@Article{info:doi/10.2196/37004，作者=“Dang, Ting和Han, Jing和Xia, Tong和Spathis, Dimitris和Bondareva, Erika和Siegele-Brown, Chlo{\“e}和Chauhan, Jagmohan和Grammenos, Andreas和Hasthanasombat, Apinan和Floto, R Andres和Cicuta, Pietro和Mascolo, Cecilia”，标题=“通过顺序深度学习探索COVID-19进展预测的纵向咳嗽，呼吸和语音数据:模型开发与验证”，期刊=“J Med Internet Res”，年=“2022”，月=“6”，日=“21”，卷=“24”，号=“6”，页=“e37004”，关键词=“COVID-19;音频;COVID-19进展;深度学习;移动健康;背景:最近的研究表明，使用音频数据(如咳嗽、呼吸和声音)筛查COVID-19具有潜力。然而，鉴于目前的音频样本，这些方法仅侧重于一次性检测和检测感染，而不监测COVID-19的疾病进展。通过纵向音频数据持续监测COVID-19的进展，特别是恢复情况，提出了有限的探索。跟踪疾病进展特征和恢复模式可以带来见解，并导致更及时的治疗或治疗调整，以及更好的卫生保健系统资源管理。 Objective: The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. Methods: Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning--enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95{\%} CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. Results: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1{\%}) of 21 COVID-19--positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. Conclusions: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general. ", issn="1438-8871", doi="10.2196/37004", url="//www.mybigtv.com/2022/6/e37004", url="https://doi.org/10.2196/37004", url="http://www.ncbi.nlm.nih.gov/pubmed/35653606" }