Semiquantitative CT imaging as a tool in improving detection of ground glass patches in patients with COVID-19 pneumonia and for better follow-up

Abstract

BackgroundCoronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 influencing millions of people worldwide. It has clinical symptoms going from mild symptoms in about 80% of patients to a case mortality rate of about 2% in hospitalized patients associated with radiologic findings at chest CT which is showing multifocal bilateral ground glass opacities (GGO) and consolidative patches with subpleural and peri-bronchovascular predominant distribution. The role of chest CT in COVID-19 is very crucial, so this study hypothesized that increasing the accuracy and rapidity of CT in the detection of COVID-19-related pneumonia will offer rapid management and intervention of affected cases and gain better outcomes. The aim of this study is to offer and assess the ability of a software computer program in helping the radiologists in rapid detection of COVID-19 pneumonic criteria.ResultsThis cross-sectional study involved 73 patients with clinical symptoms and real-time polymerase chain reaction test positive results diagnosed as COVID-19. They were referred to perform chest CT; their CT images were sent to a separate workstation to be automated and processed through the COVID-19 detector, and compared the finding of the radiologist and the COVID-19 detector. The median number of lesions was 2 among the studied participants ranging from 1 to 12 lesions. The most common affected site of the lesions was the lower lobes. There was a significant strong agreement (P value < 0.001, kappa = 0.923) between the radiologist and the semiquantitative CT assessment in the detection of GGO among patients with COVID-19 pneumonia. Also, there were 6 patients who underwent follow-up by semiquantitative CT and radiologist; the median number of lesions was 1 among the studied participants ranging from 1 to 8 lesions. There was a significant strong agreement (P value = 0.001, Kappa = 0.856) between the radiologist and the semiquantitative CT assessment in the detection of GGO during follow-up among patients with COVID-19 pneumonia.ConclusionsThe tested computer program can accurately detect COVID-19 pneumonia as it has better visualization in detecting GGO for diagnosing and following up on COVID-19 pneumonia.