论文标题
通过深度学习预测胸部X射线上的Covid-19肺炎严重程度
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
论文作者
论文摘要
目的：简化Covid-19的患者管理的需求比以往任何时候都变得更加紧迫。胸部X射线提供了一种非侵入性（潜在的床边）工具来监测疾病的进展。在这项研究中，我们提出了额叶X射线图像的Covid-19肺炎的严重性评分预测模型。这样的工具可以评估可用于升级或降低护理的肺部感染（和肺炎）的严重程度（以及一般的肺炎），并监测治疗效果，尤其是在ICU中。 方法：从肺参与程度和不透明度程度来看，三名失明的专家对公共Covid-19数据库的图像进行了回顾性评分。在大型（非旋转-19）胸部X射线数据集上进行了预训练的神经网络模型用于构建可预测我们任务的COVID-19图像的功能。 结果：这项研究发现，从该预训练的胸部X射线模型的输出子集上训练回归模型可以预测我们的地理范围得分（范围0-8），其平均绝对误差为1.14，我们的肺不透明度得分（范围0-6）和0.78 MAE。 结论：这些结果表明，我们的模型评估CoVID-19肺部感染严重程度的能力可用于升级或降级护理以及监测治疗功效，尤其是在重症监护病房（ICU）。需要进行适当的临床试验来评估功效。 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
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
