  Rapid and affordable methods of testing for COVID-19 infections are essential
to reduce infection rates and prevent medical facilities from becoming
overwhelmed. Current approaches of detecting COVID-19 require in-person testing
with expensive kits that are not always easily accessible. This study
demonstrates that crowdsourced cough audio samples recorded and acquired on
smartphones from around the world can be used to develop an AI-based method
that accurately predicts COVID-19 infection with an ROC-AUC of 77.1%
(75.2%-78.3%). Furthermore, we show that our method is able to generalize to
crowdsourced audio samples from Latin America and clinical samples from South
Asia, without further training using the specific samples from those regions.
As more crowdsourced data is collected, further development can be implemented
using various respiratory audio samples to create a cough analysis-based
machine learning (ML) solution for COVID-19 detection that can likely
generalize globally to all demographic groups in both clinical and non-clinical
settings.
