AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Open accessAbstract
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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- Authors
- Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M.
- Affiliations
- University of Oklahoma (US)
- Kharkiv National Medical University (UA)
- Michigan State University (US)
- Corresponding author
- Nabeel, Muhammad
- ORCID
- Nabeel, Muhammad
- Venue
- Informatics in Medicine Unlocked
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 20
- Pages
- 100378
- ISSN
- 2352-9148 (ISSN-L)
- PMC
- PMC7318970
- OpenAlex
- W3015034944
- Cited by
- 621
- References
- 43
- Citations by year
- 2026: 13 · 2025: 41 · 2024: 56 · 2023: 86 · 2022: 143 · 2021: 229 · 2020: 53
- FWCI
- 71.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Informatics in Medicine Unlocked (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- COVID-19 pandemic, mobile application, mobile health screening, transfer learning, respiratory infections, clinical decision support, cough sound classification
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