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.
\nResults: 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[22],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts\/1338"}],"collection":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/comments?post=1338"}],"version-history":[{"count":3,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts\/1338\/revisions"}],"predecessor-version":[{"id":1442,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts\/1338\/revisions\/1442"}],"wp:attachment":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/media?parent=1338"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/categories?post=1338"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/tags?post=1338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}