A literature review on COVID-19 disease diagnosis from respiratory sound data

TL;DR

This paper reviews existing literature on diagnosing COVID-19 using respiratory sounds like cough and breath, highlighting AI techniques and digital tools to aid clinical diagnosis and support ongoing research efforts.

Contribution

It provides a comprehensive overview of methods and AI-based algorithms for COVID-19 diagnosis from respiratory sounds, emphasizing the potential for accessible digital health solutions.

Findings

Respiratory sound analysis can aid COVID-19 diagnosis.

AI algorithms show promise in classifying COVID-19 from respiratory data.

The review encourages open access research for better pandemic response.

Abstract

The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day, in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.