使用深度神经网络诊断通用关节超运动与步态模式的诊断
Guoqing Zhong1, Shuai Huang2, Ziyue Zhang3
1Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Computers in biology and medicine
|August 20, 2023
概括
综合性关节多动性 (GJH) 增加了受伤的风险. 一个深度学习模型从步行信号中准确地检测到GJH,有助于早期诊断和体育医学应用.
科学领域:
- 生物力学 生物力学
- 医学技术 医学技术 医学技术
- 运动医学运动医学
背景情况:
- 综合性关节高移动性 (GJH) 的特点是关节运动范围过大.
- GJH增加了膝盖受伤和骨关节炎的风险,影响体育表现.
- 步态分析对于评估部和膝盖功能至关重要,但传统方法与杂,可变的步态数据作斗争.
研究的目的:
- 开发和评估一个端到端的深度学习模型,用于识别与GJH相关的步态模式.
- 用传统的统计方法来解决分析噪音和可变步态数据的挑战.
主要方法:
- 开发了一个端到端的深度学习模型,包含卷积,残余和注意力网络块.
- 采用3D运动捕捉系统收集了452个步态样本的数据集,记录了各种行走条件下部,膝盖和脚关节的运动数据.
- 该模型经过训练,以识别标志着GJH的步态信号中的模式.
主要成果:
- 深度学习模型实现了高性能指标:95.77%的准确性,98.68%的特异性和76.84%的回忆.
- 与传统的机器学习方法相比,该模型显示出更高的效率.
- 开发的模型在计算上是高效的,适合在经济设备上部署.
结论:
- 拟议的深度学习模型提供了一种有效和高效的方法,用于精确的GJH诊断.
- 这项技术有可能在体育医学中立即临床使用和大规模GJH查.
- 精确的GJH检测可以帮助运动员和普通人预防和管理伤害.
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