在非对比计算机断层扫描上对缺血核心的自动评估:使用CT输液进行多中心比较分析
Puja Shahrouki1, Shingo Kihira1, Elham Tavakkol1
1Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Journal of neurointerventional surgery
|November 2, 2023
概括
机器学习算法可以使用非对比CT (NCCT) 扫描精确估计缺血核心体积. 这种方法可与CT输液 (CTP) 进行急性缺血性中风评估.
科学领域:
- 神经学 神经学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 算法在估计非对比CT (NCCT) 的缺血核心体积方面表现有前途.
- 准确估计缺血核心体积对于急性缺血中风管理至关重要.
研究的目的:
- 评估e-Stroke Suite软件在评估NCCT上的缺血核心体积方面的性能.
- 为了比较NCCT衍生的缺血核心体积与CT输液 (CTP) 的体积.
主要方法:
- 对111名患有大血管封闭症的患者进行了回顾性多中心研究.
- 在NCCT (e-Stroke Suite) 和CTP (Olea软件) 上使用ML进行自动化缺血核心体积计算.
- 将NCCT和CTP体积与MRI计算的最终心脏病发作体积 (FIV) 的比较.
主要成果:
- 在NCCT (20.4±19.0毫升) 和CTP (19.9±18.6毫升) (P=0.82) 之间,缺血核心体积相似.
- 在NCCT和CTP估计的核心体积之间观察到中等相关性 (r=0.40,P<0.001).
- 在FIV和NCCT (r=0.30,P=0.001) 和CTP (r=0.36,P<0.001) 的估计核心体积之间发现了类似的相关性.
结论:
- 在NCCT上基于ML的缺血核心体积估计与CTP相似.
- 在急性缺血性中风中,NCCT为缺血核心体积评估提供了可行的替代方案.
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