[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125322-en":3,"doc-seo-125322-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125322,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Wet and dry cough classification using cough sound characteristics and machine learning: A systematic review","Distinguishing productive (wet) from non-productive (dry) cough is important for evaluating respiratory health, supporting differential diagnosis, and tracking disease progression, yet clinical assessment of cough sounds remains subjective. This systematic review synthesizes evidence on machine learning algorithms that analyze cough audio to classify cough type. Searches in Scopus, MEDline, and Embase (March 8, 2025) identified three eligible studies, assessed using the ChAMAI checklist. Results show inter-rater agreement variability (0.22–0.81) and reported accuracies of 78%–87%, but no external validation.","International Journal of Medical Informatics 199 (2025) 105912  \nContents lists available at ScienceDirect  \nInternational Journal of Medical Informatics  \njournal [homepage:](homepage: www.elsevier.com/locate/ijmedinf)[ www.elsevier.com/locate/ijmedinf](homepage: www.elsevier.com/locate/ijmedinf)  \n| Review article\u003Cbr>Wet and dry cough classification using cough sound characteristics and machine learning: A systematic review |  |  |  |\n| --- | --- | --- | --- |\n| Roneel V. Sharana,*, Hao Xiong b\u003Cbr>a School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom b Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Cough sound Feature extraction Machine learning Respiratory diseases Wet cough |  | Background: 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. Method: 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).\u003Cbr>Results: 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.\u003Cbr>Conclusion: 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. |  |\n\n1. Introduction  \nCough is a prevalent symptom of respiratory diseases and a common presenting condition in primary care settings globally [1]. It serves as a reflex mechanism to expel irritants from the respiratory system, with cough types broadly classified as productive (wet) or non-productive (dry). Wet coughs typically involve sputum production and may arise from infections, inflammation, or chronic conditions such as chronic obstructive pulmonary disease (COPD), whereas dry coughs can result from asthma or follow respiratory infections [2–4]. A dry cough has also been reported in the majority of COVID-19 patients [5]. In certain illnesses, the cough may initially present as dry but evolve into a phlegmy or wet cough as the disease progresses and airway secretions increase  \n[6].  \nDifferentiating between cough types is important for diagnosing and monitoring respiratory diseases, understanding disease progression, and guiding treatment decisions [7]. For instance, studies have shown that recognizing wet cough characteristics in COPD patients can help identify exacerbations early, potentially preventing hospitalization [8]. Simi","cbCaimvKIglKLtVk","https://ap.wps.com/l/cbCaimvKIglKLtVk","pdf",700904,1,7,"English","en",105,"# Introduction\n# Methods\n## Search strategy\n# Results\n# Conclusion","[{\"question\":\"Why is distinguishing wet and dry cough clinically important?\",\"answer\":\"Wet and dry cough types help evaluate respiratory health, support differential diagnosis, and monitor disease progression, which can guide treatment decisions.\"},{\"question\":\"How did this systematic review evaluate machine learning for cough sound classification?\",\"answer\":\"It performed a systematic search in Scopus, Medline, and Embase (March 8, 2025) and assessed study quality using the ChAMAI checklist.\"},{\"question\":\"What were the main limitations found across the included studies?\",\"answer\":\"Inter-rater agreement for wet/dry labeling varied widely (0.22–0.81), and none of the studies used external validation to assess model performance on independent datasets.\"}]","Wet and dry cough classification 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