  Background: The inability to test at scale has become humanity's Achille's
heel in the ongoing war against the COVID-19 pandemic. A scalable screening
tool would be a game changer. Building on the prior work on cough-based
diagnosis of respiratory diseases, we propose, develop and test an Artificial
Intelligence (AI)-powered screening solution for COVID-19 infection that is
deployable via a smartphone app. The app, named AI4COVID-19 records and sends
three 3-second cough sounds to an AI engine running in the cloud, and returns a
result within two minutes. Methods: Cough is a symptom of over thirty
non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19
infection by cough alone an extremely challenging multidisciplinary problem. We
address this problem by investigating the distinctness of pathomorphological
alterations in the respiratory system induced by COVID-19 infection when
compared to other respiratory infections. To overcome the COVID-19 cough
training data shortage we exploit transfer learning. To reduce the misdiagnosis
risk stemming from the complex dimensionality of the problem, we leverage a
multi-pronged mediator centered risk-averse AI architecture. Results: Results
show AI4COVID-19 can distinguish among COVID-19 coughs and several types of
non-COVID-19 coughs. The accuracy is promising enough to encourage a
large-scale collection of labeled cough data to gauge the generalization
capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool.
Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It
can also be a clinical decision assistance tool used to channel
clinical-testing and treatment to those who need it the most, thereby saving
more lives.
