可解释的人工智能用于步态分析:进步,陷和挑战 - - 一个系统的审查
Liangliang Xiang1, Zixiang Gao2, Peimin Yu3,4
1KTH MoveAbility, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.
Frontiers in bioengineering and biotechnology
|November 17, 2025
概括
可解释的人工智能 (XAI) 增强了用于步态分析的机器学习 (ML),改善了临床决策. 这次审查发现,XAI方法可以识别关键的步态特征,但需要标准化才能更广泛地使用.
科学领域:
- 生物力学 生物力学
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 机器学习 (ML) 模型在步态数据分析方面表现出色,但往往缺乏透明度.
- 可解释的人工智能 (XAI) 提供了一个解决方案,以提高临床环境的ML模型可解释性.
研究的目的:
- 系统地审查XAI在步态分析中的应用.
- 在不同患者群体中评估XAI方法,性能和临床实用性.
主要方法:
- 通过全面的数据库搜索确定了31项研究的系统审查 (PROSPERO注册:CRD42024622752).
- 将XAI方法分类为模型不可知,模型特定和混合方法.
- 解释技术的分析,包括SHAP,LIME,Grad-CAM和注意力机制.
主要成果:
- XAI成功地确定了与生物机械相关的步态特征 (例如步长,关节角度),使病态步态区分开来.
- 最常被应用的是模型不可知论方法 (SHAP,LIME).
- 研究涵盖了各种临床群体,包括帕金森病,中风和肉症.
结论:
- 在步态分析中,XAI显示了弥合ML预测性能和临床解释性之间的差距的潜力.
- 标准化,验证和平衡准确性与透明度对于广泛的临床采用至关重要.
- 需要进一步的研究来完善XAI框架并评估它们在不同步态障碍中的现实应用性.
相关概念视频
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