Evidence mapPaperPMID 42404694Full record

ReviewInternational journal of MCH and AIDS2026

A Systematic Review and Meta-analysis on Innovative Approaches in Tuberculosis Diagnosis: Challenges and Future Directions.

Ahmad Abdulhadi, Nasiru Abdullahi, Ibrahim Yusuf, Basheer I Waziri, Kasimu Mamuda, Husna F Ibrahim, Khadija Muhammad, Maryam M Ibrahim, Aisha A Abdullahi, Hassan A Murtala and 2 more

Abstract readReview
In one paragraph

Review in International journal of MCH and AIDS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ahmad AbdulhadiDepartment of Microbiology, Division of Tuberculosis and Antimicrobial Resistance Research, Kano Independent Research Center Trust, Nigeria.
Nasiru AbdullahiDepartment of Genomics and Molecular Biology Research, Kano Independent Research Center Trust, Nigeria.ORCID https://orcid.org/0000-0002-8145-3399
Ibrahim YusufDepartment of Microbiology, Division of Tuberculosis and Antimicrobial Resistance Research, Kano Independent Research Center Trust, Nigeria.
Basheer I WaziriDepartment of Genomics and Molecular Biology Research, Kano Independent Research Center Trust, Nigeria.ORCID https://orcid.org/0009-0003-1832-5786
Kasimu MamudaDepartment of Microbiology, Division of Tuberculosis and Antimicrobial Resistance Research, Kano Independent Research Center Trust, Nigeria.
Husna F IbrahimDepartment of Microbiology, Division of Tuberculosis and Antimicrobial Resistance Research, Kano Independent Research Center Trust, Nigeria.
Khadija MuhammadDepartment of Genomics and Molecular Biology Research, Kano Independent Research Center Trust, Nigeria.
Maryam M IbrahimDepartment of Genomics and Molecular Biology Research, Kano Independent Research Center Trust, Nigeria.
Aisha A AbdullahiDepartment of Epidemiology and Population Health, Kano Independent Research Center Trust, Nigeria.ORCID https://orcid.org/0009-0005-5816-9399
Hassan A MurtalaDepartment of Epidemiology and Population Health, Kano Independent Research Center Trust, Nigeria.ORCID https://orcid.org/0009-0004-2827-4545
Muhammad A AbbasKano Center for Disease Control and Prevention, Nigeria.
Hamisu M SalihuDepartment of Epidemiology and Population Health, Kano Independent Research Center Trust, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Tuberculosis (TB) has continued to be one of the global threats, affecting millions of individuals globally, including the maternal and child health (MCH) populations and individuals with HIV/AIDS. TB infected 10.8 million individuals, leading to 1.25 million deaths across the globe annually, leaving 4 million missing cases contributing to the global TB burden. This study aims to unveil innovative approaches to TB diagnosis, challenges, and the future direction of this field. Methods: A comprehensive literature search was conducted to retrieve studies published between 2019 and 2024 from PubMed, Google Scholar, and Web of Science. The studies were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and screened using the Rayyan tool. The quality and risk of bias of the included studies were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 for diagnostic accuracy studies and Strengthening the Reporting of Observational Studies in Epidemiology for observational studies. A random effects model was employed to calculate the pooled sensitivity and specificity, while Egger's test and funnel plots were utilized to evaluate publication bias. R and MetaDTA software were used for all the statistical analyses. Results: The review included 25 studies with sample sizes ranging from 100 to 6,520 participants and assessed various innovative approaches for diagnosing TB, including molecular methods, biomarker-based techniques, and artificial intelligence (AI) applications. The most common approach was molecular testing, specifically cartridge-based tests. The overall sensitivity of these methods was 0.79 (95% confidence interval [CI]: 0.70-0.86), with a heterogeneity index (I²) of 92.9% and a Conclusion and Global Health Implications: This review reinforces the promise of innovative diagnostic methods, especially cartridge-based molecular tests, for improving TB detection. However, moderate sensitivity and high heterogeneity emphasize the need for cautious implementation and further validation of new tools such as the AI-based and biomarker-based. Strategic investment in research and contextual deployment is critical for closing the TB diagnosis gap globally.

Indexed as

DiagnosisInnovative ApproachesReviewScreeningTuberculosis

Identifiers

PMID42404694
PMCPMC13330734

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.