A new study published in Cureus presents a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the 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.
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 the task.
This study finds that training a regression model on a subset of the outputs from 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.
These results indicate that the study model’s ability to gauge the 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 ICU. To enable follow up work, the study authors have made their code, labels, and data available online.
Cohen J, Dao L, Roth K, et al. (July 28, 2020) Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning. Cureus 12(7): e9448. doi:10.7759/cureus.9448
Featured image: Figure 4: Saliency maps of model predictions. Examples of correct (a,b) and incorrect (c,d) predictions by the model are shown with a saliency map generated by computing the gradient of the output prediction with respect to the input image and then blurred using a 5×5 Gaussian kernel. The assigned and predicted scores for Geographic Extent are shown to the right.