  In the break of COVID-19 pandemic, mass testing has become essential to
reduce the spread of the virus. Several recent studies suggest that a
significant number of COVID-19 patients display no physical symptoms
whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19
test, which increases their chances of unintentionally spreading the virus.
Currently, the primary diagnostic tool to detect COVID-19 is RT-PCR test on
collected respiratory specimens from the suspected case. This requires patients
to travel to a laboratory facility to be tested, thereby potentially infecting
others along the way.It is evident from recent researches that asymptomatic
COVID-19 patients cough and breath in a different way than the healthy people.
Several research groups have created mobile and web-platform for crowdsourcing
the symptoms, cough and breathing sounds from healthy, COVID-19 and Non-COVID
patients. Some of these data repositories were made public. We have received
such a repository from Cambridge University team under data-sharing agreement,
where we have cough and breathing sound samples for 582 and 141 healthy and
COVID-19 patients, respectively. 87 COVID-19 patients were asymptomatic, while
rest of them have cough. We have developed an Android application to
automatically screen COVID-19 from the comfort of people homes. Test subjects
can simply download a mobile application, enter their symptoms, record an audio
clip of their cough and breath, and upload the data anonymously to our servers.
Our backend server converts the audio clip to spectrogram and then apply our
state-of-the-art machine learning model to classify between cough sounds
produced by COVID-19 patients, as opposed to healthy subjects or those with
other respiratory conditions. The system can detect asymptomatic COVID-19
patients with a sensitivity more than 91%.
