Tuberculosis remains a global health burden. Ahmad et al. used machine learning to develop an algorithm that distinguished active tuberculosis from other diseases with similar symptoms by measuring expression of four proteins in blood samples. The authors validated their triage test’s discriminatory power using blood samples from subjects with persistent cough across several continents, showing that performance was improved when detection of antibodies against a mycobacterial antigen was added to the panel. These promising results support further development and field testing using a point-of-care format.
