AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App
arXiv:2004.01275 · doi:10.1016/j.imu.2020.100378
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-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.
arXiv commentAccepted in Informatics in Medicine Unlocked 2020
References in corpus (1)
Cited by in corpus (19)
- Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
- The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
- COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
- COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features
- A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels
- Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
- Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice
- Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
- A literature review on COVID-19 disease diagnosis from respiratory sound data
- Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition
- QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds
- Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification
- Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
- Automatic Tuberculosis and COVID-19 cough classification using deep learning
- Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data
- COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
- Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework
- Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation
- Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Example