Circulation. Cardiovascular imaging
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Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. ⋯ Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.
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Circ Cardiovasc Imaging · Oct 2017
Coronary Artery Calcium Distribution Is an Independent Predictor of Incident Major Coronary Heart Disease Events: Results From the Framingham Heart Study.
The presence and extent of coronary artery calcium (CAC) are associated with increased risk for cardiovascular events. We determined whether information on the distribution of CAC and coronary dominance as detected by cardiac computed tomography were incremental to traditional Agatston score (AS) in predicting incident major coronary heart disease (CHD). ⋯ Distribution of coronary atherosclerosis, especially CAC in the proximal dominant coronary artery and an increased number of coronary arteries with CAC, predict major CHD events independently of the traditional AS in community-dwelling men and women.