ReviewExperimental biology and medicine (Maywood, N.J.)2022
Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.
Review in Experimental biology and medicine (Maywood, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- 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
- Beyond fingerprint and voice print: cough sequence sound for identity verification.npj health systems · 2026Article
- Short-term associations between air pollution and nocturnal cough frequency.Communications health · 2026Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- Automated Cough Analysis with Convolutional Recurrent Neural Network.Bioengineering (Basel, Switzerland) · 2024Article
- Fused Audio Instance and Representation for Respiratory Disease Detection.Sensors (Basel, Switzerland) · 2024Article
- Back to the future: the novel art of digital auscultation applied in a prospective observational study of critically ill Covid-19 patients.Pneumonia (Nathan Qld.) · 2024Article
- BrachySound: machine learning based assessment of respiratory sounds in dogs.Scientific reports · 2023Article
- Evaluation of the Submaximal Treadmill-Based Fitness Test in Six Brachycephalic Breeds-A Follow-Up Study.Animals : an open access journal from MDPI · 2023Article
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.Sensors (Basel, Switzerland) · 2023Article
- Interpretation of lung disease classification with light attention connected module.Biomedical signal processing and control · 2023Article
- Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.Sensors (Basel, Switzerland) · 2023Article
- In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review.Sensors (Basel, Switzerland) · 2022Review
- Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.Experimental biology and medicine (Maywood, N.J.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.
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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.