Benefits of artificial intelligence versus human-reader in chest X-ray screening for tuberculosis in the Philippines

Journal article

Background: Since 2017, the Philippines Business for Social Progress (PBSP) has implemented active case finding for Tuberculosis under their Advancing Client-centered Care and Expanding Sustainable Services for TB (ACCESSTB) project. This study aims to conduct a comparative analysis of a screening approach using AI for Chest X-ray (CXR) interpretation versus an approach relying solely on human-readers in three major regions of the Philippines.

Methods: This study undertook a retrospective analysis of data derived from two well-established and ongoing screening approaches. The data on number of people screened and the outcome of the screening at each stage of the screening process was extracted from quarterly reports provided by PBSP. Subsequently, the data was analysed to determine the diagnostic yield, the number needed to screen and the drop-out rates.

Results: The AI screening approach had a lower number needed to screen (26.3) compared to human-reader screening (41.5). The main reason driving this difference is the lower drop-out rate after CXR (16.6% in AI approach versus 43.1% in human reader approach). This lower drop-out rate is attributed to the quicker turnaround time for CXR results and this has an important public health benefit because a higher proportion of positive TB individuals participating in the screening will receive treatment in the AI screening approach (3.8% versus 2.4%).

Conclusion: The results illustrate that AI-powered CXR screening has clear benefits compared to screening with human readers. Further research is required to determine the comparative cost-effectiveness of the two screening approaches.

Authors: Nampewo, I; Ariana, P; Vijayan, S; Holthof, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Galore Knowledge Publication
• Journal: International Journal of Health Sciences and Research
• Volume: 14
• Issue: 2
• Pages: 277-287
• Publication date: 2024-02-17
• DOI: 10.52403/ijhsr.20240237
• ISSN: 2249-9571
• Language: English
• Keywords: tuberculosis; active case finding; CXR screening; artificial intelligence; benefit