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肺部超声教学:模拟与实践。

Lung ultrasound education: simulation and hands-on.

机构信息

Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.

School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK.

出版信息

Br J Radiol. 2021 Mar 1;94(1119):20200755. doi: 10.1259/bjr.20200755. Epub 2020 Dec 22.

Abstract

COVID-19 can cause damage to the lung, which can result in progressive respiratory failure and potential death. Chest radiography and CT are the imaging tools used to diagnose and monitor patients with COVID-19. Lung ultrasound (LUS) during COVID-19 is being used in some areas to aid decision-making and improve patient care. However, its increased use could help improve existing practice for patients with suspected COVID-19, or other lung disease. A limitation of LUS is that it requires practitioners with sufficient competence to ensure timely, safe, and diagnostic clinical/imaging assessments. This commentary discusses the role and governance of LUS during and beyond the COVID-19 pandemic, and how increased education and training in this discipline can be undertaken given the restrictions in imaging highly infectious patients. The use of simulation, although numerical methods or dedicated scan trainers, and machine learning algorithms could further improve the accuracy of LUS, whilst helping to reduce its learning curve for greater uptake in clinical practice.

摘要

COVID-19 可导致肺部损伤,进而引起进行性呼吸衰竭和潜在的死亡。放射摄影和 CT 是用于诊断和监测 COVID-19 患者的影像学工具。在某些地区,COVID-19 期间的肺部超声(LUS)正被用于辅助决策和改善患者的护理。然而,其更多的应用可能有助于改善疑似 COVID-19 或其他肺部疾病患者的现有实践。LUS 的局限性在于它需要具有足够能力的从业者来确保及时、安全和具有诊断性的临床/影像学评估。本评论讨论了 LUS 在 COVID-19 大流行期间和之后的作用和管理,以及如何在对高度传染性患者进行影像学检查受限的情况下,在该领域开展更多的教育和培训。尽管可以使用模拟、数值方法或专用的扫描训练器以及机器学习算法来进一步提高 LUS 的准确性,但同时也有助于降低其学习曲线,以促进其在临床实践中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8eb/8011243/df84fa51d3b5/bjr.20200755.g001.jpg

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