Abstract

BACKGROUND:

Recent occurrence of the 2019 coronavirus disease (COVID-19) outbreak, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the need for fast, accurate, and simple strategies to identify cases on a large scale.

OBJECTIVE:

This study aims to develop and test an accurate detection and severity classification methodology that may help medical professionals and non-radiologists recognize the behavior and propagation mechanisms of the virus by viewing computed tomography (CT) images of the lungs with implicit materials.

METHODS:

In this study, the process of detecting the virus began with the deployment of a virtual material inside CT images of the lungs of 128 patients. Virtual material is a hypothetical material that can penetrate the healthy regions in the image by performing sequential numerical measurements to interpret images with high data accuracy. The proposed method also provides a segmented image of only the healthy parts of the lung.

RESULTS:

The resulting segmented images, which represent healthy parts of the lung, are classified into six levels of severity. These levels are classified according to physical symptoms. The results of the proposed methodology are compared with those of the radiologists’ reports. This comparison revealed that the gold-standard reports correlated with the results of the proposed methodology with a high accuracy rate of 93%.

CONCLUSION:

The study results indicate the possibility of relying on the proposed methodology for discovering the effects of the SARS-CoV-2 virus in the lungs through CT imaging analysis with limited dependency on radiologists.

中文翻译：

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开发一种使用计算机断层摄影图像进行COVID-19病例的早期发现和严重程度分类的新型计算方法

摘要

背景：

由严重急性呼吸系统综合症冠状病毒2（SARS-CoV-2）引起的2019年冠状病毒病（COVID-19）爆发最近发生，突显了需要快速，准确和简单的策略来大规模识别病例。

目的：

这项研究旨在开发和测试一种准确的检测和严重性分类方法，该方法可通过查看含隐性材料的肺部计算机断层扫描（CT）图像来帮助医学专业人士和非放射线医师识别病毒的行为和传播机制。

方法：

在这项研究中，检测病毒的过程始于在128位患者的肺部CT图像内部署虚拟材料。虚拟材料是一种假想的材料，可以通过执行连续的数值测量来解释具有高数据准确性的图像，从而可以穿透图像中的健康区域。所提出的方法还提供了仅肺部健康部分的分割图像。

结果：

代表肺部健康部位的分割图像分为六个严重级别。这些水平根据身体症状分类。拟议方法的结果与放射科医生的报告进行了比较。这种比较表明，金标准报告与拟议方法的结果相关，准确率高达93％。

结论：

研究结果表明，可以依靠拟议的方法通过CT成像分析发现SARS-CoV-2病毒在肺中的作用，而对放射科医生的依赖性有限。