  Purpose: The need to streamline patient management for COVID-19 has become
more pressing than ever. Chest X-rays provide a non-invasive (potentially
bedside) tool to monitor the progression of the disease. In this study, we
present a severity score prediction model for COVID-19 pneumonia for frontal
chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections
(and pneumonia in general) that can be used for escalation or de-escalation of
care as well as monitoring treatment efficacy, especially in the ICU.
  Methods: Images from a public COVID-19 database were scored retrospectively
by three blinded experts in terms of the extent of lung involvement as well as
the degree of opacity. A neural network model that was pre-trained on large
(non-COVID-19) chest X-ray datasets is used to construct features for COVID-19
images which are predictive for our task.
  Results: This study finds that training a regression model on a subset of the
outputs from an this pre-trained chest X-ray model predicts our geographic
extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung
opacity score (range 0-6) with 0.78 MAE.
  Conclusions: These results indicate that our model's ability to gauge
severity of COVID-19 lung infections could be used for escalation or
de-escalation of care as well as monitoring treatment efficacy, especially in
the intensive care unit (ICU). A proper clinical trial is needed to evaluate
efficacy. To enable this we make our code, labels, and data available online at
https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-severity and
https://github.com/ieee8023/covid-chestxray-dataset
