Automated diagnosis of Tuberculosis: A review of advancements

Authors

  • EO Shobowale
  • AO Coker
  • BA Adegunle

DOI:

https://doi.org/10.38029/bumj.v1i2.12

Keywords:

Mycobacterium tuberculosis, Automated diagnosis, BACTEC 460, Gene Xpert, MGIT 960

Abstract

Objective: A narrative review of advances in automated diagnostic tests for diagnosis of tuberculosis infections.

Methods: Electronic databases were searched for tests on automation in Mycobacterium tuberculosis identication. Studies were selected and evaluated that tested for the performance of new and old methods in automated diagnosis with significant impact on the turn-around time of diagnosis and also positive impact on patient care with respect to outcomes.

Results: A total of 40 studies were included. Overall, the gene expert system was found to be superior when applied to respiratory samples as opposed to other body fluids when compared to other test methods.

High specificity estimates suggest that  Nucleic Acid Amplification Tests (NAATS) should be the first-line test for rapid diagnosis of meningitis, but that they also need to be combined with the result of other tests in order to rule out disease. 

Discussion: Fully automated liquid culture methods overall are superior to mycobacterial culture on solid media, in terms of speed of diagnosis, ease of use and their accuracy with several user friendly systems that can be applied to the Nigerian environment.

Conclusion: The DNA amplification tests provide a reliable way of increasing the specificity of diagnosis. Their superior diagnostic capability has been found to hold up in routine clinical practice, and they could confer several advantages on tuberculosis control programs.

Author Biographies

EO Shobowale

Department of Medical Microbiology and Parasitology, Babcock University, Ilishan-Remo, Ogun State, Nigeria

AO Coker

Department of Medical Microbiology and Parasitology, Babcock University, Ilishan-Remo, Ogun State, Nigeria

BA Adegunle

Department of Medical Microbiology and Parasitology, Babcock University, Ilishan-Remo, Ogun State, Nigeria

Published

2015-06-30

How to Cite

Shobowale, E., Coker, A., & Adegunle, B. (2015). Automated diagnosis of Tuberculosis: A review of advancements. Babcock University Medical Journal (BUMJ), 1(2), 42-48. https://doi.org/10.38029/bumj.v1i2.12

Issue

Section

Research Article