Location: Nigeria
Introduction
Lagos accounts for approximately 10% of Nigeria’s TB burden. It has piloted first-of-its-kind facility-based TB case finding using digital X-ray and AI-powered CAD4TB to close the TB notification gap.
Intervention
- With the support of the Global Fund, 18 CAD4TB systems were deployed to 18 public and private facilities in Lagos, including general hospitals, tertiary hospitals, primary healthcare centers, and private health facilities.
- All the facilities have existing CXR infrastructure and human resources.
Result
- Between January 2022 and December 2023, a total of 95,770 individuals were screened by CAD4TB, and 17% (16,572) were identified as presumptive TB cases (CAD4TB score ≥45).
- A further evaluation was conducted on 16,023 individuals, resulting in the diagnosis of 4,969 TB cases, yielding a 5% TB rate.
- The number needed to screen (NNS) was 19, and the number needed to test (NNT) was 3.
Conclusion
- The assessment indicates that using AI-enabled CAD4TB for facility-based TB screening enables quality presumptive identification with a high TB yield and good NNS/NNT.
- The intervention has significantly contributed to the overall increase in TB case finding in Lagos. Scaling up this approach to bridge the TB case notification gap is recommended.
REFERENCE: Sokoya, O. et al (2024, November 12-16). Implementing facility-based artificial intelligence enable chest X-ray screening as innovative strategy to improving TB case finding in Lagos, Nigeria [Presentation]. The Union World Conference on Lung Health, Bali, Indonesia.
