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

By Ali Imran, Iryna Posokhova, Haneya Naeem Qureshi, Usama Masood, Sajid Riaz, K. Ali, Charles N. John, Muhammad Nabeel. Informatics in Medicine Unlocked, 2020

546 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/809572ae170df1c479bf13f406ea8ac7e2045eb4

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection (2020, background): “Many research projects and innovations related to COVID-19 have been proposed [20]–[22].”
Coswara - A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis (2020, background): “Also, a recent work by Imran et al. [16] suggests a good accuracy for cough based detection of COVID-19 using a preliminary investigation with a small number of subjects.”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “Then the residual errors between the actual and predicted values can be obtained using this relation [34] : Here,”
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, background): “Recent studies have shown that applying convolutional neural networks to CT scans can help extract meaningful radiological features for COVID-19 diagnosis and facilitate automatic pulmonary CT screening as well as cough monitoring [25, 26, 27, 28].”
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data (2020, methodology): “In [17] a study of detection of coughs related to COVID-19 is presented using a cohort of 48 COVID-19 tested patients versus other pathology coughs on which an AI model is trained.”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, methodology): “[159] presents an app called AI4COVID-19 for the diagnosis of COVID-19.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “Finally, a growing number of efforts aim to utilize mobile phones for COVID-19 detection, using e.g. embedded sensors to identify COVID-19 symptoms [84] † , phonebased surveys to filter high-risk patients based on responses to key questions regarding travel and symptoms [85] , or the analysis of cou”
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, background): “which may be encrypted and compressed before sending to the cloud for training [39], [63].”
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