Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues.
Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation.
Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 1st August 2021. Terms were selected based on the target intervention (i.e. AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e. speech, breathing and cough). A narrative approach was used to summarize the extracted data.
Results: 21 studies and 8 Apps out of the 83 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 for cough-, breathing- or speech-based acoustic features.
Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. However, the proposed methods have not been tested clinically, understood neurophysiologicallly, nor validated with broad training and testing datasets.