ReviewCommunications medicine2022
Making cough count in tuberculosis care.
Review in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
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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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Cough monitoring systems in adults with chronic respiratory diseases: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.Scientific reports · 2026Article
- The value of continuous cough monitoring: a narrative review.Journal of thoracic disease · 2025Review
- 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
- Development and validation of a risk prediction model for pulmonary tuberculosis in presumptive tuberculosis patients in Tigray, northern Ethiopia.Scientific reports · 2025Article
- Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish.BMC medical informatics and decision making · 2025Article
- Continuous digital cough monitoring during 6-month pulmonary tuberculosis treatment.ERJ open research · 2025Article
- Validation and accuracy of the Hyfe cough monitoring system: a multicenter clinical study.Scientific reports · 2025Observational
- Managing Pulmonary Injury in Older Adults with Tuberculosis: The Role of Nursing Interventions.Journal of multidisciplinary healthcare · 2025Review
- WHO target product profile for TB detection at peripheral settings: 2024 update.PLOS global public health · 2025Article
- SympCoughNet: symptom assisted audio-based COVID-19 detection.Frontiers in digital health · 2025Article
- A dataset of Solicited Cough Sound for Tuberculosis Triage Testing.Scientific data · 2024Article
- Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes.NPJ cardiovascular health · 2024Article
- What is New in the Diagnosis of Childhood Tuberculosis?Indian journal of pediatrics · 2024Review
- Advances in TB diagnostics: A critical element for the elimination toolkit.The Indian journal of medical research · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cough assessment is central to the clinical management of respiratory diseases, including tuberculosis (TB), but strategies to objectively and unobtrusively measure cough are lacking. Acoustic epidemiology is an emerging field that uses technology to detect cough sounds and analyze cough patterns to improve health outcomes among people with respiratory conditions linked to cough. This field is increasingly exploring the potential of artificial intelligence (AI) for more advanced applications, such as analyzing cough sounds as a biomarker for disease screening. While much of the data are preliminary, objective cough assessment could potentially transform disease control programs, including TB, and support individual patient management. Here, we present an overview of recent advances in this field and describe how cough assessment, if validated, could support public health programs at various stages of the TB care cascade.
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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.