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Artificial Intelligence and Echocardiography

机译:人工智能和超声心动图

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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.Copyright ? 2021 Korean Society of Echocardiography.
机译:人工智能(AI)正在进行诊断医学成像领域,包括超声心动图。尽管超声心动图的动态性质存在于来自X射线,计算机断层扫描,磁共振和放射性同位素成像的静态图像之外的挑战,但AI影响了超声心动图的所有步骤,从图像采集到自动测量和解释。考虑到超声心动图常常受到观察者间变异性的影响并且表现出强烈依赖于经验水平,AI在最大限度地减少观察者变异并提供可再现的措施,可以实现精确诊断。目前,大多数报告的超声心动图测量中的AI应用都集中在改进的图像采集和自动化方面的重复和繁琐的任务;但是,AI应用的作用不应限于常规过程。相反,AI可以从微妙和非特异性数据提供临床重要的见解,例如心肌疾病患者心肌纹理的变化。开发大型超声心动图数据库的最近举措可以促进AI应用的发展。将AI应用于超声心动图的最终目标是超声心动图分析的整个过程的自动化。一旦自动分析变得可靠,临床超声心动图中的工作流程将自然地改变。人类专家将留在控制整体诊断过程的主人,不会被AI替换,并将获得AI系统的重要支持,以指导获取,执行测量和集成和集成和比较请求的数据。 2021年韩国超声心动图。

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