Nigeria – Finding TB cases at private health facilities using CAD4TB
In Lagos, facility-based TB screening using digital chest X-ray with AI-powered CAD4TB was piloted across public and private health facilities. The intervention achieved high screening volumes, efficient presumptive identification, and strong TB yield, contributing to closing the TB notification gap.
High-urban settings
Lagos accounts for a significant proportion of Nigeria’s TB burden, requiring innovative approaches to improve case notification.
Global Fund – supported deployment
Eighteen CAD4TB systems were deployed with support from the Global Fund.
Deployment across public and private facilities
Systems were installed across general hospitals, tertiary hospitals, primary healthcare centers, and private facilities in Lagos.
Use of existing infrastructure
All participating facilities had chest X-ray infrastructure and trained health personnel in place.
Between January 2022 and December 2023,
- 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.
- 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.