Wet and dry cough classification using cough sound characteristics and machine learning: A systematic review.

Abstract

Distinguishing between productive (wet) and non-productive (dry) cough types is important for evaluating respiratory health, assisting in differential diagnosis, and monitoring disease progression. However, assessing cough type through the perception of cough sounds in clinical settings poses challenges due to its subjectivity. Employing objective cough sound analysis holds promise for aiding diagnostic assessments and guiding the management of respiratory conditions. This systematic review aims to assess and summarize the predictive capabilities of machine learning algorithms in analyzing cough sounds to determine cough type. A systematic search of the Scopus, Medline, and Embase databases conducted on March 8, 2025, yielded three studies that met the inclusion criteria. The quality assessment of these studies was conducted using the checklist for the assessment of medical artificial intelligence (ChAMAI). The inter-rater agreement for annotating wet and dry coughs ranged from 0.22 to 0.81 across the three studies. Furthermore, these studies employed diverse inputs for their machine learning algorithms, including different cough sound features and time-frequency representations. The algorithms used ranged from conventional classifiers like logistic regression to neural networks. While the classification accuracy for identifying wet and dry coughs ranged from 78% to 87% across these studies, none of them assessed their algorithms through external validation. The high variability in inter-rater agreement highlights the subjectivity in manually interpreting cough sounds and underscores the need for objective cough sound analysis methods. The predictive ability of cough-type classification algorithms shows promise in the small number of studies analyzed in this systematic review. However, more studies are needed, particularly those validating their models on independent and external datasets.