PDF(Pubmed)
Abstract：
Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.
摘要：
医学成像方法在COVID-19患者的检查中发挥着更大的作用，主要与肺部疾病的主要表现和血管紧张素转换酶2（ACE2）受体的组织分布有关。然而,该领域是如此新，以至于没有共识的观点出现，以指导临床决策采用影像学检查等程序，计算机断层扫描(CT),正电子发射断层扫描（PET），和磁共振成像，以及所获得的知识可以证明工作人员暴露于可能感染的风险。当前RT-PCR方法对阳性诊断的敏感性是诉诸成像程序的基本原理的一部分。虽然在住院患者中CT比基因检测更敏感，毛玻璃混浊的阳性发现取决于疾病阶段。在COVID-19中，[18F]-FDG的PET/CT报告很少，但现有结果与早期关于病毒性肺炎的文献一致。COVID-19的脑部发现发生率很高，同样有胃肠道受累的证据。人工智能,特别是机器学习正在成为诊断图像分析的有效方法，在COVID-19肺炎诊断的鉴别诊断方面的表现与人类从业者相当。
