Innovative Approaches to Tuberculosis Screening and Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1133

Special Issue Editors


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Guest Editor
Department for HIV, Tuberculosis, Hepatitis & Sexually Transmitted Infections (HTH), World Health Organization, CH-1211 Geneva, Switzerland
Interests: tuberculosis; public health

E-Mail Website
Guest Editor
Department for HIV, Tuberculosis, Hepatitis & Sexually Transmitted Infections (HTH), World Health Organization, CH-1211 Geneva, Switzerland
Interests: tuberculosis; informatics; surveillance; monitoring; drug-resistant TB

E-Mail Website
Guest Editor
Department for HIV, Tuberculosis, Hepatitis & Sexually Transmitted Infections (HTH), World Health Organization, CH-1211 Geneva, Switzerland
Interests: tuberculosis

Special Issue Information

Dear Colleagues,

Screening and diagnosis play a critical role in the prevention and care of tuberculosis (TB), which remains one of the major communicable diseases of global public health importance (11 million new TB patients per year) and the single leading cause of death from an infectious agent worldwide (1.5 million TB deaths annually). Each year close to three million TB patients go undiagnosed or unreported, presenting a challenge to many countries to reduce their TB burden.

After many decades of stagnation in the technologies used to screen, detect and confirm TB, the last 15 years have seen an encouraging expansion in new TB diagnostics appearing on the market. These include molecular and other rapid biomarkers that can be used on a variety of biological specimens; interferon gamma release assays for the detection of TB infection; and low-radiation ultraportable equipment, bringing chest X-ray within reach of more populations. The application of artificial intelligence to interpret chest X-ray imaging and the signatures of cough and stethoscope sounds for TB is another promising frontier that is already being operationalized and scaled up in low-resource, high-burden settings. These developments have been matched by new treatments and care for drug-susceptible and drug-resistant TB and TB preventive treatment (TPT). The World Health Organization helps countries implement diagnostics of proven accuracy, guides manufacturers to develop technologies that better address the needs of users, and helps shape the research agenda.

As Guest Editors on this Special Issue, we invite leading researchers to contribute studies on new approaches for TB screening and diagnosis. A collection of articles with a broad scope of research will allow readers to appreciate the dynamic nature of screening and diagnostics for TB. The contributions being targeted will include, amongst others, research related to molecular diagnostics, the clinical chemistry of biomarkers, immunology, imaging, analysis of chest sounds and computer-aided detection employing artificial intelligence. We also encourage reports on the implementation of innovations, such as the cost-effectiveness of different testing strategies and information systems and field studies on the feasibility, accessibility, end-user values and equity and linkage to care of innovative approaches to the screening and diagnosis of TB disease, TB infection or post-TB impairments. We look forward to hearing from you.

Dr. Nguyen Nhat Linh
Dr. Dennis Falzon
Dr. Alexei Korobitsyn
Guest Editors

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Keywords

  • tuberculosis
  • diagnosis
  • screening
  • genomics
  • biomarkers
  • artificial intelligence
  • linkage to care

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Published Papers (1 paper)

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Review

22 pages, 882 KB  
Review
Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation
by Hien Thi Thu Nguyen, Vang Le-Quy, Anh Tuan Dinh-Xuan and Linh Nhat Nguyen
Diagnostics 2026, 16(8), 1127; https://doi.org/10.3390/diagnostics16081127 - 9 Apr 2026
Viewed by 825
Abstract
Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and [...] Read more.
Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care. Full article
(This article belongs to the Special Issue Innovative Approaches to Tuberculosis Screening and Diagnosis)
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