Validation of a fully automated deep learning-enabled solution for CCTA atherosclerotic plaque and stenosis quantification in a diverse real-world cohort

Coronary CT angiography (CCTA) has proven to be a reliable test for the evaluation of coronary artery stenosis severity and for quantification of the overall burden of coronary atherosclerosis providing incremental prognostic information. Recent advances in CT technology allow for semi-automated measurements of coronary atherosclerotic plaque characteristics with high accuracy as compared to intravascular imaging.1 However, semi-automated plaque quantification is time-consuming and requires a high level of human expertise.

Source: Journal of Cardiovascular Computed Tomography - March 28, 2024 Category: Radiology Authors: Daniel Lorenzatti, Annalisa Filtz, Pamela Pina, Andrea Scotti, Aldo L. Schenone, Carlos A. Gongora, Alan C. Kwan, Victor Y. Cheng, Mario J. Garcia, Daniel S. Berman, Piotr J. Slomka, Damini Dey, Leandro Slipczuk Tags: Correspondence Source Type: research