Immunological testing and machine learning in detecting latent tuberculosis among high-risk groups (nature review)
The creation of a strategy for detecting LTBI in individuals from risk groups can facilitate the detection of infection and play an important role in preventing the development of tuberculosis. The possibility of using machine learning and artificial intelligence will allow the risk of developing active tuberculosis to be determined based on the use of immunological tests.
Authors
- D.А. Kudlаy (ORCID: https://orcid.org/0000-0003-1878-4467)
- Анна Старшинова (ORCID: https://orcid.org/0000-0002-9023-6986)
- R. K. Tukfatullin (ORCID: https://orcid.org/0009-0003-4100-6839)
- Р. А. Шарипов
- I. F. Dovgalyuk
- Igor Kudryavtsev (ORCID: https://orcid.org/0000-0002-0005-3095)
- Ekaterina Belyaeva
- Adilya Sabirova
- Artem Rubinstein
- Nikolay Nikolenko
- Kulpina Anastasia
- Leonid P. Churilov
Institutions
- St Petersburg University (RU)
- Sechenov University (RU)
- Government of Russia (RU)
- Federal Almazov North-West Medical Research Centre (RU)
- Bashkir State Medical University (RU)
- Institute of Experimental Medicine (RU)
- Bioengineering Center (RU)
- Saint-Petersburg Research Institute of Phthisiopulmonology (RU)
- Saint Petersburg State Pediatric Medical University (RU)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-07
- DOI
- https://doi.org/10.3389/fmed.2025.1710960
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and Higher Education of the Russian Federation