AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app

Ali Imran, Iryna Posokhova, Haneya N Qureshi, Usama Masood, Muhammad Sajid Riaz, Kamran Ali, Charles N John, Md Iftikhar Hussain, Muhammad Nabeel, Ali Imran, Iryna Posokhova, Haneya N Qureshi, Usama Masood, Muhammad Sajid Riaz, Kamran Ali, Charles N John, Md Iftikhar Hussain, Muhammad Nabeel

Abstract

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.

Keywords: Artificial intelligence; COVID-19; Pre-screening; Preliminary medical diagnosis; Public healthcare.

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

© 2020 The Authors.

Figures

Fig. 1
Fig. 1
Visualization of features for the four classes via t-SNE (gray triangles correspond to normal, blue circles correspond to bronchitis, black stars correspond to pertussis and orange diamonds represent COVID-19 cough. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2
Fig. 2
Proposed system architecture and flow diagram of AI4COVID-19, showing snapshot of Smartphone App at user front-end and back-end cloud AI-engine blocks consisting of Cough Detector block (further elaborated in Fig. 4 and Section 2.3) and COVID-19 diagnosis block containing Deep Transfer Learning-based Multi-Class classifier (DTL-MC), Classical Machine Learning-based Multi-Class classifier (CML-MC) and Deep Transfer Learning-based Binary-Class classifier (DTL-BC) (further elaborated in Fig. 5 and Section 2.3).
Fig. 3
Fig. 3
A flow chart highlighting the steps of the proposed system.
Fig. 4
Fig. 4
Cough detection classifier.
Fig. 5
Fig. 5
Classical Machine Learning-based Multi-Class classifier (CML-MC).
Fig. 6
Fig. 6
Normalized mean confusion matrix for cough detection (in percentage) using 5-fold cross validation.
Fig. 7
Fig. 7
Mean model loss for 5-fold cross validation of cough detector.
Fig. 8
Fig. 8
Normalized mean confusion matrix for cough diagnosis (in percentage) for DTL-MC using 5-fold cross validation.
Fig. 9
Fig. 9
Mean model loss for 5-fold cross validation of DTL-MC.
Fig. 10
Fig. 10
Normalized mean confusion matrix for cough diagnosis (in percentage) for CML-MC using 5-fold cross validation.
Fig. 11
Fig. 11
Overall accuracy CDF for varying k-fold experiments in CML-MC approach.
Fig. 12
Fig. 12
Normalized mean confusion matrix for cough diagnosis (in percentage) for DTL-BC using 5-fold cross validation.
Fig. 13
Fig. 13
Mean model loss for 5-fold cross validation of DTL-BC.

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