The COVID-19 pandemic has affected the world unevenly; while industrial
economies have been able to produce the tests necessary to track the spread of
the virus and mostly avoided complete lockdowns, developing countries have
faced issues with testing capacity. In this paper, we explore the usage of deep
learning models as a ubiquitous, low-cost, pre-testing method for detecting
COVID-19 from audio recordings of breathing or coughing taken with mobile
devices or via the web. We adapt an ensemble of Convolutional Neural Networks
that utilise raw breathing and coughing audio and spectrograms to classify if a
speaker is infected with COVID-19 or not. The different models are obtained via
automatic hyperparameter tuning using Bayesian Optimisation combined with
HyperBand. The proposed method outperforms a traditional baseline approach by a
large margin. Ultimately, it achieves an Unweighted Average Recall (UAR) of
74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks,
considering the best test set result across breathing and coughing in a
strictly subject independent manner. In isolation, breathing sounds thereby
appear slightly better suited than coughing ones (76.1% vs 73.7% UAR).

本研究探讨了使用深度学习模型作为一种通用的、低成本的 COVID-19 预筛查方法，以从手机设备或通过 Web 获取的呼吸或咳嗽音频中检测 COVID-19，通过深度学习模型的集成表现优于传统方法，特别针对呼吸音频具有更好的诊断性能。

使用深度神经网络从呼吸和咳嗽声音中检测 COVID-19

Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural  Networks

Testing capacity for COVID-19 remains a challenge globally due to the lack of
adequate supplies, trained personnel, and sample-processing equipment. These
problems are even more acute in rural and underdeveloped regions. We
demonstrate that solicited-cough sounds collected over a phone, when analysed
by our AI model, have statistically significant signal indicative of COVID-19
status (AUC 0.72, t-test,p <0.01,95% CI 0.61-0.83). This holds true for
asymptomatic patients as well. Towards this, we collect the largest known(to
date) dataset of microbiologically confirmed COVID-19 cough sounds from 3,621
individuals. When used in a triaging step within an overall testing protocol,
by enabling risk-stratification of individuals before confirmatory tests, our
tool can increase the testing capacity of a healthcare system by 43% at disease
prevalence of 5%, without additional supplies, trained personnel, or physical
infrastructure

在 COVID-19 测试能力不足的情况下，一种基于人工智能模型的咳嗽声音分析方法结合症状调查，能有效提高较为贫困的地区的 COVID-19 检测能力，并对 2000 多个样本进行了测试。