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Updated: Jan 23, 2026

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
09:36

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

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可解释的机器学习模型来分类罪祸首 - - 化 Carotid 斑块在不确定的来源的栓塞性中风

Yu Sakai1, Jiehyun Kim2, Huy Q Phi3

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
|January 22, 2026
PubMed
概括

机器学习模型可以比传统方法更好地识别未确定源 (ESUS) 栓塞性中风中的罪祸首 - 带斑块. 可解释的人工智能 (SHAP) 揭示了改善中风风险评估的关键斑块特征.

关键词:
动脉样硬化 动脉样硬化石灰化是指化.计算机断层扫描 (CT) 是一种计算机断层扫描.机器学习是机器学习.标志性斑块 标志性斑块 标志性斑块一次性中风中风中风中风中风

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