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Background: Chest computed tomography (CT) is recognized as an important toolfor COVID-19 severity assessment. As the number of affected patients increaserapidly, manual severity assessment becomes a labor-intensive task, and maylead to delayed treatment. Purpose: Using machine learning method to realizeautomatic severity assessment (non-severe or severe) of COVID-19 based on chestCT images, and to explore the severity-related features from the resultingassessment model. Materials and Method: Chest CT images of 176 patients (age45.3$\pm$16.5 years, 96 male and 80 female) with confirmed COVID-19 are used,from which 63 quantitative features, e.g., the infection volume/ratio of thewhole lung and the volume of ground-glass opacity (GGO) regions, arecalculated. A random forest (RF) model is trained to assess the severity(non-severe or severe) based on quantitative features. Importance of eachquantitative feature, which reflects the correlation to the severity ofCOVID-19, is calculated from the RF model. Results: Using three-fold crossvalidation, the RF model shows promising results, i.e., 0.933 of true positiverate, 0.745 of true negative rate, 0.875 of accuracy, and 0.91 of area underreceiver operating characteristic curve (AUC). The resulting importance ofquantitative features shows that the volume and its ratio (with respect to thewhole lung volume) of ground glass opacity (GGO) regions are highly related tothe severity of COVID-19, and the quantitative features calculated from theright lung are more related to the severity assessment than those of the leftlung. Conclusion: The RF based model can achieve automatic severity assessment(non-severe or severe) of COVID-19 infection, and the performance is promising.Several quantitative features, which have the potential to reflect the severityof COVID-19, were revealed.

Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images