COVID-19 severity stratification using quantitative computed tomography analysis

Abstract

Aim: This study aimed to examine the utility of computer-assisted quantitative assessment of chest
 computed tomography (CT) images in the stratification of Coronavirus Disease 2019 (COVID-19)
 severity.
 Materials and Methods: This study was designed as a retrospective, single-center study and
 included a total of 142 RT-PCR-confirmed COVID-19 patients. CT findings were visually evaluated
 and noted for their morphology and distribution characteristics. Visual semi-quantitative score (VSS)
 and computer-aided quantitative score (CQS) were calculated. The utility of the approach was
 assessed based on its ability to predict the patients who would require intensive care.
 Results: The presence of underlying fibrosis, air bubble sign, and co-occurrence of central and
 peripheral lung area involvement were the CT findings that were significantly more commonly
 encountered in patients with intensive care requirements during the follow-up period. We found a
 significant positive correlation between total VSS and CQS (p