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Artificial intelligence on chest X-ray for tuberculosis screening in Tanzania: a multicentre evaluation
Deogratias Mzurikwao1,2, Twaha Kabika3,4, Asa Kalonga4
1Department of Biomedical Engineering, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. dmzurikwao@gmail.com.
BMC Infectious Diseases
|May 2, 2026
Summary
Tanzania
Area of Science:
- Public Health
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Tanzania utilizes AI-assisted chest X-ray screening for tuberculosis (TB) detection using CAD4TB version 6.
- GeneXpert is the standard bacteriological test, but national thresholds for AI tool use are lacking.
- Mobile screening units are key for TB case finding in Tanzania.
Purpose of the Study:
- To evaluate the implementation and operational use of CAD4TB version 6 in Tanzanian mobile TB screening units.
- To identify challenges impacting the effective deployment of AI-assisted screening.
- To assess workflow inconsistencies and data completeness in AI-assisted TB screening.
Main Methods:
- Retrospective analysis of screening data from 11,923 individuals in mobile clinics.
- Comparison of manual X-ray interpretation, CAD4TB scores, and GeneXpert results.
- Assessment of AI tool integration within existing TB screening protocols.
Main Results:
- Substantial inconsistencies in screening workflows were observed across sites.
- Non-uniform application of CAD4TB and GeneXpert testing, missing records, and protocol deviations were noted.
- While CAD4TB scores generally aligned with GeneXpert-positive cases, diagnostic accuracy could not be determined due to data limitations.
Conclusions:
- The study highlights significant gaps in protocol adherence, data completeness, and workflow standardization for AI-assisted TB screening.
- Further prospective, protocol-driven studies are needed to establish validated national thresholds for CAD4TB use in Tanzania.
- Operational improvements are crucial for the effective implementation of AI tools in public health initiatives.
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