膝关节骨关节炎查使用通过格拉米安角场转换的多式步态信号,并通过深度学习模型集成
Kai Sun1, Zhenfu Huang1, Minghui Hang2
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, People's Republic of China.
Medical engineering & physics
|February 5, 2026
概括
这项研究提出了一种新的,具有成本效益和无辐射的步态分析方法,用于查膝关节骨关节炎 (KOA). 使用可穿戴传感器和人工智能的多模式方法显著提高了检测准确性和稳定性.
科学领域:
- 生物医学工程 生物医学工程
- 整形外科 整形外科 整形外科
- 医疗保健中的人工智能
背景情况:
- 目前的膝关节骨关节炎 (KOA) 查方法面临诸多挑战,包括高成本,复杂的程序和有限的动态功能数据.
- 像X射线这样的成像技术是昂贵的,并且涉及辐射暴露.
- 需要可访问,准确和非侵入性的方法来早期检测KOA.
研究的目的:
- 开发和验证一种多式联动步态分析方法,以实现高效准确的KOA查.
- 通过结合动态功能步态信息来克服传统选方法的局限性.
- 为了引入一个成本效益和无辐射的替代KOA检测.
主要方法:
- 使用可穿戴惯性测量单元 (IMU) 来收集时间序列步态数据.
- 将步态数据转换为格拉米安角场 (GAF) 图像,用于特征提取.
- 开发了一种双通道深度学习架构,集成时间卷积网络 (TCN) 和深度可分离的卷积神经网络 (CNN).
- 采用多头注意力 (MHA) 机制来实现多式联运特征融合.
主要成果:
- 拟议的模型实现了高性能指标:97.87%的准确性,98.23%的精度,98.17%的回忆和98.19%的F1分数.
- 与已建立的时间序列模型和单模方法相比,在十倍交叉验证中表现出卓越的性能.
- 整合GAF图像的多式联络框架显著提高了选的灵敏度和稳定性.
结论:
- 使用GAF图像和深度学习的多式行走分析方法对KOA查非常有效.
- 这种方法为早期KOA检测提供了一个有希望的,准确的,具有成本效益的,无辐射的解决方案.
- 集成动态步态数据为骨关节炎评估提供了有价值的功能性见解.
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