On World TB Day, researchers reveal that advanced imaging technologies can detect evidence of asymptomatic tuberculosis (TB) in the lungs long before symptoms develop or routine tests return positive results. The study, published in The Lancet Respiratory Medicine, is the largest to date to follow high‑risk individuals using highly sensitive imaging over a five‑year period.
Global estimates suggest that up to one quarter of the world’s population has been infected with Mycobacterium tuberculosis. However, this figure is based largely on inference from tests showing an immune response to the bacterium, not direct evidence of infection. Most people who test positive on immune response tests never develop the disease. Accurately identifying who is most at risk remains one of the most urgent gaps in TB prevention.
Identifying those most likely to develop TB is crucial if we want to prevent transmission and intervene earlier
The research team followed 250 HIV‑negative, asymptomatic adults in Khayelitsha, Cape Town, South Africa, all of whom were household contacts of drug‑resistant TB cases. Each participant underwent a PET‑CT scan and a digital chest X‑ray interpreted by artificial intelligence (AI) tools, and was monitored for up to five years.
During the follow‑up period, 18 participants were diagnosed and treated for TB. Six were detected early through enhanced screening at enrolment, five of whom would have been missed by routine rapid molecular testing. The remaining 12 cases were diagnosed after an average of three years. Several individuals were still asymptomatic when the bacterium was detected in their sputum, highlighting that transmission may occur before routine systems detect disease.
PET‑CT, the most sensitive imaging tool available for research, revealed a wide range of lung abnormalities. Participants whose PET‑CT scans showed a specific pattern of TB‑associated abnormalities at baseline were more than 28 times more likely to progress to TB than those with normal scans. Although 205 of the 250 participants had an immune response to the bacterium, it was the 29 individuals with lung abnormalities on PET‑CT who were at the highest risk.
“These findings position PET‑CT as a powerful research tool for understanding how TB develops within the body,” said co‑senior author Associate Professor Anna Coussens. “While the method is too costly and complex for public health screening, its precision offers valuable insight for designing better diagnostics and treatments.”
The study also highlights the promise of AI‑interpreted chest X‑rays. Although less sensitive than PET‑CT, AI readings aligned closely with PET‑CT‑based risk predictions, indicating strong potential for wider, scalable use in screening programmes.
AI‑read chest X‑rays could play a vital role in strengthening TB control strategies through mass‑screening efforts,
The findings mark a significant step forward: the ability to identify those most likely to develop TB long before symptoms emerge. Earlier detection could enable more timely treatment, reduce transmission, protect lung health, and improving global TB prevention efforts.
Professor of Infectious Diseases
MRC Clinical Trials Unit & Institute for Global Health
Professor Hanif Esmail is a clinical academic specialising in tuberculosis (TB) and infectious diseases. He holds a dual appointment at the MRC Clinical Trials Unit and the Institute for Global Health at University College London, where he leads translational research and clinical trials aimed at improving TB diagnosis, treatment, and prevention.