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在MRI上基于深度学习的时间肌肉量化预测了急性缺血性中风的不良结果

Ruibin Huang¹, Jiawei Chen², Huanpeng Wang¹

¹Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China.

European journal of radiology

|July 27, 2025

概括

深度学习在急性缺血性中风 (AIS) 患者中准确量化时肌厚度 (TMT) 和面积 (TMA). 这些测量是强大的预后标记,有助于风险分层和个性化康复.

科学领域:

医学成像分析分析医学成像分析
医疗保健中的人工智能
神经学和中风研究的神经学和中风研究.

背景情况:

急性缺血性中风 (AIS) 给健康带来了重大挑战.
准确的预后标记对于患者管理和康复规划至关重要.
时间肌肉 (TM) 测量可能为AIS提供预后见解.

研究的目的:

开发一个深度学习 (DL) 管道,用于对AIS患者的肌 (TM) 参数进行自动分析.
评估TM厚度 (TMT) 和TM面积 (TMA) 的预后价值,以预测AIS后6个月的结果.
为了实现TMT和TMA的快速和可扩展的量化,用于临床整合.

主要方法:

开发了一个DL管道,使用ResNet50进行切片选择和TransUNet进行TM细分.
该管道在包括1020名AIS患者的数据集上进行了培训和验证.
使用准确度,±1切片准确度,平均绝对误差和子相似系数 (DSC) 来评估性能.
分析了DL量化的TMT与TMA之间的关联以及6个月后的结果.

主要成果:

该DL系统在切片选择 (72.91%) 和TM细分 (DSC为0.858) 中实现了高精度.
自动量化的TMT和TMA独立地与AIS患者的6个月不良结果相关.
危险比率表明,较高的TMT和TMA对结果具有保护作用 (TMT:0.736,TMA:0.702).

结论:

时间肌肉厚度 (TMT) 和面积 (TMA) 是AIS的显著预后标志物.
开发的端到端DL管道提供TMT和TMA的自动化,快速量化.
这项技术支持可扩展的风险分层和针对AIS患者的个性化康复规划.

关键词:

深度学习是一种深度学习.缺血性中风是因为缺血性中风.磁共振图像是一种磁共振图像.萨尔科佩尼亚是什么意思萨尔科佩尼亚时间肌肉的肌肉时间肌肉.

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