ArticleScientific reports2021
Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.
Article in Scientific reports, 2021. 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 54 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.
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
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Broadband wearable acoustic sensing for continuous cardiopulmonary monitoring.Microsystems & nanoengineering · 2026Article
- An Interpretable Multidimensional Acoustic Physiology Map for COPD Using Digital Lung Sounds.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2026Article
- Explainable Multi-Modal Deep Learning for Recording-Level Classification of Respiratory Audio Signals Under Internal and Domain-Shift Evaluation.Life (Basel, Switzerland) · 2026Article
- From Fixed-Frequency to Tunable: Advances in Acoustic Sensors for Physiological Acoustic Monitoring.Sensors (Basel, Switzerland) · 2026Review
- 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
- Respiratory sound analysis in a rabbit tracheomalacia model.Scientific reports · 2026Article
- Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles.Bioengineering (Basel, Switzerland) · 2026Article
- Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model.PLOS digital health · 2026Article
- Federated training of spiking neural networks on edge hardware for audio processing.Frontiers in neuroscience · 2026Article
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- Construction and Validation of an Automatic Segmentation Method for Respiratory Sound Time Labels.The clinical respiratory journal · 2025Article
- Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.Scientific reports · 2025Article
- Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation.Journal of clinical medicine · 2025Article
- A Multimodal AI Framework for Automated Multiclass Lung Disease Diagnosis from Respiratory Sounds with Simulated Biomarker Fusion and Personalized Medication Recommendation.International journal of molecular sciences · 2025Article
- Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation.Scientific reports · 2025Article
- The use of graphical lung sound visualizations in medical education: an evaluation of its impact on clinical clerkship.BMC medical education · 2025Article
- A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.Scientific reports · 2025Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician's considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.
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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.