Background: 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.
View Article and Find Full Text PDFBackground: Aortic valve (AV) calcification (AVC) is a strong predictor of aortic stenosis (AS) severity. The two-dimensional AVC (2D-AVC) ratio, a gain-independent ratio composed of the average pixel density of the AV and the aortic annulus, has previously shown strong correlations with two-dimensional (2D) echocardiographic hemodynamic parameters for severe AS and AVC by cardiac computed tomography. We hypothesize that the 2D-AVC ratio correlates with hemodynamic parameters in all severities of AS.
View Article and Find Full Text PDFIntroduction: Coronary arteries are exposed to a variety of complex biomechanical forces during a normal cardiac cycle. These forces have the potential to contribute to coronary stent failure. Recent advances in stent design allow for the transmission of native pulsatile biomechanical forces in the stented vessel.
View Article and Find Full Text PDFAortic valve calcium (AVC) is a strong predictor of aortic stenosis (AS) severity and is typically calculated by multidetector computed tomography (MDCT). We propose a novel method using pixel density quantification software to objectively quantify AVC by two-dimensional (2D) transthoracic echocardiography (TTE) and distinguish severe from non-severe AS. A total of 90 patients (mean age 76 ± 10 years, 75% male, mean AV gradient 32 ± 11 mmHg, peak AV velocity 3.
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