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Background: A critical step in effective care and treatment planning forsevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of theCOVID-19 pandemic, is the assessment of the severity of disease progression.Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with twoimportant assessment metrics being extent of lung involvement and degree ofopacity. In this proof-of-concept study, we assess the feasibility ofcomputer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deeplearning system.  Materials and Methods: Data consisted of 396 CXRs from SARS-CoV-2 positivepatient cases. Geographic extent and opacity extent were scored by twoboard-certified expert chest radiologists (with 20+ years of experience) and a2nd-year radiology resident. The deep neural networks used in this study, whichwe name COVID-Net S, are based on a COVID-Net network architecture. 100versions of the network were independently learned (50 to perform geographicextent scoring and 50 to perform opacity extent scoring) using random subsetsof CXRs from the study, and we evaluated the networks using stratified MonteCarlo cross-validation experiments.  Findings: The COVID-Net S deep neural networks yielded R$^2$ of 0.664 $\pm$0.032 and 0.635 $\pm$ 0.044 between predicted scores and radiologist scores forgeographic extent and opacity extent, respectively, in stratified Monte Carlocross-validation experiments. The best performing networks achieved R$^2$ of0.739 and 0.741 between predicted scores and radiologist scores for geographicextent and opacity extent, respectively.  Interpretation: The results are promising and suggest that the use of deepneural networks on CXRs could be an effective tool for computer-aidedassessment of SARS-CoV-2 lung disease severity, although additional studies areneeded before adoption for routine clinical use.

COVID-Net S: Towards computer-aided severity assessment via training and  validation of deep neural networks for geographic extent and opacity extent  scoring of chest X-rays for SARS-CoV-2 lung disease severity