Abstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a critical global health challenge, claiming over one million lives annually. Despite substantial progress in reducing TB incidence in developed countries post-World War II, it persists as a major cause of mortality, particularly in low-income populations. Socio-economic disparities significantly contribute to TB prevalence, exacerbating the uneven distribution of resources and healthcare access. Pakistan is among the nations severely impacted by TB, with eradication targets still unmet. All forms of TB are prevalent in the region, presenting symptoms such as fever, chronic cough, chills, and weight loss. This review assesses treatment outcomes and the prevalence of pulmonary TB in diagnosed patients in Pakistan. The airborne transmission of TB significantly drives its global burden. According to the World Health Organization (WHO), 2021 witnessed 1.2 million TB-related deaths and 9.9 million new cases. Diagnostic methods include immunological assays and molecular techniques, requiring sputum and blood sample analyses. Early diagnosis is crucial to prevent the emergence of drug-resistant strains. Pulmonary TB primarily spreads via respiratory pathways, with MTB infecting alveolar macrophages, leading to disease progression. Although studies on genetic susceptibility to TB have been conducted, conclusive evidence remains elusive. Computed Tomography (CT) provides detailed insights into TB manifestations but demands automation due to the high volume of data generated. The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB presents serious challenges to control and treatment. This review offers a comprehensive analysis of the epidemiology, pathogenesis, diagnosis, treatment, and prevention strategies for MDR and XDR TB. It highlights existing research gaps and proposes future directions to address these challenges. Advances in Artificial Intelligence (AI) and Computer Vision (CV) provide novel approaches for automating TB image analysis, facilitating scalable pre-clinical trials and improving disease management.
Published Version
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