Breakthrough research presented at The Union World Conference on Lung Health in Bali, Indonesia showcased how artificial intelligence (AI) and computer-aided detection (CAD) technology are transforming tuberculosis (TB) detection across Africa.
These innovations are significantly improving active case finding (ACF) efforts, revealing TB incidence rates far higher than national estimates and offering a pathway to more effective disease control.
Revolutionizing Detection with AI
Studies from Ethiopia, Kenya, and Nigeria demonstrated the remarkable impact of AI-powered screening in identifying TB cases that might otherwise go undetected.
In Ethiopia, researchers from REACH Ethiopia screened over 12,000 people in urban slums, rural areas, and pastoralist communities using chest X-rays enhanced by AI detection. Individuals flagged for TB-suggestive symptoms or X-ray abnormalities underwent bacteriological testing.
The study revealed TB incidence rates of 927 cases per 100,000 population—nearly seven times higher than national estimates. The findings highlighted the sensitivity of AI-assisted X-ray screening, which outperformed symptom-based methods in detecting TB.
In Nigeria, researchers from KNCV Nigeria used AI-enabled portable digital X-ray tools to retrospectively analyze data from nearly 26,000 individuals. Presumptive cases were referred for further testing using GeneXpert technology. The study emphasized AI’s ability to identify subtle, subclinical lung lesions, which are often missed by traditional methods, making it a crucial tool in closing TB detection gaps among high-burden populations.
In Kenya, researchers integrated AI-equipped digital chest X-ray tools into the country’s targeted outreach programs. Funded by USAID under the “Introducing New Tools” project, the program reached nearly 16,000 individuals.
The study reported a high TB positivity rate among those with elevated CAD scores, demonstrating the tools’ effectiveness in streamlining community-based screenings and enabling early detection of TB.
“Our findings highlight the transformative potential of AI-enabled digital chest X-ray tools in community-based TB screening programs,” said lead researcher Rhoda Karisa. “By leveraging this innovation, countries can enhance early detection of TB and move closer to eliminating the disease.”
Call for Greater Action
Experts emphasized that scaling up the deployment of AI and CAD technologies is essential to maximizing their impact. Dr. Cassandra Kelly-Cirino, Executive Director of the International Union Against Tuberculosis and Lung Disease (The Union), underscored the urgency of widespread implementation.
“Innovative technologies, such as AI and computer-aided detection, have the potential to change the landscape for TB detection,” she said. “By enabling earlier diagnosis, these tools help patients access treatment faster and reduce transmission.
“However, innovation in healthcare is only as valuable as the number of people it reaches. To fully realize the potential of AI and CAD in TB detection, we must expand their use, secure funding, and implement them globally. Research is proving their efficacy—it’s up to us to act.”
Looking Ahead
The studies presented at the conference demonstrate how AI and CAD technology can revolutionize TB detection in high-burden settings, providing a blueprint for integrating these innovations into national TB programs.
With sustained investment and strategic deployment, these tools could play a pivotal role in achieving the goal of eliminating TB as a global health threat.