阿尔伯塔中风计划早期CT分数 (ASPECTS) 和心脏病核心在急性缺血性中风中扩散权重成像的自动定量:多中心验证和严重程度分层
Lai Wei1, Ting Xiao2, Hao Wang2
1Department of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China.
Brain communications
|March 11, 2026
概括
一个新的深度学习框架准确量化了扩散加权成像-阿尔伯塔中风计划早期CT得分 (DWI-ASPECTS) 和急性缺血性中风中的心脏核心体积. 这种工具有助于医生做出紧急的临床决策,并确定中风的严重程度.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 急性缺血性中风,特别是中脑动脉急性缺血性中风 (MCA-AIS),需要快速准确的评估才能进行有效的治疗.
- 扩散加权成像 (DWI) 对于评估中风至关重要,其中DWI-ASPECTS和心脏病核心体积是关键预后指标.
- 目前对DWI-ASPECTS和心脏病核心体积的手动量化可能会耗时,并且会受到评价者之间的变化.
研究的目的:
- 开发和验证一个深度学习 (DL) 框架,用于在MCA-AIS中同时自动量化DWI-ASPECTS和心脏病核心体积.
- 评估该DL框架在多中心环境中对中风严重程度分层的临床实用性.
主要方法:
- 在738名MCA-AIS患者的多中心队列上训练了一种3D U-Net架构,用于同时进行心脏病细分和ASPECTS区域分析.
- 模型的性能与专家神经放射学家进行了验证,使用了类内相关系数和斯皮尔曼相关系数.
- 该研究调查了ASPECTS,核心体积和MCA-AIS严重程度之间的相关性.
主要成果:
- 在外部测试组中,DL模型实现了与手动细分 (迪斯系数0.801) 和体积测量 (斯皮尔曼相关性0.988) 的高相关性.
- 自动化DWI-ASPECTS和心脏病核心体积显示出强烈的相关性和与评级人员手动评估的良好一致性.
- DL模型的表现与神经放射学家的表现相当,特定的皮质区域 (M1-M6) 对于中度至重度中风分类有意义.
结论:
- 开发的DL框架提供了MCA-AIS中DWI-ASPECTS和心脏病核心体积的准确和自动定量.
- 这种自动化工具可以作为医生在紧急临床决策和中风严重程度评估中的辅助工具.
- 该研究强调了ASPECTS区域,核心体积和MCA-AIS严重程度之间的显著关联,有助于识别中度至重度病例.
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