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人工智能与超声心动图

Artificial Intelligence and Echocardiography.

作者信息

Yoon Yeonyee E, Kim Sekeun, Chang Hyuk Jae

机构信息

Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea.

Department of Internal Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

出版信息

J Cardiovasc Imaging. 2021 Jul;29(3):193-204. doi: 10.4250/jcvi.2021.0039. Epub 2021 May 3.

Abstract

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.

摘要

人工智能(AI)正在诊断医学成像领域不断发展,包括超声心动图。尽管超声心动图的动态特性带来了超越X射线、计算机断层扫描、磁共振成像和放射性核素成像等静态图像的挑战,但人工智能已经影响了超声心动图从图像采集到自动测量和解读的各个环节。鉴于超声心动图常常受到观察者间差异的影响,并且对经验水平有很强的依赖性,人工智能在最大限度地减少观察者差异和提供可重复的测量方面可能极具优势,从而实现准确诊断。目前,大多数报道的人工智能在超声心动图测量中的应用都集中在改进图像采集以及重复和繁琐任务的自动化上;然而,人工智能应用的作用不应局限于传统流程。相反,人工智能可以从细微和非特异性数据中提供具有临床重要性的见解,例如心肌病患者心肌纹理的变化。最近开发大型超声心动图数据库的举措可以促进人工智能应用的发展。将人工智能应用于超声心动图的最终目标是实现超声心动图分析全过程的自动化。一旦自动分析变得可靠,临床超声心动图的工作流程将发生根本性变化。人类专家仍将是控制整个诊断过程的主导者,不会被人工智能取代,并将从人工智能系统获得重要支持,以指导采集、进行测量以及根据需要整合和比较数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b86f/8318807/4b88c179af4e/jcvi-29-193-g001.jpg

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