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

Abstract

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.

Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients

“…Different machine learning (ML) and AI-based studies on chronic cough and asthma have been carried out in recent years, by leveraging the newest medical technologies [ 8 ]. An AI-based cough count, CoughyTM [ 9 ], system was recently developed that quantifies cough sounds collected through a smartphone application. Study results showed that suggest that CoughyTM could be a novel solution for objectively monitoring cough in a clinical setting.…”

Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients

“…Different machine learning (ML) and AI-based studies on chronic cough and asthma have been carried out in recent years, by leveraging the newest medical technologies [17]. An AI-based cough count, CoughyTM [14], system was recently developed that quantifies cough sounds collected through a smartphone application. Study results showed that suggest that CoughyTM could be a novel solution for objectively monitoring cough in a clinical setting.…”