使用无标记运动捕捉和长期短期记忆的膝关节骨关节炎严重程度的分类完全卷积网络
Engin Kaya1, Hülya Şirzai2, Güneş Yavuzer2
1Acibadem Mehmet Ali Aydinlar University, Institute of Natural Sciences, Department of Biomedical Engineering, Istanbul, Turkey.
Computers in biology and medicine
|June 29, 2025
概括
使用无标记运动捕捉的深度学习模型可以从步态来分类膝关节关节炎的严重程度. 虽然对各种数据准确,但对新患者的概括仍然是这种自动化评估的挑战.
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:
- 膝关节骨关节炎 (OA) 诊断和严重程度分级传统上依赖于临床评估和成像,这可能是主观的和资源密集的.
- 步态分析提供了一种非侵入性方法来评估膝关节OA的功能障碍,反映了与疾病相关的生物机械变化.
研究的目的:
- 研究将无标记运动捕捉与深度学习相结合的有效性,以根据步态动力学进行自动化膝关节OA严重程度分类.
- 用随机与基于主题的数据分割策略来比较长期短期记忆完全卷积网络 (LSTM-FCN) 模型的性能.
主要方法:
- 利用无标记运动捕捉来收集不同严重程度的膝盖骨关节炎患者的步态数据.
- 采用LSTM-FCN深度学习模型来分析步态模式,并根据Kellgren-Lawrence等级对严重程度进行分类.
- 使用两种数据分割策略评估模型通用性:随机分割和基于主题的分割.
主要成果:
- 通过随机数据分割,LSTM-FCN模型实现了高精度 (0.91).
- 根据学科进行分类,分类性能显著下降 (准确率为0.76),突出显示了学科间概括性方面的挑战.
- 该模型有效地区分了严重的OA和健康的步态模式,但由于重叠的步态特征,早期和中度OA的错误分类率更高.
结论:
- 对步态动力学的深度学习分析为自动膝关节OA严重程度分类提供了一个有希望,可扩展和可访问的替代方案.
- 步行模式的跨学科变异性对模型通用性构成重大挑战,需要进一步研究先进的特征提取和多式联络数据集成.
- 未来的研究应该探索纵向数据,以评估这些模型对疾病进展和治疗疗效的预测潜力.
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