论文标题
咳嗽反对COVID：COVID-19签名的证据在咳嗽声音中
Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
论文作者
论文摘要
由于缺乏足够的供应，训练有素的人员和样品加工设备，因此Covid-19的测试能力在全球范围内仍然是一个挑战。在农村和欠发达地区，这些问题更加敏锐。我们证明，当我们的AI模型分析时，通过电话收集的咳嗽声具有统计学意义的信号，指示COVID-19的状态（AUC 0.72，t检验，p <0.01,95％CI 0.61-0.83）。这也适用于无症状的患者。为此，我们收集了来自3,621个人的微生物学确认的Covid-19咳嗽声的最大的（迄今为止）的最大（迄今为止）的数据集。当在整体测试方案中使用分列步骤时，通过在确认性测试之前实现风险分层，我们的工具可以在5％的患病率下将医疗保健系统的测试能力提高43％，没有其他供应，训练有素的人员或身体基础设施或物理基础设施
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
