Recent work has shown the potential of the use of audio data in screening for COVID-19. However, very little exploration has been done of monitoring disease progression, especially recovery in COVID-19 through audio. Tracking disease progression characteristics and patterns of recovery could lead to tremendous insights and more timely treatment or treatment adjustment, as well as better resources management in health care systems. The primary objective of this study is to explore the potential of longitudinal audio dynamics for COVID-19 monitoring using sequential deep learning techniques, focusing on prediction of disease progression and, especially, recovery trend prediction. We analysed crowdsourced respiratory audio data from 212 individuals over 5 days to 385 days, alongside their self-reported COVID-19 test results. We first explore the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUC-ROC of 0.79, sensitivity of 0.75 and specificity of 0.70, supports the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examine the predicted disease progression trajectory, which displays high consistency with the longitudinal test results with a correlation of 0.76 in the test cohort, and 0.86 in a subset of the test cohort with 12 participants who report disease recovery. Our findings suggest that monitoring COVID-19 progression via longitudinal audio data has enormous potential in the tracking of individuals' disease progression and recovery.
翻译:最近的工作表明,利用声音数据进行COVID-19筛查的潜力。然而,对监测疾病进展,特别是通过声音监测COVID-19的恢复,很少进行关于监测疾病进展、特别是COVID-19的恢复的探索。跟踪疾病进展特点和恢复模式可带来巨大的洞察力和更及时的治疗或治疗调整,以及改善保健系统的资源管理。这项研究的主要目的是探索利用连续深层学习技术对COVID-19进行监测的纵向音频动态的潜力,重点是预测疾病进展,特别是复原趋势预测。我们分析了来自212人5天到385天的人群源呼吸道音频数据,以及他们自我报告的COVI-19的检测结果。我们首先探讨了为COVI-19的检测获取音频生物标志的纵向动态和模式的好处。强效的ACUC-ROC的性能,使0.79的敏感性达到0.75,而具体性为0.70,支持该方法的效力,而不是利用纵向动态动态的预测。我们进一步研究了预测的疾病演变轨迹,该轨迹显示,与测试组中0.76个测试组中的气态测试结果与0.76的相关性,并表明CReval-16组中的个人在恢复中进行长期的跟踪。