Machine learning techniques designed to recognize views and perform measurements are increasingly used to address the need for automation of the interpretation of echocardiographic images. The current study was designed to determine whether a recently developed and validated deep learning (DL) algorithm for automated measurements of echocardiographic parameters of left heart chamber size and function can improve the reproducibility and shorten the analysis time, compared to the conventional methodology. The DL algorithm trained to identify standard views and provide automated measurements of 20 standard parameters, was applied to images obtained in 12 randomly selected echocardiographic studies.
View Article and Find Full Text PDFAims: Aortic valve area (AVA) used for echocardiographic assessment of aortic stenosis (AS) has been traditionally interpreted independently of sex, age and race. As differences in normal values might impact clinical decision-making, we aimed to establish sex-, age- and race-specific normative values for AVA and Doppler parameters using data from the World Alliance Societies of Echocardiography (WASE) Study.
Methods And Results: Two-dimensional transthoracic echocardiographic studies were obtained from 1903 healthy adult subjects (48% women).
Annuloplasty is the cornerstone of surgical mitral valve repair. A percutaneous transvenous catheter-based approach for mitral valve repair was tested by placing a novel annuloplasty device in the coronary sinus of sheep with acute ischemic mitral regurgitation. Mitral regurgitation was reduced from 3-4+ to 0-1+ in all animals (P < 0.
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