ArticleBiomedical engineering online2025
Assessing chronic obstructive pulmonary disease risk based on exhalation and cough sounds.
Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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Who cites it
2 citing papers in PubMed.
- Respiratory sound-based AI screening of asthma and COPD via multi-feature fusion and CatBoost classification.Scientific reports · 2026Article
- Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
BACKGROUND AND
objectiveChronic obstructive pulmonary disease (COPD), a progressively worsening respiratory condition, severely impacts patient quality of life. Early risk assessment can improve treatment outcomes and lessen healthcare burdens. However, current early assessment methods are limited. This study seeks to develop innovative approaches for the early detection and evaluation of COPD.
methodsThis study employed a cross-sectional design. Initially, we created a dedicated recording application deployed on smartphones to gather audio data from participants. Following this, each individual completed pulmonary function tests and participated in questionnaire surveys. COPD risk was defined as a pre-bronchodilator FEV
resultsWe gathered valid data from 530 adults, of whom 171 met the criteria for being at risk of COPD. Utilizing the XGBoost algorithm, we achieved a precision of 0.98 and a recall of 0.89.
conclusionsOur study demonstrates that cough audio signals provide valuable insights for identifying COPD risk, effectively complementing exhalation signals in assessments. This approach is not only feasible and practical for real-world applications, but also offers an affordable and accessible solution, especially beneficial in resource-limited settings.
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Registered trials
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