ArticlePhysiological measurement2021
Automatic cough classification for tuberculosis screening in a real-world environment.
Article in Physiological measurement, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 5 of them syntheses that pooled 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.
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.
Who cites it
32 citing papers in PubMed, 5 syntheses or guidelines pooled it, 5 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- AI Based Cough Analysis for Pulmonary Tuberculosis Triage and Diagnosis: A Technical Review.npj biomedical innovations · 2026Review
- External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.Scientific reports · 2026Article
- Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification.Sensors (Basel, Switzerland) · 2026Article
- Conversational speech for respiratory triage in primary care: a pilot study.Frontiers in medicine · 2026Article
- A cross sectional feasibility study to evaluate the usability and efficacy of Swaasa AI platform for rapid respiratory health assessment.Scientific reports · 2025Article
- Tuberculosis Detection from Cough Recordings Using Bag-of-Words Classifiers.Sensors (Basel, Switzerland) · 2025Article
- Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results From the CODA TB DREAM Challenge.Open forum infectious diseases · 2025Article
- Use of artificial intelligence in healthcare in South Africa: A scoping review.Health SA = SA Gesondheid · 2025Review
- Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes.NPJ cardiovascular health · 2024Article
- Fused Audio Instance and Representation for Respiratory Disease Detection.Sensors (Basel, Switzerland) · 2024Article
- Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.JMIR research protocols · 2024Article
- Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset.medRxiv : the preprint server for health sciences · 2024Article
- TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.Science advances · 2024Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- Feature evaluation of accelerometry signals for cough detection.Frontiers in digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.