A 2024 study in Punjab, Pakistan, utilized AI-enhanced mobile X-ray screening to detect tuberculosis (TB) among brick kiln workers, revealing a prevalence of 1.17%, significantly higher than national averages. This research highlights the critical need for targeted TB interventions in high-risk occupational groups and demonstrates the potential of AI in advancing TB diagnostics and treatment strategies.Background: Tuberculosis (TB) remains a major public health challenge in Pakistan, with an incidence of 277 per 100,000 population in 2023 [1]. Brick kiln workers, exposed to silica dust and poor ventilation, face amplified TB risks but are not classified as high-risk under current policies. In diagnostics, AI-powered tools like Computer-Aided Detection for TB (CAD4TB) have shown high accuracy in identifying TB from chest X-rays, making them valuable for active case finding in resource-limited settings [2].Design/Methods: In 2024, a mobile X-ray screening initiative was conducted in Punjab, Pakistan, targeting brick kiln workers. Using mobile X-ray vans equipped with CAD4TB, 53,188 brick kiln workers were screened. Individuals with abnormal radiographs underwent GeneXpert MTB/RIF testing. Comparative data were collected from 78,560 individuals screened in general community hotspots using similar methods.Results: The study found a TB prevalence of 1.17% (624/53,188) among brick kiln workers, which is 4.2 times higher than the national incidence (0.28%) and 4.5 times higher than the provincial average (0.27%). In comparison, community hotspots showed a prevalence of 1.40% (1,100/78,560). Brick kiln workers had a higher diagnostic yield, with 18.7% GeneXpert positivity compared to 22.0% in hotspots.Conclusions: This study underscores the effectiveness of AI in TB diagnostics for high-risk populations and advocates for the reclassification of brick kiln workers as a high-risk group. Moreover, it illustrates the broader potential of AI in TB research, from optimizing treatment regimens to accelerating the discovery of new drugs and vaccines.MetricBrick Kiln WorkersCommunity HotspotsNational Incidence (Pakistan)Provincial Incidence (Punjab)Sample Size53,18878,560TB Prevalence1.17% (624 cases)1.40% (1,100 cases)0.28%0.27%Gene Xpert Positivity Rate18.7%22.0%Prevalence vs National Incidence4.2x higher5.0x higher

A 2024 study in Punjab, Pakistan, used AI-powered mobile X-ray screening (CAD4TB) to detect TB among 34,500 coal miners, revealing a prevalence of 522 per 100,000—nearly double the national incidence. This underscores AI’s role in early TB detection and supports targeted interventions.Background: Tuberculosis (TB) poses a significant occupational health risk for coal miners in Pakistan, with Punjab accounting for 55–60% of the national TB burden (277 per 100,000) [1]. Punjab province accounts for approximately 55-60% of this burden, with 290,000 cases reported in 2024 (Provincial TB Control Program, Punjab, 2024). Miners face heightened risks from silica dust exposure and limited healthcare access. Globally, TB prevalence in mining communities ranges from 1,200–2,500 per 100,000 [2]. This study evaluates AI-assisted active case finding (ACF) using mobile X-ray screening with CAD4TB to enhance early TB detection in this high-risk group.Design/Methods: In 2024, a cross-sectional study screened 34,500 coal miners across Punjab using mobile X-ray units with CAD4TB, a deep-learning tool for TB detection. X-rays scoring ≥60 were flagged for GeneXpert MTB/RIF testing. Risk factors, including silica exposure and healthcare access, were documented, aligning with WHO screening guidelines [3].Results: Of 7,260 X-rays analyzed, 1,704 (23.5%) were flagged, leading to 180 confirmed TB cases (prevalence: 522 per 100,000), nearly double the national incidence (277 per 100,000). The case detection rate was 10.6%, with silica exposure and poor healthcare access as key risk factors, consistent with global mining trends [2,4].Conclusions: The high TB prevalence among coal miners supports their classification as a high-risk group under WHO guidelines. AI-driven mobile screening offers a scalable, high-impact approach for early detection. Policy recommendations include silica dust mitigation and nationwide AI-assisted ACF, aligning with WHO’s End TB Strategy to enhance Pakistan’s TB control [5].​ ParameterValue / FindingStudy PopulationCoal Miners, PunjabTotal Individuals Screened34,500Individuals with CXR suggestive of TB (CAD4TB Score ≥60)1,704 (4.94% of screened)Bacteriologically Confirmed TB Cases (via Xpert MTB/RIF)180Yield among Presumptive Cases (Confirmed TB / CAD4TB ≥60)10.6%Prevalence of Confirmed TB among Screened Coal Miners522 per 100,000National TB Incidence (Pakistan, WHO 2024)277 per 100,000Relative Burden in Coal Miners vs. General Population1.89 times higherKey Technology UtilizedMobile Digital X-ray, CAD4TB (AI), GeneXpert MTB/RIF