By Joyce Ojanji
Tuberculosis (TB) experts have called for greater TB surveillance and wider use of new screening technologies in high-burden communities, revealing how new technology is enhancing active case finding (ACF) efforts in communities across Africa.
Breakthrough research from Ethiopia, Kenya, and Nigeria presented at The Union World Conference on Lung Health demonstrated the role of innovative technology in driving these efforts, slowing the progression of TB in these countries.
From Ethiopia, researchers presented on the Registration, Evaluation, Authorisation and Restriction of Chemical (REACH) research focusing on how Artificial Intelligence (AI)-powered TB screening is being used across urban slum, rural, and pastoralist communities to detect TB across the country.
Over 12,000 people in Ethiopia were screened using a chest X-ray supported by AI detection, with individuals either presenting with TB-suggestive symptoms or X-ray findings suggestive of TB then invited for bacteriological testing.
TB-suggestive symptoms were detected in 8.5% of community members screened, with the TB incidence rate found to be 927 per 100,000 population – nearly seven times higher than the national estimation.
The researchers concluded that AI-assisted X-ray screening exhibited notable sensitivity, surpassing symptom-based screening and significantly contributing to case detection rates in Ethiopia.
Additionally, experts from Koninklijke Nederlandse Centrale Vereniging (KNCV) Nigeria presented separate research on how AI-aided screening of non-symptomatic groups is helping to close the TB case finding gap – with their study results indicating that surveillance for TB among high-burden populations should be increased.
Data from AI-supported scans of nearly 26,000 individuals from across different communities were retrospectively examined using an AI-aided portable digital X-ray tool. Presumptive cases were sent for further examination using GeneXpert, with radiologists also interpretating scans to make clinical diagnoses.
Therefore, the researchers concluded that AI is playing a significant role in TB programming by helping to detect subtle, subclinical lung lesions earlier – which likely would have otherwise remained undiagnosed.
Moreover, Kenya based researchers illustrated how eight digital chest X-ray tools equipped with Computer-Aided Design (CAD) software could provide a cost-effective strategy for early active TB case identification – particularly in high-burden countries such as Kenya. The CAD-equipped X-ray tools were used as part of the ‘Introducing New Tools’ project, funded by USAID.
The tools were incorporated into Kenya’s TB targeted outreach programme with screening algorithms and alongside symptom screening for all outreach attendees.
Nearly 16,000 individuals were reached through the programme, which streamlined the process for identifying people with TB whose symptoms required further laboratory investigation. The study also found a high TB positivity rate among individuals with elevated CAD scores, stressing the efficacy of community-based screening efforts using CAD-supported tools in early disease detection.
The researchers concluded that scaling up the deployment of this technology holds promise for significantly enhancing TB detection rates in Kenya – demonstrating the important role that the strategic deployment of innovative tech solutions could play in eliminating the disease.
“Our findings highlight the transformative potential of using AI-enabled digital chest X-ray tools in community-based TB screening programmes.
“By leveraging this innovation, countries can enhance early detection of TB and ultimately move a step closer to the goal of eliminating TB in high-burden countries like Kenya,’’ Lead researcher Rhoda Karisa highlighted.
Dr Cassandra Kelly-Cirino, Executive Director, International Union Against Tuberculosis and Lung Disease (The Union) noted that Innovative technologies, such as AI and computer-aided detection, have the potential to change the landscape for detection of TB, allowing people with the disease to access treatment more quickly and helping to break the chain of transmission.
However, she noted that innovation in healthcare is only as valuable as the number of people it reaches. To fully realise the potential of AI and CAD in TB detection, they must further in using them as widely as possible to help make the elimination of TB a reality. Research is proving the efficacy of these tools – it’s up to us all to take action in funding and implementing them worldwide.
