Electrical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia.
Computers and communications Department, College of Engineering, Delta University for Science and Technology, Gamasa, Egypt.
J Xray Sci Technol. 2021;29(2):211-228. doi: 10.3233/XST-200794.
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.
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.
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.
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%.
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.
由严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）引起的 2019 年冠状病毒病（COVID-19）疫情的近期爆发，凸显了需要快速、准确和简单的策略来大规模识别病例。
本研究旨在开发和测试一种准确的检测和严重程度分类方法，通过观察肺部 CT 图像中的隐式材料，帮助医疗专业人员和非放射科医生识别病毒的行为和传播机制。
在这项研究中，病毒的检测过程始于在 128 名患者的肺部 CT 图像中部署虚拟材料。虚拟材料是一种假设的材料，可以通过进行顺序数值测量来穿透图像中的健康区域，从而以高精度解释图像。该方法还提供了仅肺部健康部分的分割图像。
表示肺部健康部分的分割图像被分为六个严重程度级别。这些级别根据身体症状进行分类。所提出方法的结果与放射科医生报告的结果进行了比较。这种比较表明，黄金标准报告与提出的方法的结果高度相关，准确率为 93%。
研究结果表明，通过使用 CT 成像分析来发现 SARS-CoV-2 病毒在肺部的影响，该方法具有很大的可能性，对放射科医生的依赖有限。
