Objectives
: To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data.
Methods
: We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into subcohorts with or without necessity of intensive care unit (ICU) treatment. Subcohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test.
Results
: We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all P<0.001 if not otherwise stated) with occurrence of ICU stay (R=0.74), length of ICU stay (R=0.81), lethal outcome (R=0.56) and length of hospital stay (R=0.33, P<0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R=0.60), lactate dehydrogenase (LDH) (R=0.60), troponin (TNTHS) (R=0.55) and c-reactive protein (CRP) (R=0.51). Differences (P<0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher Opacityscore (12.50 vs. 4.96) and severity of laboratory data changes such as CRP (11.64 vs. 5.07 mg/dl, P<0.01).
Conclusions
: Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.
Summary. Besides clinical and anamnestic data, image in formation from Cf-image data in the tumour border was used for classification of brain tumours. If the image re gions are properly selected a classification rate of 85% is obtained with a hierarchic classifier, although our study is based on only 139 patients.
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