Cough Sounds in Screening and Diagnostics: A Scoping Review

Hegde, Siddhi; Sreeram, Shreya; Alter, Isaac L.; Shor, Chaya; Valdez, Tulio A.; Meister, Kara D.; Rameau, Anaïs

doi:10.1002/lary.31042

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The decision to exclude children younger than 18 years of age from our study was driven by the extensive consent process required for minors, compounded by our limited resources. Additionally, previous research has highlighted challenges in collecting cough sounds from this demographic [ 37 - 39 ].…”

Section: Methodsmentioning

confidence: 99%

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

Isangula,

Haule

2024

JMIR Res Protoc

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Background Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. Objective This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. Methods This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. Results This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Conclusions Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. International Registered Report Identifier (IRRID) PRR1-10.2196/54388

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Section: Methodsmentioning

confidence: 99%

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

Isangula,

Haule

2024

JMIR Res Protoc

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New developments in the application of artificial intelligence to laryngology

Torborg,

Kim,

Rameau

2024

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review The purpose of this review is to summarize the existing literature on artificial intelligence technology utilization in laryngology, highlighting recent advances and current barriers to implementation. Recent findings The volume of publications studying applications of artificial intelligence in laryngology has rapidly increased, demonstrating a strong interest in utilizing this technology. Vocal biomarkers for disease screening, deep learning analysis of videolaryngoscopy for lesion identification, and auto-segmentation of videofluoroscopy for detection of aspiration are a few of the new ways in which artificial intelligence is poised to transform clinical care in laryngology. Increasing collaboration is ongoing to est ablish guidelines and standards for the field to ensure generalizability. Summary Artificial intelligence tools have the potential to greatly advance laryngology care by creating novel screening methods, improving how data-heavy diagnostics of laryngology are analyzed, and standardizing outcome measures. However, physician and patient trust in artificial intelligence must improve for the technology to be successfully implemented. Additionally, most existing studies lack large and diverse datasets, external validation, and consistent ground-truth references necessary to produce generalizable results. Collaborative, large-scale studies will fuel technological innovation and bring artificial intelligence to the forefront of patient care in laryngology.

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Validation of the Hyfe cough monitoring system: a multicenter clinical study (Preprint)

Chaccour,

Sánchez-Olivieri,

Siegel

et al. 2024

Preprint

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BACKGROUND The ability to passively and continuously monitor coughing would significantly improve cough management and research. To date there is no automated clinically validated cough monitor that can be routinely used in clinical care and research. Here we describe the validation of such an automated cough monitor. OBJECTIVE To assess the overall performance of the Hyfe Cough Monitoring System when used by individuals with problematic cough, under common living conditions METHODS This multicenter observational study compared the results of the Hyfe CoughMonitor wrist-worn device with manually counted coughs in subjects with a variety of etiologies as they went about their usual daily activities. We collected 24 hours of continuous sounds from subjects while they simultaneously wore a CoughMonitor and an audio recorder. Coughs were labelled by multiple trained annotators who listened to the continuous audio recordings using validated methodology. The time stamps of these human-detected coughs were compared to those of the CoughMonitor to determine the system’s overall performance using event-to-event and hourly rate correlation analyses. RESULTS Over the 546 hours monitored, 4454 cough events were recorded; The overall sensitivity was 90.4% (95% CI of 88.3% to 92.2%). The overall false positive rate was 1.03 false positives per hour (95% CI of 0.84 to 1.24). The overall correlation between manual and CoughMonitor measured hourly coughing was high (Pearson correlation coefficient of 0.99 with OLS slope 0.94 and OLS intercept 0.68). CONCLUSIONS The present analysis of cough events demonstrated that the Hyfe CoughMonitor accurately reflects them with a high sensitivity and a low false positive rate. Future studies should confirm its potential role in the management of patients with cough in clinical practice. CLINICALTRIAL Clinicaltrials.gov: NCT05723159

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Cough Sounds in Screening and Diagnostics: A Scoping Review

Cited by 3 publications

References 35 publications

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

New developments in the application of artificial intelligence to laryngology

Validation of the Hyfe cough monitoring system: a multicenter clinical study (Preprint)

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