Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review
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View abstract on PubMed
Summary
This summary is machine-generated.Digital biomarkers from respiratory sounds and voice, analyzed using machine learning (ML), offer efficient tools for diagnosing respiratory diseases. Research shows a growing trend in ML applications for cough detection, symptom identification, and voice analysis, especially post-pandemic.
Area Of Science
- Medical Informatics
- Bioacoustics
- Machine Learning
Background
- Respiratory diseases pose a significant global health challenge, requiring advanced diagnostic tools.
- Audio-based digital biomarkers from respiratory sounds and voice are emerging as key indicators of respiratory health.
- Machine learning (ML) provides powerful methods for analyzing these complex audio signals.
Approach
- This review synthesizes findings from 75 studies focused on audio analysis for respiratory conditions.
- The analysis categorizes research into cough detection, lower respiratory symptom identification, and voice/speech diagnostics.
- Publicly available datasets relevant to respiratory audio analysis are also presented.
Key Points
- ML algorithms are increasingly used to extract diagnostic information from respiratory sounds and voice.
- Studies address challenges like cough sound recognition in noisy environments and detecting wheezes or crackles.
- Voice and speech analysis are explored for evaluating voice abnormalities related to respiratory issues.
Conclusions
- Audio-based digital biomarkers and ML show significant potential for non-invasive respiratory disease diagnosis.
- Research trends are influenced by the COVID-19 pandemic, accelerating studies in remote diagnostics and mobile data acquisition.
- Further development in this field promises improved, accessible respiratory care.
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