Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning

• URL: http://arxiv.org/abs/2005.11856v3
• Date: Tue, 30 Jun 2020 17:09:53 GMT
• Title: Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
• Authors: Joseph Paul Cohen and Lan Dao and Paul Morrison and Karsten Roth and Yoshua Bengio and Beiyi Shen and Almas Abbasi and Mahsa Hoshmand-Kochi and Marzyeh Ghassemi and Haifang Li and Tim Q Duong
• Abstract summary: 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 that can be used for escalation or de-escalation of care.
• Score: 57.00601760750389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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