ArticleBMJ open respiratory research2016
Wheezes, crackles and rhonchi: simplifying description of lung sounds increases the agreement on their classification: a study of 12 physicians' classification of lung sounds from video recordings.
Article in BMJ open respiratory research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles.Bioengineering (Basel, Switzerland) · 2026Article
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- Wheeze and Crackle Discrimination Algorithm in Pneumonia Respiratory Signals.Conference proceedings. IEEE Colombian Conference on Communications and Computing · 2024Article
- Usefulness of Quantification and Serial Monitoring of Fine Crackles for Early Detection of Treatment-related Lung Injury: A Report of Two Cases.Internal medicine (Tokyo, Japan) · 2024Article
- Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study.JMIR pediatrics and parenting · 2024Article
- Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis.Science advances · 2022Article
- Impact of respiratory syncytial virus disease on quality of life in adults aged ≥50 years: A qualitative patient experience cross-sectional study.Influenza and other respiratory viruses · 2022Article
- Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study.BMJ open respiratory research · 2022Article
- The coming era of a new auscultation system for analyzing respiratory sounds.BMC pulmonary medicine · 2022Review
- Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.Scientific reports · 2021Article
- Lung Auscultation Using the Smartphone-Feasibility Study in Real-World Clinical Practice.Sensors (Basel, Switzerland) · 2021Article
- Article
- Inspiratory crackles-early and late-revisited: identifying COPD by crackle characteristics.BMJ open respiratory research · 2021Article
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
- The use of spectrograms improves the classification of wheezes and crackles in an educational setting.Scientific reports · 2020Article
- Influence of observer preferences and auscultatory skill on the choice of terms to describe lung sounds: a survey of staff physicians, residents and medical students.BMJ open respiratory research · 2020Article
- Time to Say Goodbye to Bronchiolitis, Viral Wheeze, Reactive Airways Disease, Wheeze Bronchitis and All That.Frontiers in pediatrics · 2020Review
- Accuracy of chest auscultation in detecting abnormal respiratory mechanics in the immediate postoperative period after cardiac surgery.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2019Article
- Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm.Journal of medical systems · 2019Article
- Convolutional Neural Network for Breathing Phase Detection in Lung Sounds.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe European Respiratory Society (ERS) lung sounds repository contains 20 audiovisual recordings of children and adults. The present study aimed at determining the interobserver variation in the classification of sounds into detailed and broader categories of crackles and wheezes.
methodsRecordings from 10 children and 10 adults were classified into 10 predefined sounds by 12 observers, 6 paediatricians and 6 doctors for adult patients. Multirater kappa (Fleiss' κ) was calculated for each of the 10 adventitious sounds and for combined categories of sounds.
resultsThe majority of observers agreed on the presence of at least one adventitious sound in 17 cases. Poor to fair agreement (κ<0.40) was usually found for the detailed descriptions of the adventitious sounds, whereas moderate to good agreement was reached for the combined categories of crackles (κ=0.62) and wheezes (κ=0.59). The paediatricians did not reach better agreement on the child cases than the family physicians and specialists in adult medicine.
conclusionsDescriptions of auscultation findings in broader terms were more reliably shared between observers compared to more detailed descriptions.
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