The AddiCAD project, a R46 million international research endeavor, is poised to enhance TB detection with a combination of AI-powered chest X-ray analysis and a biomarker blood test.
The project’s objectives are clear: to improve the specificity of TB diagnosis by 20% over AI imaging alone, while also reducing reliance on sputum-based testing, which is time-consuming and often inaccurate. This innovative approach is particularly significant in under-resourced areas where access to healthcare is limited.
In parallel, the CAD LUS4TB project, also led by SU, is a €10 million international trial that leverages AI to improve TB diagnosis. This project uses handheld ultrasound and smartphones to facilitate diagnosis, bringing together 10 health and research institutions from Africa and Europe.
The CAD LUS4TB initiative is funded by the EU’s Global Health EDCTP3 Joint Undertakings.
Prof Grant Theron, trial coordinator for CAD LUS4TB, has highlighted the pressing need for scalable, accessible diagnostic tools to combat TB, which remains the world’s deadliest infectious disease. “We often test the wrong people at the wrong time. “
The AddiCAD project, which spans from May 2026 to April 2029, is part of a larger effort to enhance the detection of TB, a disease that affects an estimated 10 million people annually, with around a quarter never formally diagnosed.
The project is expected to validate its technology in about 1,000 patients.
The AI — driven diagnostic tools are designed to offer a faster and potentially more accurate way to identify individuals with TB, thereby improving patient outcomes and reducing the spread of the disease. Stellenbosch University’s role in these projects underscores the institution’s commitment to addressing critical global health challenges.
The implementation of AI in TB diagnosis is a significant step forward in the fight against the disease. It promises to transform the landscape of TB detection, providing healthcare workers with a powerful new tool in the fight against this infectious disease.
As the projects progress, they will likely set new standards for TB diagnosis across Africa and beyond.
What remains to be seen is how these AI — powered tools will be integrated into existing healthcare systems and whether they will achieve the desired improvements in diagnosis rates.
As the projects advance, their outcomes will be closely watched by healthcare professionals and policymakers alike. Sources indicate that the success of these projects will depend on the quality of the AI algorithms developed and the ease with which they can be implemented in real-world settings.
Stellenbosch University’s leadership in these initiatives is a testament to the institution’s dedication to using technology to improve global health outcomes.
As the AddiCAD and CAD LUS4TB projects continue to evolve, they are not only poised to transform TB diagnosis in Africa but also to serve as a model for how AI can be effectively employed in healthcare settings worldwide.
*Additional reporting by ImNews | Sources consulted: 5*
—
This original article was produced by the ImNews editorial team
Source: Google News v2
