Shim Ji-Su, Kim Byung-Keun, Kim Sae-Hoon, Kwon Jae-Woo, Ahn Kyung-Min, Kang Sung-Yoon, Park Han-Ki, Park Heung-Woo, Yang Min-Suk, Kim Min-Hye, Lee Sang Min
Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
J Thorac Dis. 2023 Jul 31;15(7):4053-4065. doi: 10.21037/jtd-22-1492. Epub 2023 Jun 9.
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.
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 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.
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.
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 could be a novel solution for objectively monitoring cough in a clinical setting.
虽然临床研究中有用于客观咳嗽计数的工具,但临床实践中尚无用于客观咳嗽测量的工具。最近开发了一种基于人工智能(AI)的咳嗽计数系统,可对通过智能手机应用程序收集的咳嗽声音进行量化。在这项前瞻性研究中,这种基于AI的咳嗽算法被应用于现实世界中急性加重期哮喘患者。
急性哮喘加重期患者使用Coughy智能手机应用程序记录7天的咳嗽声音(清醒时连续2小时,睡眠时连续5小时)。在研究期间,受试者接受全身用糖皮质激素和支气管扩张剂以控制哮喘。应用程序收集的咳嗽由AI算法和两名人类专家进行计数。受试者还提供自我测量的呼气峰值流速(PEFR)并完成其他结局评估[例如,咳嗽症状视觉模拟量表(CS-VAS)、清醒频率、沙丁胺醇使用情况],以研究咳嗽与其他参数之间的相关性。
共从24名哮喘患者(中位年龄=39岁)获得了1417.6小时的咳嗽记录。AI计数的咳嗽与睡眠期间(rho=0.908,P<0.001)和清醒期间(rho=0.847,P<0.001)人工计数的咳嗽密切相关。睡眠期间咳嗽计数与CS-VAS(rho=0.339,P<0.001)、醒来频率(rho=0.462,P<0.001)和夜间沙丁胺醇使用情况(rho=0.243,P<0.001)呈中度至高度相关。清醒期间咳嗽计数与CS-VAS(rho=0.313,P<0.001)、活动受限程度(rho=0.169,P=0.005)和清醒时沙丁胺醇使用情况(rho=0.276,P<0.001)之间存在弱至中度相关性。清醒和睡眠期间的咳嗽计数与PEFR均无显著相关性。
基于AI的算法计数的咳嗽与人类专家计数的咳嗽以及患者健康状况的其他指标之间的强相关性,为该AI算法在急性加重期哮喘患者中的有效性提供了证据。研究结果表明,Coughy可能是临床环境中客观监测咳嗽的一种新解决方案。