Gandhi Sumeet, Mosleh Wassim, Shen Joshua, Chow Chi-Ming
Hamilton Health Sciences Centre, McMaster University, Hamilton, Ontario, Canada.
St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
Echocardiography. 2018 Sep;35(9):1402-1418. doi: 10.1111/echo.14086. Epub 2018 Jul 5.
Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Automation with longitudinal strain and 3D echocardiography has shown great accuracy and reproducibility allowing the incorporation of these techniques into daily workflow. This will give further experience to nonexpert readers and allow the integration of these essential tools into more echocardiography laboratories. The potential for machine learning in cardiovascular imaging is still being discovered as algorithms are being created, with training on large data sets beyond what traditional statistical reasoning can handle. Deep learning when applied to large image repositories will recognize complex relationships and patterns integrating all properties of the image, which will unlock further connections about the natural history and prognosis of cardiac disease states. The purpose of this review article was to describe the role and current use of automation, machine learning, and AI in echocardiography and discuss potential limitations and challenges of in the future.
自动化、机器学习和人工智能正在改变超声心动图领域，为医生提供辅助工具以改善患者护理。多个供应商的软件程序已融入自动化技术，以提高手动描记的准确性和效率。纵向应变和三维超声心动图的自动化技术已显示出很高的准确性和可重复性，使得这些技术能够纳入日常工作流程。这将为非专业读者提供更多经验，并使这些重要工具能够整合到更多的超声心动图实验室中。随着算法的创建，心血管成像中机器学习的潜力仍在不断被发掘，其训练基于传统统计推理无法处理的大数据集。深度学习应用于大型图像库时，将识别复杂的关系和模式，整合图像的所有属性，这将揭示有关心脏疾病自然史和预后的更多联系。这篇综述文章的目的是描述自动化、机器学习和人工智能在超声心动图中的作用和当前应用，并讨论未来可能存在的局限性和挑战。
