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Abstract #3484

An integrative deep learning model to distinguish between normal and atherosclerotic carotid arteries on black-blood vessel wall MRI

Jiayi Wu1,2, Jingmin Xin1, Jie Sun2, Zechen Zhou3, Baocheng Chu2, Dongxiang Xu2, and Chun Yuan2

1Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi'an, China, 2Department of Radiology, University of Washington, Seattle, WA, United States, 3Philips Research North America, Cambridge, Cambridge, MA, United States

Vessel wall (VW) MRI has been used to characterize atherosclerotic plaques but the review process is complex. To facilitate the translation of VWMRI into clinical application, we utilized deep convolutional neural networks (CNN) to distinguish between normal and atherosclerotic carotid arteries automatically in black-blood (BB) VWMRI. Trained with a dataset that contains both normal and diseased carotid arteries with expert labeling, an integrative deep CNN model was developed and yielded better automatic diagnosis accuracy of carotid atherosclerosis (85.18%) compared with other existing methods. This model may be used as an initial screening to separate normal from diseased arteries.

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