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Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study

Due to COVID-19 pandemic, there is a large global drop in the number of newly diagnosed cases with tuberculosis (TB) worldwide. Actions to mitigate and reverse the impact of the COVID-19 pandemic on TB are urgently needed. Recent development of TB smear microscopy automation systems using artificial...

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Autores principales: Huang, Hsiao-Chuan, Kuo, King-Lung, Lo, Mei-Hsin, Chou, Hsiao-Yun, Lin, Yusen E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116043/
https://www.ncbi.nlm.nih.gov/pubmed/35609488
http://dx.doi.org/10.1016/j.tube.2022.102212
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author Huang, Hsiao-Chuan
Kuo, King-Lung
Lo, Mei-Hsin
Chou, Hsiao-Yun
Lin, Yusen E.
author_facet Huang, Hsiao-Chuan
Kuo, King-Lung
Lo, Mei-Hsin
Chou, Hsiao-Yun
Lin, Yusen E.
author_sort Huang, Hsiao-Chuan
collection PubMed
description Due to COVID-19 pandemic, there is a large global drop in the number of newly diagnosed cases with tuberculosis (TB) worldwide. Actions to mitigate and reverse the impact of the COVID-19 pandemic on TB are urgently needed. Recent development of TB smear microscopy automation systems using artificial intelligence may increase the sensitivity of TB smear microscopy. The objective is to evaluate the performance of an automation system (μ-Scan 2.0, Wellgen Medical) over manual smear microscopy in a multi-center, double-blind trial. Total of 1726 smears were enrolled. Referee medical technician and culture served as primary and secondary gold standards for result discrepancy. Results showed that, compared to manual microscopy, the μ-Scan 2.0's performance of accuracy, sensitivity and specificity were 95.7% (1651/1726), 87.7% (57/65), and 96.0% (1594/1661), respectively. The negative predictive value was 97.8% at prevalence of 8.2%. Manual smear microscopy remains the primary diagnosis of pulmonary tuberculosis (TB). Use of automation system could achieve higher TB smear sensitivity and laboratory efficiency. It can also serve as a screening tool that complements molecular methods to reduce the total cost for TB diagnosis and control. Furthermore, such automation system is capable of remote access by internet connection and can be deployed in area with limited medical resources.
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spelling pubmed-91160432022-05-18 Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study Huang, Hsiao-Chuan Kuo, King-Lung Lo, Mei-Hsin Chou, Hsiao-Yun Lin, Yusen E. Tuberculosis (Edinb) Article Due to COVID-19 pandemic, there is a large global drop in the number of newly diagnosed cases with tuberculosis (TB) worldwide. Actions to mitigate and reverse the impact of the COVID-19 pandemic on TB are urgently needed. Recent development of TB smear microscopy automation systems using artificial intelligence may increase the sensitivity of TB smear microscopy. The objective is to evaluate the performance of an automation system (μ-Scan 2.0, Wellgen Medical) over manual smear microscopy in a multi-center, double-blind trial. Total of 1726 smears were enrolled. Referee medical technician and culture served as primary and secondary gold standards for result discrepancy. Results showed that, compared to manual microscopy, the μ-Scan 2.0's performance of accuracy, sensitivity and specificity were 95.7% (1651/1726), 87.7% (57/65), and 96.0% (1594/1661), respectively. The negative predictive value was 97.8% at prevalence of 8.2%. Manual smear microscopy remains the primary diagnosis of pulmonary tuberculosis (TB). Use of automation system could achieve higher TB smear sensitivity and laboratory efficiency. It can also serve as a screening tool that complements molecular methods to reduce the total cost for TB diagnosis and control. Furthermore, such automation system is capable of remote access by internet connection and can be deployed in area with limited medical resources. Elsevier Ltd. 2022-07 2022-05-18 /pmc/articles/PMC9116043/ /pubmed/35609488 http://dx.doi.org/10.1016/j.tube.2022.102212 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Huang, Hsiao-Chuan
Kuo, King-Lung
Lo, Mei-Hsin
Chou, Hsiao-Yun
Lin, Yusen E.
Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study
title Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study
title_full Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study
title_fullStr Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study
title_full_unstemmed Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study
title_short Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study
title_sort novel tb smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – a multi-center double blind study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116043/
https://www.ncbi.nlm.nih.gov/pubmed/35609488
http://dx.doi.org/10.1016/j.tube.2022.102212
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