Trial reportRespiratory research2019
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children.
Trial report in Respiratory research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 52 papers, 3 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.
Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain
Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease
Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it, 144 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
- 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
- IoT Adoption and Application for Smart Healthcare: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Cough Audio Recognition for Early Detection of Respiratory Diseases: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Article
- Conversational speech for respiratory triage in primary care: a pilot study.Frontiers in medicine · 2026Article
- Using mHealth to Predict Asthma Exacerbations in Children and Adolescents (Mobile Health for Kids With Asthma): Protocol for an Observational Study.JMIR research protocols · 2025Article
- Review
- Real-time airway monitoring system using binary classification model based on respiratory sounds of rabbits with a tracheostomy tube.Scientific reports · 2025Article
- Development and psychometric evaluation of the artificial intelligence attitude scale for nurses.BMC nursing · 2025Article
- Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.Scientific reports · 2025Article
- Development and validation of a nomogram for predicting lung cancer based on acoustic-clinical features.Frontiers in medicine · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care.Journal of asthma and allergy · 2025Review
- Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.Journal of clinical medicine · 2024Review
- Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.Diagnostics (Basel, Switzerland) · 2024Article
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.Science advances · 2024Article
- Improving transparency in malaria programme funds: A business case for connected diagnostics in Kenya.Journal of public health in Africa · 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
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser.
methodsWe recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations.
resultsA total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%).
conclusionThe results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders.
trial registrationAustralian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213 : 11.09.2018.
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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.