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The outbreak of COVID-19 has lead to a global effort to decelerate thepandemic spread. For this purpose chest computed-tomography (CT) basedscreening and diagnosis of COVID-19 suspected patients is utilized, either as asupport or replacement to reverse transcription-polymerase chain reaction(RT-PCR) test. In this paper, we propose a fully automated AI based system thattakes as input chest CT scans and triages COVID-19 cases. More specifically, weproduce multiple descriptive features, including lung and infectionsstatistics, texture, shape and location, to train a machine learning basedclassifier that distinguishes between COVID-19 and other lung abnormalities(including community acquired pneumonia). We evaluated our system on a datasetof 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaboratedfeature analysis and ablation study to explore the importance of each feature.

Automated triage of COVID-19 from various lung abnormalities using chest CT features