Author: Bagad, Piyush; Dalmia, Aman; Doshi, Jigar; Nagrani, Arsha; Bhamare, Parag; Mahale, Amrita; Rane, Saurabh; Agarwal, Neeraj; Panicker, Rahul
Title: Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds Cord-id: ggdf8l6f Document date: 2020_9_17
ID: ggdf8l6f
Snippet: 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 c
Document: 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
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