AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via
an app
#MMPMID32839734
Imran A
; Posokhova I
; Qureshi HN
; Masood U
; Riaz MS
; Ali K
; John CN
; Hussain MI
; Nabeel M
Inform Med Unlocked
2020[]; 20
(?): 100378
PMID32839734
show ga
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-s cough
sounds to an AI engine running in the cloud, and returns a result within 2 min.
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.
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