Zhang Zisang, Zhu Ye, Liu Manwei, Zhang Ziming, Zhao Yang, Yang Xin, Xie Mingxing, Zhang Li
Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China.
J Clin Med. 2022 May 20;11(10):2893. doi: 10.3390/jcm11102893.
The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer's experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.
准确评估左心室收缩功能对心血管疾病的诊断和治疗至关重要。左心室射血分数（LVEF）和整体纵向应变（GLS）是心脏收缩功能的最关键指标。超声心动图已成为测量LVEF和GLS的心脏成像的主要手段，因为它是非侵入性的、无辐射的，并且允许床边操作和实时处理。然而，心脏功能的人工评估取决于超声检查人员的经验，并且尽管他们经过多年培训，但观察者间仍存在变异性。此外，GLS需要后处理，这既耗时又在不同设备间存在变异性。研究人员已转向人工智能（AI）来应对这些挑战。AI强大的学习能力能够进行特征提取，这有助于实现心脏结构的准确识别以及心室容积和心肌运动的可靠估计。因此，基于超声心动图图像可以实现收缩功能指标的自动输出。本综述试图全面解释AI在通过超声心动图评估左心室收缩功能和心脏病鉴别诊断方面的最新进展，并讨论这一新领域的挑战和前景。
