当前位置: X-MOL 学术arXiv.cs.SD › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
arXiv - CS - Sound Pub Date : 2020-09-17 , DOI: arxiv-2009.08790
Piyush Bagad, Aman Dalmia, Jigar Doshi, Arsha Nagrani, Parag Bhamare, Amrita Mahale, Saurabh Rane, Neeraj Agarwal, Rahul Panicker

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

中文翻译:

针对 COVID 的咳嗽:咳嗽声音中 COVID-19 特征的证据

由于缺乏足够的物资、训练有素的人员和样本处理设备,COVID-19 的测试能力在全球范围内仍然是一项挑战。这些问题在农村和欠发达地区更为突出。我们证明,当通过我们的 AI 模型分析时,通过电话收集的请求咳嗽声音具有指示 COVID-19 状态的统计显着信号(AUC 0.72,t 检验,p <0.01,95% CI 0.61-0.83)。这也适用于无症状患者。为此,我们从 3,621 个人中收集了最大的已知(迄今为止)微生物学证实的 COVID-19 咳嗽声音数据集。当用于整个测试方案中的分类步骤时,通过在确认测试之前对个人进行风险分层,
更新日期:2020-09-24
down
wechat
bug