Stellenbosch project speeds up TB diagnosis with AI technology
Stellenbosch University is leading the AddiCAD project, which uses AI-powered chest X-rays combined with biomarker blood tests to improve tuberculosis diagnosis. The initiative aims to make accurate TB detection more accessible in resource-limited settings.
Why it matters
Improving TB detection is a global health priority, and this technology could significantly reduce the number of undiagnosed cases in developing regions.
In what is expected to be an important weapon in the fight against tuberculosis, Stellenbosch University is leading AddiCAD , a R46m international project to improve diagnosis by combining AI-powered chest X-ray analysis with a simple fingerstick blood test – the blend of cutting-edge science and real-world usability expected to make accurate diagnosis more accessible where it is needed most.
The project, to speed up detection in settings with limited healthcare access, is being co-ordinated by the university and funded by the Global Health European and Developing Countries Clinical Trials Partnership 3 (Global Health EDCTP3).
One of the major challenges in the fight against TB is effective detection: of the estimated 10.7m new cases each year, around 2.5m people remain undiagnosed.
This is partly because current diagnostic tools are often too expensive, laboratory-dependent or difficult to deploy at the point of care.
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