Evidence map›Paper›PMID 42768162›Full record

Reviewnpj biomedical innovations2026

AI Based Cough Analysis for Pulmonary Tuberculosis Triage and Diagnosis: A Technical Review.

Gutta J Chowdary, Shams Nafisa Ali, Varunya Sakpuntoon, Adrienne E Shapiro, Blanca I Restrepo, Stephen J Pont, Lisa Y Armitige, Leonard Kingwara, Umberto E Villa, Nuttada Panpradist

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in npj biomedical innovations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

10 authors.

Gutta J Chowdary *Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Shams Nafisa Ali *Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Varunya SakpuntoonDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Adrienne E ShapiroDepartments of Global Health and Medicine (Division of Allergy & Infectious Diseases), University of Washington, Seattle, WA, USA.
Blanca I RestrepoDepartment of Epidemiology, UTHealth School of Public Health in Brownsville, Brownsville, TX, USA.
Stephen J PontTexas Department of State Health Services; Departments of Pediatrics and Population Health, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
Lisa Y ArmitigeHeartland National TB Center, San Antonio, TX, USA.
Leonard KingwaraBiomedical Testing and Analytical Services, Kenya National Public Health Institute, Ministry of Health (KNPHI-MoH), Nairobi, Kenya.
Umberto E VillaDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Nuttada PanpradistDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA. nuttadap@utexas.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence-based cough analysis has emerged as a potential non-sputum approach for pulmonary tuberculosis triage and diagnostic support. This technical review synthesizes evidence from cough-based TB studies, highlighting current datasets, AI methods, performance benchmarks, implementation requirements, and translational barriers. We identify key limitations, including dataset bias, recording variability, limited prospective validation, and insufficient evidence in early-stage or asymptomatic TB populations.

Identifiers

PMID42768162
PMCPMC13594303

What OpenQuestion 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 OpenQuestion graph.