Artificial Intelligence in Echocardiography for Anesthesiologists

被引:15
|
作者
Chen, Xia [1 ]
Owen, Cindy A. [2 ]
Huang, Emma C. [3 ]
Maggard, Brittany D. [4 ]
Latif, Rana K. [4 ,6 ]
Clifford, Sean P. [4 ]
Li, Jinbao [1 ]
Huang, Jiapeng [2 ,4 ,5 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Med, Shanghai Gen Hosp, Dept Anesthesiol, Shanghai, Peoples R China
[2] GE Healthcare, Waukesha, WI USA
[3] duPont Manual High Sch, Louisville, KY USA
[4] Univ Louisville, Dept Anesthesiol & Perioperat Med, 530 South Jackson St, Louisville, KY 40202 USA
[5] Univ Louisville, Dept Cardiovasc & Thorac Surg, Louisville, KY 40202 USA
[6] Outcomes Res Consortium, Cleveland, OH USA
关键词
echocardiography; artificial intelligence; machine learning; deep learning; anesthesiology; VENTRICULAR SYSTOLIC FUNCTION; EJECTION FRACTION; LONGITUDINAL STRAIN; VALVE; QUANTIFICATION; MACHINE;
D O I
10.1053/j.jvca.2020.08.048
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Echocardiography is a unique diagnostic tool for intraoperative monitoring and assessment of patients with cardiovascular diseases. However, there are high levels of interoperator variations in echocardiography interpretations that could lead to inaccurate diagnosis and incorrect treatment. Furthermore, anesthesiologists are faced with the additional challenge to interpret echocardiography and make decisions in a limited time-frame from these complex data. The need for an automated, less operator-dependent process that enhances speed and accuracy of echocardiography analysis is crucial for anesthesiologists. Artificial intelligence is playing an increasingly important role in the medical field and could help anesthesiologists analyze complex echocardiographic data while adding increased accuracy and consistency to interpretation. This review aims to summarize practical use of artificial intelligence in echocardiography and discusses potential limitations and challenges in the future for anesthesiologists. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码:251 / 261
页数:11
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