ReviewBMC pulmonary medicine2022
The coming era of a new auscultation system for analyzing respiratory sounds.
Review in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 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
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 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
- Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled 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
- Lung Auscultation for Detecting Interstitial Lung Disease in Patients with Newly Diagnosed Systemic Sclerosis: Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection.IEEE transactions on bio-medical engineering · 2026Article
- Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.BMC medical informatics and decision making · 2026Article
- Risk factors for progression to bronchitis or pneumonia in hospitalized children with human rhinovirus infection: a retrospective cohort study using machine learning.Frontiers in pediatrics · 2026Article
- Streamlining heart failure patient care with machine learning of thoracic cavity sound data.World journal of cardiology · 2025Review
- Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation.Journal of clinical medicine · 2025Article
- Patient information needs for transparent and trustworthy cardiovascular artificial intelligence: A qualitative study.PLOS digital health · 2025Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.Physiological measurement · 2025Article
- Early Diagnosis of Pneumonia and Chronic Obstructive Pulmonary Disease with a Smart Stethoscope with Cloud Server-Embedded Machine Learning in the Post-COVID-19 Era.Biomedicines · 2025Article
- Review
- Machine learning-driven strategies for enhanced pediatric wheezing detection.Frontiers in pediatrics · 2025Article
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence.NPJ primary care respiratory medicine · 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
- Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies.Biosensors · 2024Review
- Tele-Monitoring Applications in Respiratory Allergy.Journal of clinical medicine · 2024Review
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
Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.
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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.