Advancing the fight against tuberculosis: integrating innovation and public health in diagnosis, treatment, vaccine development, and implementation science

Abstract

Tuberculosis (TB) remains one of the leading causes of infectious disease mortality worldwide, increasingly complicated by the emergence of drug-resistant strains and limitations in existing diagnostic and therapeutic strategies. Despite decades of global efforts, the disease continues to impose a significant burden, particularly in low- and middle-income countries (LMICs) where health system weaknesses hinder progress. This comprehensive review explores recent advancements in TB diagnostics, antimicrobial resistance (AMR surveillance), treatment strategies, and vaccine development. It critically evaluates cutting-edge technologies including CRISPR-based diagnostics, whole-genome sequencing, and digital adherence tools, alongside therapeutic innovations such as shorter multidrug-resistant TB regimens and host-directed therapies. Special emphasis is placed on the translational gap-highlighting barriers to real-world implementation such as cost, infrastructure, and policy fragmentation. While innovations like the Xpert MTB/RIF Ultra, BPaLM regimen, and next-generation vaccines such as M72/AS01E represent pivotal progress, their deployment remains uneven. Implementation science, cost-effectiveness analyses, and health equity considerations are vital to scaling up these tools. Moreover, the expansion of the TB vaccine pipeline and integration of AI in diagnostics signal a transformative period in TB control. Eliminating TB demands more than biomedical breakthroughs-it requires a unified strategy that aligns innovation with access, equity, and sustainability. By bridging science with implementation, and integrating diagnostics, treatment, and prevention within robust health systems, the global community can accelerate the path toward ending TB.

Keywords: diagnostic innovation; public health; therapeutic strategies; tuberculosis; vaccine development.

Figure 1

Pathogenesis of M. tuberculosis: from initial infection to reactivation. Following inhalation, M. tuberculosis is phagocytosed by alveolar macrophages, where it employs the ESX-1 secretion system to escape phagosomal destruction. This triggers granuloma formation as the host attempts to contain the infection. Control may be established, leading to latent infection, or the host may progress to active disease. Impaired immune responses or external stressors can disrupt granuloma integrity, leading to reactivation. Necrotic granulomas facilitate bacillary dissemination and renewed transmission.

Figure 2

Overview of diagnostic methods for TB. This schematic illustrates the major diagnostic approaches for TB, including culture-based methods (solid and liquid media), microscopy (Ziehl-Neelsen and fluorescence staining), molecular diagnostics—such as nucleic acid amplification tests (NAATs), whole genome sequencing (WGS), CRISPR-based assays, and mass spectrometry—as well as immunological tools (e.g., interferon-gamma release assays, lipoarabinomannan (LAM) detection, and gold nanoparticles (GNPs)-based tests). These platforms enable early detection, resistance profiling, and improved disease management, especially in high-burden settings.

Figure 3

NAATs for tuberculosis diagnosis. This schematic illustrates the major nucleic acid amplification platforms used for TB detection, including Loop-mediated isothermal amplification (LAMP), Xpert MTB/RIF, digital PCR, Hain GenoType Line Probe Assay, Anyplex MTB/NTM assay, and RAPD. These assays improve diagnostic accuracy, speed, and resistance profiling, and are central to early TB case detection and treatment guidance.

Figure 4

Schematic illustration of the LAM Assay principle. This diagram outlines the core steps of the LAM-based lateral flow immunoassay used for TB diagnosis. Following urine collection, LAM antigens bind to anti-LAM antibodies conjugated with a detection label. As the sample migrates along the test strip, the presence of LAM is indicated by a colored test line, while a control line ensures assay validity. The interpretation panel distinguishes between positive (both lines visible) and negative (only control line visible) results. This generic schematic avoids proprietary device branding and reflects the underlying principle applicable across LAM assay platforms.

Figure 5

Artificial intelligence applications in tuberculosis diagnostics. This infographic illustrates the integration of AI across three major TB diagnostic platforms. Left: AI-assisted microscopy uses deep learning to detect AFB in smear samples. Center: AI-driven radiographic systems, such as CAD4TB, analyze chest X-rays for automated TB screening. Right: AI algorithms applied to WGS data enable rapid prediction of drug resistance mutations in M. tuberculosis. At the center, an AI microchip symbolizes the convergence of machine intelligence in advancing TB diagnostics across modalities.

Figure 6

Host genetics and microbiome interactions shaping TB outcomes. The schematic highlights how host genetic variants (e.g., TLR1, VDR, NAT2) and the composition of the gut and lung microbiomes modulate the immune response to M. tuberculosis. These host-specific factors influence infection risk, disease severity, and the success of pharmacologic therapy, underscoring the potential of precision-guided TB management that integrates genomics and microbiome profiling.

Figure 7

The key HDTs that may enhance the efficacy of M. tuberculosis treatment are as follows: (A) Regulating inflammatory mechanisms and mediators is crucial for reducing inflammation and preventing lung tissue damage, ultimately enhancing lung integrity. (B) Strengthening the host immune and memory responses is essential for overall health. (C) Enhancing host bactericidal mechanisms, such as macrophage-mediated killing of M. tuberculosis and inhibiting bacillary growth, is vital for an effective immune response. (D) The disintegration of granulomas and the release of M. tuberculosis bacilli increase exposure to anti-TB medications.

Figure 8

Implementation challenges and strategic solutions for advanced TB technologies. This infographic outlines four major barriers to implementing innovative TB diagnostics and treatments—high costs, limited infrastructure and workforce, fragmented healthcare systems, and regulatory or funding hurdles—and pairs each with corresponding solutions. These include cost-effectiveness analysis, training programs, service integration, and policy advocacy, all of which are critical to ensuring the real-world success of TB innovations in diverse health system contexts.