ArXiv Preprint
Recent work has reported that AI classifiers trained on audio recordings can
accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2)
infection status. Here, we undertake a large scale study of audio-based deep
learning classifiers, as part of the UK governments pandemic response. We
collect and analyse a dataset of audio recordings from 67,842 individuals with
linked metadata, including reverse transcription polymerase chain reaction
(PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects
were recruited via the UK governments National Health Service Test-and-Trace
programme and the REal-time Assessment of Community Transmission (REACT)
randomised surveillance survey. In an unadjusted analysis of our dataset AI
classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver
Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854])
consistent with the findings of previous studies. However, after matching on
measured confounders, such as age, gender, and self reported symptoms, our
classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon
quantifying the utility of audio based classifiers in practical settings, we
find them to be outperformed by simple predictive scores based on user reported
symptoms.
Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker, Jobie Budd, Richard Payne, Emma Karoune, David Hurley, Alexander Titcomb, Sabrina Egglestone, Ana Tendero Cañadas, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel, Tracey Thornley, Peter Diggle, Sylvia Richardson, Josef Packham, Björn W. Schuller, Davide Pigoli, Steven Gilmour, Stephen Roberts, Chris Holmes
2022-12-15
