Deep-learning analysis of computed tomography coronary angiography data enables more accurate computation of the shear stress distribution than conventional analysis by experts: A head-to-head comparison with near-infrared spectroscopy-intravascular ultrasound-based modelling

Coronary computed tomography angiography (CTA) is the primary non-invasive test for detecting coronary artery disease (CAD) and constitutes an attractive alternative to intravascular imaging for the study of pathophysiological mechanisms that are involved in atherosclerotic evolution. Deep learning (DL) methodologies offers potential to overcome limitations in CTA analysis enabling reproducibly and fast and accurate segmentation of large datasets however their value in assessing coronary physiol…

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