PT  - JOURNAL ARTICLE
AU  - Harry Coppock
AU  - Alex Gaskell
AU  - Panagiotis Tzirakis
AU  - Alice Baird
AU  - Lyn Jones
AU  - Björn Schuller
TI  - End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study
AID  - 10.1136/bmjinnov-2021-000668
DP  - 2021 Apr 01
TA  - BMJ Innovations
PG  - 356--362
VI  - 7
IP  - 2
4099  - http://innovations.bmj.com/content/7/2/356.short
4100  - http://innovations.bmj.com/content/7/2/356.full
SO  - BMJ Innov2021 Apr 01; 7
AB  - Background 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.Methods 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.Results 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.Conclusion 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.Data are available upon reasonable request.