Work

COVID-19 detection from audio: seven grains of salt

Year: 2021

Type: article

Abstract: Digital mass testing for COVID-19 via a mobile phone application could be made possible through machine learning and its ability to identify patterns in data. COVID-19 appears to confer unique features in the audio produced by infected individuals,1 and machine learning COVID-19 detection from breath, cough, and speech audio recordings has yielded promising results.2–4 In this critique, we present seven major issues with this research and argue that further investigation is needed before conclusions about the detectability of COVID-19 from audio can be made.

Source: The Lancet Digital Health

DOI: 10.1016/s2589-7500(21)00141-2

Authors: Harry Coppock, Lyn Jones, Ivan Kiskin, Björn Schuller

Institutions: Imperial College London, North Bristol NHS Trust, University of Oxford

Language: English

Topic: Phonocardiography and Auscultation Techniques

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,000