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In the break of COVID-19 pandemic, mass testing has become essential toreduce the spread of the virus. Several recent studies suggest that asignificant number of COVID-19 patients display no physical symptomswhatsoever. Therefore, it is unlikely that these patients will undergo COVID-19test, which increases their chances of unintentionally spreading the virus.Currently, the primary diagnostic tool to detect COVID-19 is RT-PCR test oncollected respiratory specimens from the suspected case. This requires patientsto travel to a laboratory facility to be tested, thereby potentially infectingothers along the way.It is evident from recent researches that asymptomaticCOVID-19 patients cough and breath in a different way than the healthy people.Several research groups have created mobile and web-platform for crowdsourcingthe symptoms, cough and breathing sounds from healthy, COVID-19 and Non-COVIDpatients. Some of these data repositories were made public. We have receivedsuch a repository from Cambridge University team under data-sharing agreement,where we have cough and breathing sound samples for 582 and 141 healthy andCOVID-19 patients, respectively. 87 COVID-19 patients were asymptomatic, whilerest of them have cough. We have developed an Android application toautomatically screen COVID-19 from the comfort of people homes. Test subjectscan simply download a mobile application, enter their symptoms, record an audioclip 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 ourstate-of-the-art machine learning model to classify between cough soundsproduced by COVID-19 patients, as opposed to healthy subjects or those withother respiratory conditions. The system can detect asymptomatic COVID-19patients with a sensitivity more than 91%.

QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds