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 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.
Artificial intelligence, COVID-19, Preliminary medical diagnosis, Pre-screening, Public healthcare
By April 28, 2020, there were 3,024,059 confirmed cases of coronavirus disease 2019 (COVID-19), leading to 208,112 deaths and disrupting life in 213 countries and territories around the world . The losses are compounding every day. Given no vaccination or cure exists as of now, minimizing the spread by timely testing the population and isolating the infected people is the only effective defense against the unprecedentedly contagious COVID-19. However, the ability to deploy this defense strategy at this stage of pandemic hinges on a nation’s ability to timely test significant fractions of its population including those who are not contacting the medical system yet. The capability for agile, scalable, and proactive testing has emerged as the key differentiator in some nations’ ability to cope and reverse the curve of the pandemic, and the lack of the same is the root cause of historic losses for others.
- Why might not clinic visit based COVID-19 testing mechanisms alone sufficiently control the pandemic at this stage?
The “Trace, Test and Treat” strategy succeeded in flattening the pandemic curve (e.g., in South Korea, China, and Singapore) in its early stages. However, in many parts of the world, the pandemic has already spread to an extent that this strategy is not proving effective anymore . Recent studies show that it is a virus often transmitted when an undiagnosed population coughs, which contributes to its much rapid and covert spread . Data shows that 81% of COVID-19 carriers do not develop severe enough symptoms for them to seek medical help, and yet they act as active spreaders . Others develop symptoms severe enough to prompt medical intervention only after several days of being infected. These findings call for a new strategy centered on “Pre-screen/test proactively at population scale, self-isolate those tested positive for self-healing without further spreading and channel medical care towards the most vulnerable”.
As per World Health Organization (WHO) guidance, Nucleic Acid Amplification Tests (NAAT) such as real-time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) should be used for routine confirmation of COVID-19 cases by detecting unique sequences of virus ribonucleic acid (RNA). This test method, while being the current gold standard, is not an adequate way to control the pandemic for reasons that include but are not limited to:
1)The limited availability of testing due to geographical and temporal factors.
2) The scarcity and expense of clinical tests needed to cover the massive time-sensitive demand.
3) The requirement of in-person visits to a hospital, clinic, lab, or mobile lab. Such visits expose more members of the public to COVID-19. This is not a trivial problem given the recent studies that show how highly stable and hence contagious COVID-19 appears to be. For example , shows that the aerosol stability of COVID-19 is up to 3 h in aerosols and up to seven days on different surfaces.
4) The turnaround time for current tests is several days, recently stretching to 10 days in some countries as labs are becoming overwhelmed [6,7]. By the time a patient is diagnosed using current methods, the virus has already been passed to many.
5) The in-person testing methods put the medical staff, particularly those with limited protection, at serious risk of infection. The inability to protect our medics can lead to a further shortage of medical care and increased distress on the already stressed medical staff.
To make tests more readily accessible, on March 28th the United States Food and Drug Administration (FDA) approved a faster test that can yield results in 15 min . The test works similar to Polymerase Chain Reaction (PCR) by identifying a portion of the COVID-19 RNA in the nasopharyngeal or oropharyngeal swab. The FDA also recently approved another rapid molecular-based test, which delivers positive results in as little as 5 min and negative results in 13 min . However, the FDA warns that there is a high probability of false-negative results using this test . While a leap forward, this test still requires an office visit and thus the breaching of social distancing and self-isolation. Though much faster, the newly approved test still does not solve many of the aforementioned problems. Furthermore, emerging reports of shortages of critical equipment used to collect patient specimens, like masks and swabs, could blunt its impact on controlling the pandemic [11,12]. In order to protect others from potential exposure, the FDA has also approved at-home sample collection . However, once a patient collects a nasal sample, they need to put it in a saline solution and ship it overnight to a certified lab authorized to run specific tests on the kit. Hence, this approach also introduces delays and could compromise on the quality of samples if the sample is stored for too long. In addition, it could also introduce the chances of errors while collecting the sample, since the patients collect the sample themselves, rather than trained doctors or healthcare professionals.
Scarcity, cost, and long turnaround time of clinical testing are key factors behind the covert rapid spread of the COVID-19 pandemic. Motivated by the urgent need, this paper presents a ubiquitously deployable AI-based preliminary diagnosis tool for COVID-19 using cough sound via a mobile app. The core idea of the tool is inspired by our independent prior studies that show cough can be used as a test medium for the diagnosis of a variety of respiratory diseases using AI. To see if this idea is extendable to COVID-19, we perform in-depth differential analysis of the pathomorphological alternations caused by COVID-19 relative to other cough causing medical conditions. We note that the way COVID-19 affects the respiratory system is substantially unique and hence, cough associated with it is likely to have unique latent features as well. We validate the idea further by the visualization of latent features in the cough of COVID-19 patients and two common infections, pertussis, and bronchitis as well as non-infectious coughs. Building on the insights from the medical domain knowledge, we propose and develop a tri-pronged mediator centered AI-engine for the cough-based diagnosis of COVID-19, named AI4COVID-19. The results show that the AI4COVID-19 app is able to diagnose COVID-19 with negligible misdiagnosis probability thanks to its risk-avert architecture.
Despite its impressive performance, AI4COVID-19 is not meant to compete with clinical testing. Instead, it offers a unique functional tool for timely, cost-effective, and most importantly safe monitoring, tracing, tracking, and thus, controlling the rampant spread of the global pandemic by virtually enabling testing for everyone. While we are working on improving the AI4COVID-19, this paper is meant to present a proof of concept to encourage community support for more labeled data followed by large scale trials. We hope that the AI4COVID-19 app can be leveraged to pre-screen for COVID-19 at a population scale, particularly in regions around the world where the pandemic is spreading covertly due to the lack of testing. The AI4COVID-19 enabled tele-screening can alleviate the crushing burden on the overwhelmed medical systems around the world and help save countless lives.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
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FULL Paper PDF file:AI4COVID-19: AI-enabled preliminary diagnosis for COVID-19 from cough samples via an app
AI4COVID-19: AI-enabled preliminary diagnosis for COVID-19 from cough samples via an app
Received 4 May 2020, Revised 19 June 2020, Accepted 19 June 2020, Available online 26 June 2020.
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.