ArticlePloS one2012
Validation of an automated cough detection algorithm for tracking recovery of pulmonary tuberculosis patients.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Cough Frequency Monitors: A Systematic Review and Meta-Analysis.Respirology (Carlton, Vic.) · 2026Pooled it
- Tools for assessing outcomes in studies of chronic cough: CHEST guideline and expert panel report.Chest · 2015Guideline
- A cross sectional feasibility study to evaluate the usability and efficacy of Swaasa AI platform for rapid respiratory health assessment.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
- An automatic cough counting method and system construction for portable devices.Frontiers in bioengineering and biotechnology · 2024Article
- Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study.JMIR formative research · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
- Chronic cough-the limitation and advances in assessment techniques.Journal of thoracic disease · 2022Review
- Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.Sensors (Basel, Switzerland) · 2022Review
- Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence.ERJ open research · 2022Article
- Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network.Biomedical signal processing and control · 2022Article
- Making cough count in tuberculosis care.Communications medicine · 2022Review
- Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.IEEE access : practical innovations, open solutions · 2021Article
- The present and future of cough counting tools.Journal of thoracic disease · 2020Review
- Cough dynamics in adults receiving tuberculosis treatment.PloS one · 2020Article
- Wearable Ultrafine Particle and Noise Monitoring Sensors Jointly Measure Personal Co-Exposures in a Pediatric Population.International journal of environmental research and public health · 2019Article
- Estimation of Cough Peak Flow Using Cough Sounds.Sensors (Basel, Switzerland) · 2018Article
- Article
- Dynamics of Cough Frequency in Adults Undergoing Treatment for Pulmonary Tuberculosis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2017Article
- Article
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.
Funding
Abstract
backgroundA laboratory-free test for assessing recovery from pulmonary tuberculosis (TB) would be extremely beneficial in regions of the world where laboratory facilities are lacking. Our hypothesis is that analysis of cough sound recordings may provide such a test. In the current paper, we present validation of a cough analysis tool. METHODOLOGY/PRINCIPAL
findingsCough data was collected from a cohort of TB patients in Lima, Peru and 25.5 hours of recordings were manually annotated by clinical staff. Analysis software was developed and validated by comparison to manual scoring. Because many patients cough in bursts, coughing was characterized in terms of cough epochs. Our software correctly detects 75.5% of cough episodes with a specificity of 99.6% (comparable to past results using the same definition) and a median false positive rate of 4 false positives/hour, due to the noisy, real-world nature of our dataset. We then manually review detected coughs to eliminate false positives, in effect using the algorithm as a pre-screening tool that reduces reviewing time to roughly 5% of the recording length. This cough analysis approach provides a foundation to support larger-scale studies of coughing rates over time for TB patients undergoing treatment.
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.