Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification

ABSTRACT: To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43?±?14.02; 62 males) were included. GGO?+?consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33?±?167.88 cm3 versus 101.12?±?127 cm3, p?=?0.013) as well as consolidation volume (40.85?±?60.4 cm3 versus 6.63?±?14.91 cm3, p?

SUBMITTER: Li Y 

PROVIDER: S-EPMC7745019 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature