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- W4220883318 startingPage "420" @default.
- W4220883318 abstract "At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of artificial intelligence applied to image analysis of coronary atherosclerotic plaques, covering multiple areas from plaque component analysis (e.g., identification of plaque properties, identification of vulnerable plaque, detection of myocardial function, and risk prediction) to risk prediction. Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas. The continuous development of computer science and technology may further promote the development of this field." @default.
- W4220883318 created "2022-04-03" @default.
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- W4220883318 date "2022-03-08" @default.
- W4220883318 modified "2023-09-26" @default.
- W4220883318 title "Artificial Intelligence in Cardiovascular Atherosclerosis Imaging" @default.
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- W4220883318 cites W1981386994 @default.
- W4220883318 cites W1985397601 @default.
- W4220883318 cites W2028534764 @default.
- W4220883318 cites W2033896075 @default.
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- W4220883318 cites W2990936018 @default.
- W4220883318 cites W2992005476 @default.
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- W4220883318 doi "https://doi.org/10.3390/jpm12030420" @default.
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- W4220883318 hasPublicationYear "2022" @default.
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