医学
心脏病学
心室流出道
内科学
狭窄
主动脉瓣
主动脉瓣狭窄
冲程容积
血流动力学
人口
放射科
血压
心率
环境卫生
作者
Hema Krishna,Kevin Desai,Brody Slostad,Siddharth Bhayani,Joshua H. Arnold,Wouter Ouwerkerk,Yoran Hummel,Carolyn S.P. Lam,Justin A. Ezekowitz,Matthew Frost,Zhubo Jiang,Cyril Equilbec,Aamir Twing,Patricia A. Pellikka,Leon Frazin,Mayank Kansal
标识
DOI:10.1016/j.echo.2023.03.008
摘要
Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment.
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