Machine learning comes to Aid of over 2 billion people with latent TB

Machine learning and precision medicine could help identify tuberculosis patients with the highest risk of reactivation of the disease, say researchers, which could go a long way towards fighting the disease in India where it has a high prevalence.

Researchers from the University of Michigan in the US have shown that identifying multiple biomarkers can provide a more accurate diagnosis for patients with Latent Tuberculosis Infection, when a person is infected with Mycobacterium tuberculosis but does not have active tuberculosis. It affects nearly 2 billion people in the world.

Ryan Bailey, Professor at the varsity, said, "Using a precision medicine approach reveals previously obscured diagnostic signatures and reactivation risk potential." The new diagnostic tools will help identify patients with the highest risk of reactivation and will benefit from therapy, said the researchers.