Machine learning-based identification of symptomatic carotid atherosclerotic plaques with dual-energy computed tomography angiography
This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events.
Source: Journal of Stroke and Cerebrovascular Diseases - Category: Neurology Authors: Ling-Jie Wang, Pei-Qing Zhai, Li-Li Xue, Cai-Yun Shi, Qian Zhang, Hua Zhang Source Type: research
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