Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
Joseph Paul Cohen, Lan Dao, Paul Morrison, Karsten Roth and, Yoshua Bengio, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi and, Marzyeh Ghassemi, Haifang Li, Tim Q Duong

TL;DR
This paper presents a deep learning model that predicts COVID-19 pneumonia severity from chest X-rays, aiding clinical decision-making and patient management, especially in ICU settings.
Contribution
The study introduces a novel severity scoring model for COVID-19 pneumonia using pre-trained neural networks on chest X-ray images, validated against expert annotations.
Findings
Predicts lung involvement with 1.14 MAE
Accurately estimates lung opacity with 0.78 MAE
Code and data made publicly available
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…
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging · Lung Cancer Diagnosis and Treatment
