基于双能量CT的深度学习放射学模型的开发和解释,用于预测 carotid动脉支架后新的脑性缺血病变:一个多中心研究

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Guihan Lin1,2, Weiyue Chen1,2, Weiming Hu1,3

  • 1Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.

European radiology
|February 4, 2026
PubMed
概括

一个新的深度学习放射学模型准确地预测了动脉支架后的新的ipsilateral缺血病变. 这种可解释的工具有助于识别高风险患者,以优化预防策略和改善结果.

关键词:
冠状动脉狭窄症 冠状动脉狭窄深度学习是一种深度学习.双能量CT是双能量CT.新的大脑缺血病变的发生.支架 支架是一种支架.

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