Artificial intelligence in COVID-19

The COVID-19 pandemic has taken the world by storm, placing healthcare systems around the globe under immense pressure. The exceptional circumstance has made the scientific community turn to artificial intelligence (AI), with hopes that AI techniques can be used in all aspects of combating the pandemic, whether it is in using AI to uncover sequences in the genomic code of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) virus for the purposes of developing therapeutics, such as antivirals, antibodies, or vaccines, or using AI to provide (near-) instantaneous clinical diagnosis techniques by way of analysis of chest X-ray (CXR) images, computed tomography (CT) scans or other useful modalities, or using AI for as a tool for mass population testing by analyzing patient audio recordings. In this chapter, we survey the AI research literature with respect to applications for COVID-19 and showcase and critique notable state of the art approaches. © 2023 Elsevier Inc. All rights reserved.

Detecting COVID-19 from breathing and coughing sounds using deep neural networks

The COVID-19 pandemic has affected the world unevenly;while industrial economies have been able to produce the tests necessary to track the spread of the virus and mostly avoided complete lockdowns, developing countries have faced issues with testing capacity. In this paper, we explore the usage of deep learning models as a ubiquitous, low-cost, pre-testing method for detecting COVID-19 from audio recordings of breathing or coughing taken with mobile devices or via the web. We adapt an ensemble of Convolutional Neural Networks that utilise raw breathing and coughing audio and spectrograms to classify if a speaker is infected with COVID-19 or not. The different models are obtained via automatic hyperparameter tuning using Bayesian Optimisation combined with HyperBand. The proposed method outperforms a traditional baseline approach by a large margin. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks, considering the best test set result across breathing and coughing in a strictly subject independent manner. In isolation, breathing sounds thereby appear slightly better suited than coughing ones (76.1% vs 73.7% UAR). © 2021 IEEE.