In a world where artificial intelligence is rapidly reshaping how we work, research, and solve health challenges, Zambart’s POCUS4TB team is actively exploring how these innovations can be meaningfully integrated into tuberculosis (TB) screening. As part of this journey, the team recently held a knowledge sharing meeting at Zambart House, bringing together staff from different departments as well as others from Ridgeway to strengthen shared understanding and spark new ideas.
The session focused on sharing key insights from an AI and digital transformation in health care course that was held in Cambridge and attended by the two POCUS4TB PhD students Kondwelani Mateyo and Mwiza Nyasa.The aim of this knowledge sharing was, to share key insights such as making the concepts practical, relevant, and easy to apply in health research.
The objectives of the meeting were to:
Share the most relevant and easy-to-understand concepts from the AI and Health course.
Provide simple examples of how AI can support ongoing work.
Highlight key challenges and opportunities associated with the use of AI.
Encourage discussion on how AI could be applied within current projects.
This engagement forms part of the POCUS4TB (Point-of-Care Ultrasound for Tuberculosis) initiative, a forward-looking project focused on developing and evaluating an artificial intelligence (AI)-supported point-of-care ultrasound (AI-POCUS) solution. The initiative aims to improve the reach, quality, and efficiency of TB screening, particularly among vulnerable adults..
In the current phase of the project, the team is assessing a range of portable handheld ultrasound devices to identify the most suitable option for field use. Alongside this, two scanning protocols—the 16-zone and 5-zone approaches are being evaluated to determine the most effective and practical method for TB detection.
In parallel, ultrasound images are being systematically collected to support the development of an AI algorithm. These images are shared with the collaborating partner Delft, (Netherlands), where they are used to train and refine the model, with the goal of improving the accuracy, scalability, and performance of TB screening.
Overall, the POCUS4TB initiative reflects Zambart’s commitment to leveraging cutting-edge research and strategic collaboration to address pressing public health challenges. By integrating AI with portable diagnostic technologies, the project has the potential to transform TB screening, making it more accessible, efficient, and responsive to community needs.