Coppock Harry, Gaskell Alex, Tzirakis Panagiotis, Baird Alice, Jones Lyn, Schuller Björn
Computing, Imperial College London, London, UK.
Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
BMJ Innov. 2021 Apr;7(2):356-362. doi: 10.1136/bmjinnov-2021-000668. Epub 2021 Apr 16.
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.
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.
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.
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.
自2019年12月新型冠状病毒肺炎（COVID-19）出现以来，多学科研究团队一直在努力应对如何根据其对身体、心理和经济造成的巨大损害，最好地控制这一疫情。大规模检测被倡导为一种潜在的补救措施；然而，使用物理检测进行大规模检测是一种成本高昂且难以扩大规模的解决方案。
本研究展示了一种通过使用音频生物标志物和深度学习利用数字技术进行COVID-19检测的替代形式的可行性。具体而言，我们表明可以训练一个基于深度神经网络的模型，使用呼吸和咳嗽音频记录来检测有症状和无症状的COVID-19病例。
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表现出强大的实证性能，在COVID-19分类任务中，受试者操作特征曲线下面积达到0.846。
鉴于低成本、高度可扩展的数字COVID-19诊断工具具有明显优势，本研究为使用咳嗽和呼吸音频信号诊断COVID-19提供了概念验证，并推动了对更广泛数据样本的全面后续研究。
