Akkus Zeynettin, Aly Yousof H, Attia Itzhak Z, Lopez-Jimenez Francisco, Arruda-Olson Adelaide M, Pellikka Patricia A, Pislaru Sorin V, Kane Garvan C, Friedman Paul A, Oh Jae K
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.
J Clin Med. 2021 Mar 30;10(7):1391. doi: 10.3390/jcm10071391.
Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.
超声心动图（Echo）是一种广泛可用、无创且便于携带的床边成像工具，是临床实践中评估心脏解剖结构和功能时最常用的成像方式。另一方面，其对操作者的依赖性导致图像采集、测量和解读存在变异性。为减少这些变异性，对具备人工智能（AI）的独立于操作者和解读人员的超声心动图系统的需求日益增加，人工智能已被应用于临床医学的各个领域。计算机视觉中人工智能应用的最新进展使我们能够利用人工智能模型的自学习能力和高效的并行计算能力识别概念性和复杂的成像特征。这带来了诸多巨大机遇，例如提供对变化具有鲁棒性且具有通用性的人工智能模型以进行即时图像质量控制，辅助获取最佳图像和诊断复杂疾病，以及改善心脏超声的临床工作流程。在本综述中，我们对人工智能赋能的超声心动图在心脏病学中的应用现状以及人工智能驱动的超声心动图技术的未来趋势进行了概述，这些技术可实现测量标准化、协助医生诊断心脏病、优化临床超声心动图工作流程，并最终降低医疗成本。
