Tuberculosis: Advances in Diagnostics and Treatment

  • Ju Hee Katzman
  • Mindy Sampson
  • Beata CasañasEmail author


The World Health Organization’s “End TB Strategy” aims to achieve a 90% reduction in tuberculosis deaths and an 80% reduction in incidence by 2030; this calls for innovative approaches to counteract the high burden of disease through a two-prong strategy of early detection coupled with appropriate, timely treatment.

Artificial intelligence (AI) and machine learning are two remarkable technological breakthroughs that could revolutionize the management of tuberculosis. Examples of these technological advances include recognition of tubercle bacilli and abnormal chest radiograph recognition through the artificial neural networks. AI can learn to correlate massive amounts of data, which can be translated into clinically significant results. Another approach that has become promising is the development of virtual directly observed treatment (VDOT) via video, in areas with broadband internet and affordable devices that reach even remote places with tuberculosis patients. VDOT and drone drug delivery allow the treatment of tuberculosis in areas with minimal resources.


Tuberculosis Artificial intelligence Machine learning Deep learning Artificial neural network Smear microscopy Tuberculous pleural effusion Chest x-ray Computer aided programs Host-directed therapy Immunomodulation Drug resistance Virtually observed therapy 


Conflict of Interest

The authors report no conflicts of interest.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ju Hee Katzman
    • 1
  • Mindy Sampson
    • 1
  • Beata Casañas
    • 1
    Email author
  1. 1.Division of Infectious Diseases and International Health, Department of MedicineMorsani College of Medicine, University of South FloridaTampaUSA

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