多模式机器学习预测对肌肉的超声波引导肉和振动疗法的反应 临床和成像相关性研究
Hu Chen1, Jingyuan Lin2, Huini Lu1
1Department of Rehabilitation Medicine, Anshun City People's Hospital, Anshun, China.
Frontiers in bioengineering and biotechnology
|January 26, 2026
概括
毒素 (BTX) 结合振动疗法显著改善中风后的性,比单独使用BTX还要多. 机器学习模型可以使用临床和超声波数据预测个体治疗反应.
科学领域:
- 神经学 神经学
- 康复医学 康复医学 康复医学
- 生物医学工程 生物医学工程
背景情况:
- 肌肉是一种常见的中风并发症.
- 毒素 (BTX) 用于焦点,但反应不同.
- 对BTX响应的预测模型是有限的.
研究的目的:
- 评估超声波引导的BTX加振动疗法用于中风后性.
- 开发一个机器学习模型来预测个性化的治疗反应.
主要方法:
- 200名参与者接受了单独BTX或BTX加振动疗法.
- 收集了临床 (MAS,FMA,BI) 和超声波 (肌肉厚度,回声强度,血流) 数据.
- 机器学习模型使用组合数据预测了治疗反应.
主要成果:
- 结合疗法比单独使用BTX显示出更大的MAS减少和FMA/BI改善.
- 一个神经网络模型实现了高预测性能 (AUC = 0.87).
- 肌肉厚度和回声强度在模型中具有影响力,但与临床结果的相关性很弱.
结论:
- 超声波引导的BTX与振动疗法增强了中风后的性和功能结果.
- 多模式机器学习显示了个性化性管理的潜力.
- 超声波生物标志物可以帮助个性化治疗决策.
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