A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study

Porter, Paul; Brisbane, Joanna; Abeyratne, Udantha R.; Bear, Natasha; Claxton, Scott

doi:10.1080/02770903.2022.2051546

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Although not many, there are also several previous studies on asthma, from a basic study to distinguish voluntary coughs from 12 asthmatics and 12 healthy controls ( 35 ), to a recent study which recorded voluntary cough sounds from 89 asthmatics and 89 healthy controls on a smartphone, and analyzed by their audio-based classification model, showing sensitivity and specificity of 83% and 85% in classifying cough between two groups ( 19 ). Moreover, the latest prospective diagnostic accuracy study enrolled 119 asthmatics and recorded five voluntary or spontaneous coughs from each subject using a smartphone, to differentiate acute exacerbation of asthma and controlled asthma in these asthmatic patients by AI-based cough sound analysis ( 22 ). This study showed that a positive percent agreement between the clinical diagnosis and AI-based detection of asthma exacerbation was 89%.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, artificial intelligence (AI) and a machine learning-based approach to cough monitoring emerged to address these deficiencies ( 18 - 21 ). Applications that analyze cough sounds and objectively quantify cough from a smartphone recording in near real time through an AI algorithm have substantial advantages over the above-mentioned monitoring systems ( 22 , 23 ). These solutions do not require additional equipment for use like separate microphones or machinery, which could improve patient compliance and convenience.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, this AI-based approach for objectively counting cough can improve monitoring for research purposes, as well as clinical use, as treatment response of patients can be monitored in real time. In a previous study, the usefulness of AI-based cough counts was evaluated in patients with acute asthma exacerbation, however cross-sectional measurement of only five voluntary or spontaneous coughs was conducted ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

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A smartphone-based application for cough counting in patients with acute asthma exacerbation

Shim¹,

Kim²,

Kim³

et al. 2023

J Thorac Dis

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Background While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone application. In this prospective study, this AI-based cough algorithm was applied among real-world patients with an acute exacerbation of asthma. Methods Patients with an acute asthma exacerbation recorded their cough sounds for 7 days (2 consecutive hours during awake time and 5 consecutive hours during sleep) using Coughy TM smartphone application. During the study period, subjects received systemic corticosteroids and bronchodilator to control asthma. Coughs collected by application were counted by both the AI algorithm and two human experts. Subjects also provided self-measured peak expiratory flow rate (PEFR) and completed other outcome assessments [e.g., cough symptom visual analogue scale (CS-VAS), awake frequency, salbutamol use] to investigate the correlation between cough and other parameters. Results A total of 1,417.6 h of cough recordings were obtained from 24 asthmatics (median age =39 years). Cough counts by AI were strongly correlated with manual cough counts during sleep time (rho =0.908, P<0.001) and awake time (rho =0.847, P<0.001). Sleep time cough counts were moderately to strongly correlated with CS-VAS (rho =0.339, P<0.001), the frequency of waking up (rho =0.462, P<0.001), and salbutamol use at night (rho =0.243, P<0.001). Weak-to-moderate correlations were found between awake time cough counts and CS-VAS (rho =0.313, P<0.001), the degree of activity limitation (rho =0.169, P=0.005), and salbutamol use at awake time (rho =0.276, P<0.001). Neither awake time nor sleep time cough counts were significantly correlated with PEFR. Conclusions The strong correlation between cough counts using the AI-based algorithm and human experts, and other indicators of patient health status provides evidence of the validity of this AI algorithm for use in asthma patients experiencing an acute exacerbation. Study findings suggest that Coughy TM could be a novel solution for objectively monitoring cough in a clinical setting.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A smartphone-based application for cough counting in patients with acute asthma exacerbation

