使用深度学习在MRI上对半球CSF进行自动细分:在大半球心脏病发作后量化大脑胀
Junzhao Cui1, Jingyi Yang2, Ye Wang1
1Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Heliyon
|March 11, 2024
概括
深度学习准确量化大脑脊髓液 (CSF) 在大半球心脏病发作 (LHI) 患者. 低的脑液比率预示着不良结果,有助于评估中风后脑的严重程度.
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:
- 大脑 (CED) 是急性缺血性中风 (AIS) 的严重并发症,特别是在大半球心脏病发作 (LHI) 中.
- 准确评估CED对于预测患者的结果至关重要.
研究的目的:
- 实施一种深度学习方法,从T2加权成像 (T2WI) 中提取脑脊液 (CSF).
- 评估LHI患者中量化的CSF体积和临床结果之间的关系.
主要方法:
- 深度学习算法用于对93名LHI患者的T2WI中CSF进行细分.
- 半球CSF比率计算以评估大脑胀和预后.
- 进行了接收器运行特征和多变量逻辑回归分析.
主要成果:
- 通过自动化脑液提取,实现了0.830.0的平均子相似系数.
- 半球CSF比率准确地确定了严重的大脑 (AUC=0.867).
- 低半球CSF比率,以前的中风和ASPECT得分≤6预测了不利的结果.
结论:
- 自动化的脑液体积评估提供了脑瘤的客观生物标志物.
- 量化的CSF有效预测LHI患者的结果.
- 该方法有助于量化CED并确认其预后价值.
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