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新冠疫情时代的影像学:大流行期间的经验教训。

Imaging in the COVID-19 era: Lessons learned during a pandemic.

作者信息

Sideris Georgios Antonios, Nikolakea Melina, Karanikola Aikaterini-Eleftheria, Konstantinopoulou Sofia, Giannis Dimitrios, Modahl Lucy

机构信息

Department of Radiology, University of Massachusetts Medical School, Baystate Medical Center, Springfield, MA 01199, United States.

Radiology Working Group, Society of Junior Doctors, Athens 11527, Greece.

出版信息

World J Radiol. 2021 Jun 28;13(6):192-222. doi: 10.4329/wjr.v13.i6.192.

Abstract

The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.

摘要

2019年冠状病毒病(COVID-19)大流行的第一年是充满前所未有的变化、科学突破和争议的一年。放射学界也未能幸免于全球医疗系统所面临的挑战。放射学在应对这场大流行中发挥了关键作用,无论是通过展示病毒的表现并指导患者管理,还是通过安全地处理患者并减少医院内的传播。由于这场大流行,日常放射学实践的各个方面都发生了重大变化,包括工作流程的改变、工作量的减少以及严格的感染控制策略。尽管挑战仍在持续,但我们已经获得了相当多的知识,这些知识将指导未来的创新。本综述的目的是提供关于影像学在COVID-19多方面表现诊断中作用的最新证据,并讨论这场大流行对全球放射科的影响,包括感染控制策略和癌症筛查的延迟。最后,探讨人工智能在COVID-19大流行中的潜在贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7857/8245753/0033bb4470ba/WJR-13-192-g001.jpg

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