Tuberculosis (TB) continues to be a global health issue, affecting millions of people around the world. Early detection is crucial for managing and treating the disease.
Advanced diagnostic technologies, such as deep-learning models and molecular tests, may improve the accuracy and efficiency of early TB detection, according to results from a recent systematic review. The report, published in Pneumonia, analyzed studies on early TB detection, detailing methodologies as well as their limitations.
The review emphasized the importance of TB screening for contacts and comprehensive health education for household contacts. Technologies such as deep-learning models for X-ray analysis, interferon-gamma release assays (IGRA), portable X-rays, nucleic acid amplification tests, and enzyme-linked immunosorbent assays are useful for TB detection, according to the report's authors.
Key diagnostic approaches identified include:
- Molecular tests, such as the GeneXpert MTB/RIF assay, which detect TB bacteria and drug resistance.
- IGRA, which measure immune response to TB-specific antigens.
- Chest X-rays, which identify TB-related abnormalities, such as nodules, infiltrates, or cavities, particularly in individuals with symptoms suggestive of pulmonary TB.
- Deep-learning models, which improve the accuracy and efficiency of detecting nodules, infiltrates, and cavities in chest X-ray images.
Additionally, the review discussed challenges in TB detection, particularly in resource-limited settings, such as limited access to diagnostics, socioeconomic barriers, and the complex presentation of the disease, which often delays diagnosis. The report also described the role of comprehensive health education and active case finding in improving TB detection rates and reducing transmission.
The researchers indicated a need for ongoing research to develop more accurate and affordable diagnostic tools and emphasized a multifaceted approach to TB management and control.
Full disclosures can be found in the published study.