0c297a7d9f42023-10-25T06:11:57ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-10-011410.3389/fmicb.2023.12907461290746The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithmsYi Shi0Yi Shi1Chengxi Zhang2Shuo Pan3Yi Chen4Xingguo Miao5Xingguo Miao6Xingguo Miao7Guoqiang He8Guoqiang He9Yanchan Wu10Hui Ye11Hui Ye12Hui Ye13Chujun Weng14Huanhuan Zhang15Wenya Zhou16Xiaojie Yang17Chenglong Liang18Dong Chen19Dong Chen20Liang Hong21Feifei Su22Feifei Su23Feifei Su24Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, ChinaThe First School of Medicine, Wenzhou Medical University, Wenzhou, ChinaSchool of Materials Science and Engineering, Shandong Jianzhu University, Jinan, ChinaThe First School of Medicine, Wenzhou Medical University, Wenzhou, ChinaThe First School of Medicine, Wenzhou Medical University, Wenzhou, ChinaDepartment of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, ChinaDepartment of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, ChinaWenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, ChinaPostgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, ChinaWenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, ChinaSchool of Electrical and Information Engineering, Quzhou University, Quzhou, ChinaDepartment of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, ChinaDepartment of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, ChinaWenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, ChinaThe Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu, China0School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China0School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China1Wenzhou Medical University Renji College, Wenzhou, ChinaThe First School of Medicine, Wenzhou Medical University, Wenzhou, ChinaWenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China2Wenzhou Central Blood Station, Wenzhou, China3Department of Infectious Diseases, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, ChinaDepartment of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, ChinaWenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, ChinaTuberculous 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.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1290746/fulltuberculous meningitismachine learningnext-generation sequencingdiagnosisinfectious diseasesmycobacterium tuberculosis |
| spellingShingle | Yi Shi Yi Shi Chengxi Zhang Shuo Pan Yi Chen Xingguo Miao Xingguo Miao Xingguo Miao Guoqiang He Guoqiang He Yanchan Wu Hui Ye Hui Ye Hui Ye Chujun Weng Huanhuan Zhang Wenya Zhou Xiaojie Yang Chenglong Liang Dong Chen Dong Chen Liang Hong Feifei Su Feifei Su Feifei Su The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms Frontiers in Microbiology tuberculous meningitis machine learning next-generation sequencing diagnosis infectious diseases mycobacterium tuberculosis |
| title | The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms |
| title_full | The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms |
| title_fullStr | The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms |
| title_full_unstemmed | The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms |
| title_short | The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms |
| title_sort | diagnosis of tuberculous meningitis advancements in new technologies and machine learning algorithms |
| topic | tuberculous meningitis machine learning next-generation sequencing diagnosis infectious diseases mycobacterium tuberculosis |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1290746/full |
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