ArticlePloS one2019
The accuracy of lung auscultation in the practice of physicians and medical students.
Article in PloS one, 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 33 papers, 1 of them a synthesis that pooled it.
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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.
The Efficacy of Medical Students to Correctly Recognise Pathological From Non-pathological Lung Sounds Over a Period of Time
The Effect of White Noise on the Ability of Second-year and Third-year Medical Students to Recognise Correctly Adventitious Lung Sound
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Effect of a prolonged slow expiration technique on 24-h food intake in children hospitalized for moderate bronchiolitis: a randomized controlled trial.Italian journal of pediatrics · 2024Trial
- Effectiveness of a new interactive web teaching material for improving lung auscultation skills: randomized controlled trial for clinical nurses.Nagoya journal of medical science · 2022Trial
- Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.BMC pulmonary medicine · 2021Trial
- Exploratory Assessment of Pulsed-Wave Doppler Representations of Lung Sounds Using Deep Learning: An In-Vitro Phantom Study.medRxiv : the preprint server for health sciences · 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
- Improving asthma control assessment and outcomes in children with asthma using an artificial intelligence digital tool: a prospective multicentre cohort study.ERJ open research · 2026Article
- Improving the management and outcomes of preschool wheeze: protocol of a prospective multicentre cohort study.BMJ open respiratory research · 2026Article
- Fabrication of Stretchable Piezoelectric Sensor with a Kirigami Design for Heart Sound Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation.Journal of clinical medicine · 2025Article
- Comparison of the Utility of Single-Ear and Binaural Auscultations: A Diagnostic Accuracy Study.Cureus · 2025Article
- The use of graphical lung sound visualizations in medical education: an evaluation of its impact on clinical clerkship.BMC medical education · 2025Article
- The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence.NPJ primary care respiratory medicine · 2024Article
- Article
- A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning analysis of blood flow sounds to detect arteriovenous fistula stenosis.NPJ digital medicine · 2023Article
- Article
- Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.Sensors (Basel, Switzerland) · 2023Review
- DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries.NPJ digital medicine · 2023Article
- Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAuscultation is one of the first examinations that a patient is subjected to in a GP's office, especially in relation to diseases of the respiratory system. However it is a highly subjective process and depends on the physician's ability to interpret the sounds as determined by his/her psychoacoustical characteristics. Here, we present a cross-sectional assessment of the skills of physicians of different specializations and medical students in the classification of respiratory sounds in children. METHODS AND
findings185 participants representing different medical specializations took part in the experiment. The experiment comprised 24 respiratory system auscultation sounds. The participants were tasked with listening to, and matching the sounds with provided descriptions of specific sound classes. The results revealed difficulties in both the recognition and description of respiratory sounds. The pulmonologist group was found to perform significantly better than other groups in terms of number of correct answers. We also found that performance significantly improved when similar sound classes were grouped together into wider, more general classes.
conclusionsThese results confirm that ambiguous identification and interpretation of sounds in auscultation is a generic issue which should not be neglected as it can potentially lead to inaccurate diagnosis and mistreatment. Our results lend further support to the already widespread acknowledgment of the need to standardize the nomenclature of auscultation sounds (according to European Respiratory Society, International Lung Sounds Association and American Thoracic Society). In particular, our findings point towards important educational challenges in both theory (nomenclature) and practice (training).
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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.