Prediction of COVID-19 using chest X-ray images

TL;DR

This paper presents an AI-based method for analyzing chest X-ray images to assist in diagnosing COVID-19 and estimating the risk of deterioration, aiding resource allocation during the pandemic.

Contribution

The study introduces a novel AI algorithm that quantifies COVID-19 risk from chest X-rays, improving diagnostic accuracy and patient triage.

Findings

AI algorithm effectively distinguishes COVID-19 from other lung conditions

Provides quantitative risk estimates for patient deterioration

Useful for resource management in pandemic hotspots

Abstract

COVID-19, also known as Novel Coronavirus Disease, is a highly contagious disease that first surfaced in China in late 2019. SARS-CoV-2 is a coronavirus that belongs to the vast family of coronaviruses that causes this disease. The sickness originally appeared in Wuhan, China in December 2019 and quickly spread to over 213 nations, becoming a global pandemic. Fever, dry cough, and tiredness are the most typical COVID-19 symptoms. Aches, pains, and difficulty breathing are some of the other symptoms that patients may face. The majority of these symptoms are indicators of respiratory infections and lung abnormalities, which radiologists can identify. Chest x-rays of COVID-19 patients seem similar, with patchy and hazy lungs rather than clear and healthy lungs. On x-rays, however, pneumonia and other chronic lung disorders can resemble COVID-19. Trained radiologists must be able to distinguish between COVID-19 and an illness that is less contagious. Our AI algorithm seeks to give doctors a quantitative estimate of the risk of deterioration. So that patients at high risk of deterioration can be triaged and treated efficiently. The method could be particularly useful in pandemic hotspots when screening upon admission is important for allocating limited resources like hospital beds.