AIMS: Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardi...
PURPOSE: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC...
The effect of chronic metabolic acidosis (MA) on cardiovascular disease (CVD) in the setting of chronic kidney disease (CKD) is largely unknown. Therefore, we aimed to study this relationship in nondialysis CKD patients.This cross-sectional, single-c...
IMPORTANCE: Increased ability to quantify anatomical phenotypes across multiple organs provides the opportunity to assess their cumulative ability to identify individuals at greatest susceptibility for adverse outcomes.
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