Tuberculosis is difficult to treat because it requires a long course of antibiotics, often lasting six months or more, to fully eliminate the bacteria. Drug-resistant TB is even more challenging, as it doesn’t respond to standard treatments and needs more expensive and prolonged therapy. In remote areas of the world, which don’t have access to a hospital, this can be even more difficult.

Here in Uganda, we are trying to change that, using new technology from Delft Imaging to screen and diagnose TB in remote areas. This includes 22 units, with 17 portable X-ray systems powered by AI and five mobile TB clinics equipped with GeneXpert machines and AI-driven digital X-ray equipment. These devices can detect TB, even in early stages that traditional methods might miss.

Our work is leading the way in improving healthcare in Uganda by using computer-aided diagnosis and LabXpertDS, a tool that helps connect different types of diagnostic data. This makes it easier to link patients from AI-based detection directly to further testing and treatment. By integrating data from different diagnostic tools, we get a complete view of a patient’s health and resistance patterns. This ensures that no issues are overlooked, leading to quicker and more accurate treatments.

Challenges to Using AI in TB Diagnosis

While AI can greatly improve TB diagnosis in Uganda, there are some challenges. Many healthcare facilities, especially in rural areas, don’t have reliable internet needed to leverage data from AI effectively. Also, deploying AI technology can be expensive, which is a big problem in places where healthcare budgets are already limited.

Another challenge is the lack of clear rules and regulations. Without these, it’s hard to ensure patient safety and data privacy, which can slow down the use of AI. Additionally, healthcare workers need proper training to use AI tools effectively. It’s not just about having the technology; it’s about knowing how to use it well.

Possible Solutions

To overcome these challenges, a thoughtful approach is needed. One good strategy could be to create partnerships between the government, private companies, and international organizations. By working together, they can combine resources and knowledge to find better solutions.

Developing policies is also important. We need to push for regulations that not only support the use of AI but also make sure it benefits patients while protecting their data. Uganda has already started this process by forming a group focused on AI.

Finally, investing in training is crucial. Healthcare workers need to be trained in using AI tools and understanding the results. This can be done through workshops, online courses, and hands-on training to make sure the technology is used effectively to improve TB diagnosis and treatment.

Great potential

AI has the potential to greatly improve TB diagnosis in Uganda. Working together through partnerships, creating better policies, and providing training can really help us make a difference in the fight against tuberculosis and in improving lung health overall.

Not a BSAC Member? Sign up today! Membership is free – see here to begin your BSAC journey.