Background Since the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution. Methods This study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings. Results Our model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification. Conclusion This study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool. Data are available upon reasonable request.

中文翻译：

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端到端卷积神经网络能够从呼吸和咳嗽音频中检测到 COVID-19：一项试点研究

背景 自 2019 年 12 月新型冠状病毒肺炎 (COVID-19) 出现以来，鉴于其对身体、心理和经济造成的巨大损害，多学科研究团队一直在努力研究如何最好地控制这种流行病。大规模检测被认为是一种潜在的补救措施；然而，使用物理测试进行大规模测试是一种成本高昂且难以扩展的解决方案。方法 这项研究证明了另一种形式的 COVID-19 检测的可行性，即通过使用音频生物标记和深度学习来利用数字技术。具体来说，我们表明可以训练基于深度神经网络的模型，以使用呼吸和咳嗽录音来检测有症状和无症状的 COVID-19 病例。结果我们的模型是一个定制的卷积神经网络，在由 355 名众包参与者组成的数据集上展示了强大的经验性能，在 COVID-19 分类任务中实现了 0.846 的接收器操作特征曲线下面积。结论 鉴于低成本、高度可扩展的数字 COVID-19 的明显优势，本研究为使用咳嗽和呼吸音频信号诊断 COVID-19 提供了概念证明，并激发了对更广泛数据样本进行全面的后续研究。诊断工具。数据可根据合理要求提供。