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

Abstract

The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) 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 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. 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 this pretrained 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 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, we make our code, labels, and data available online.

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

“…We found that both RALE and AI scores derived from CXRs can predict the need for mechanical ventilation and death in patients with COVID-19 pneumonia. Strong correlation between RALE and AI scores in our study (r 2 = 0.79–0.86) is similar to a recent study from Cohen et al (r 2 = 0.81–0.83) 14 . In a recent study on 697 patients with COVID-19 pneumonia with the same Qure.ai algorithm, the AI score was reported as an independent predictor of patients’ outcome 15 .…”

“…In a recent study on 697 patients with COVID-19 pneumonia with the same Qure.ai algorithm, the AI score was reported as an independent predictor of patients’ outcome 15 . Although our results are consistent with recent CXRs studies with both RALE and AI algorithm-generated severity assessment 14 , 16 , there are some notable differences. As opposed to prior studies on baseline CXRs at hospital admission 6 , we assessed the performance of severity assessment on serial CXRs.…”

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“…The suitability of X-ray and CT imaging for COVID-19 (see Figure 11 ), based on recommendations by WHO, is shown in Table 4 , where we discuss the workflow and the practical implementation. The major studies for automated COVID-19 diagnosis using the AI paradigm with X-rays and CT are [ 193 ] [ 194 ] [ 195 ] [ 21 ] [ 196 ] [ 197 ] [ 198 ] [ 199 ] [ 200 ] [ 201 ] [ 22 ] [ 23 ] [ 202 ] [ 192 ]. Due to their popularity in the scientific community, there are several open-source COVID-19 datasets available for X-ray and CT imaging modalities, such as the one from RSNA ( https://www.rsna.org ).…”

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

“…A similar approach to the proposed work was presented by Ozturk et al [37] in which they used the Darknet model with pretrained weights from the YOLO system [38], achieving the best results using CXR images until the publication of their article. Other approaches presented by Zhanh et al [39] include the use of a CNN as feature extractor and two different multilayer perceptron (MLP) as anomaly detection and confidence prediction; a DenseNet121 model presented by Cohen et al [40] established a baseline for an early version of the same dataset used in this research.…”