Shim¹,

Kim²,

Kim³

et al. 2023

J Thorac Dis

View full text Add to dashboard Cite

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“…Using smartphone-based monitoring, Gabaldón-Figueira et al found that longitudinal monitoring was more accurate than 24-h monitoring and the optimal monitoring period would depend on the baseline cough frequency ( 172 ). This technology was also used to collect the nocturnal cough of patients with physician-diagnosed asthma ( 173 - 175 ) and helped with detecting the presence of an asthma exacerbation as well ( 176 ).…”

Section: Assessing Cough As An Outcomementioning

confidence: 99%

Chronic cough—the limitation and advances in assessment techniques

Zhang¹,

Sykes²,

Brindle³

et al. 2022

J Thorac Dis

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Accurate and consistent assessments of cough are essential to advance the understanding of the mechanisms of cough and individualised the management of patients. Considerable progress has been made in this work. Here we reviewed the currently available tools for subjectively and objectively measuring both cough sensitivity and severity. We also provided some opinions on the new techniques and future directions.The simple and practical Visual Analogue Scale (VAS), the Leicester Cough Questionnaire (LCQ), and the Cough Specific Quality of Life Questionnaire (CQLQ) are the most widely used self-reported questionnaires for evaluating and quantifying cough severity. The Hull Airway Reflux Questionnaire (HARQ) is a tool to elucidate the constellation of symptoms underlying the diagnosis of chronic cough. Chemical excitation tests are widely used to explore the pathophysiological mechanisms of the cough reflex, such as capsaicin, citric acid and adenosine triphosphate (ATP) challenge test. Cough frequency is an ideal primary endpoint for clinical research, but the application of cough counters has been limited in clinical practice by the high cost and reliance on aural validation. The ongoing development of cough detection technology for smartphone apps and wearable devices will hopefully simplify cough counting, thus transitioning it from niche research to a widely available clinical application.

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“…A recently conducted double-blind, prospective, diagnostic accuracy study evaluated the performance of the algorithm by comparing it to expert clinical opinion and standard lung function testing. The accuracy of the algorithm in identifying asthma exacerbations, as measured by positive percent agreement with expert clinical diagnosis was 89% [19]. There are commercial devices available, such as the VitaloJAK Cough monitoring device and NuvoAir, which objectively measure cough by counting cough events automatically.…”

Section: Introductionmentioning

confidence: 99%

A Cross-sectional Feasibility Study to Evaluate the Usability and Efficacy of “Swaasa”: An AI-Platform for Rapid Respiratory Health Assessment

Lotheti,

Vissamsetti,

Pamarthi

et al. 2023

Preprint

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Analysing cough sounds is vital in pulmonary medicine. Recently, AI tools are being trained to analyse the acoustic signals of cough sounds so that more cases can be quickly tested, thereby reducing the patient load on primary healthcare systems. In this paper, we evaluate ”Swaasa”, our AI-based platform for rapid respiratory screening, highlighting its efficacy and ease of use. We applied our trained classifier to catch underlying pathologies from cough sound data collected from diverse sources. We then used a pattern classifier to identify specific respiratory conditions based on cough sound patterns. We tested the robustness of our methods by comparing our results with that of a pulmonary physician in 355 cases and show that Swaasa correctly predicted associated risk in 87.32% of those cases. Our platform has a sensitivity of 97.27% with a Positive Predictive Value (PPV) of 88.54%, giving us the potential to revolutionise disease screening, especially for large populations and in isolated rural areas. Our rapid and easy-to-use Software as a Service (SaaS) solution efficiently diagnoses and conserves resources, improves patient outcomes, and establishes a comprehensive and accessible healthcare framework.

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A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study

Cited by 5 publications

References 27 publications

A smartphone-based application for cough counting in patients with acute asthma exacerbation

A smartphone-based application for cough counting in patients with acute asthma exacerbation

Chronic cough—the limitation and advances in assessment techniques

A Cross-sectional Feasibility Study to Evaluate the Usability and Efficacy of “Swaasa”: An AI-Platform for Rapid Respiratory Health Assessment

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