Xu Datao, Zhou Huiyu, Quan Wenjing, Ugbolue Ukadike Chris, Gusztav Fekete, Gu Yaodong
Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China.
Faculty of Sports Science, Ningbo University, Ningbo, China.
Heliyon. 2024 Feb 9;10(4):e26052. doi: 10.1016/j.heliyon.2024.e26052. eCollection 2024 Feb 29.
As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.
作为众多基本运动技术之一，着陆动作也经常用于临床损伤筛查和诊断。然而，在不同的约束条件下着陆模式是不同的，这将给临床专家的临床诊断带来很大困难。机器学习（ML）在解决各种临床诊断任务方面非常成功，但它们都有一个缺点，即属于黑箱模型，很少提供并解释做出特定决策的原因等有用信息。当前的工作验证了应用由逐层相关性传播（LRP）构建的可解释机器学习（XML）模型用于临床生物力学中着陆模式识别的可行性。本研究收集了560组着陆数据。通过将这些着陆数据作为输入信号纳入XML模型，基于从LRP得出的相关性得分（RS）来解释预测结果。基于统计参数映射（SPM）和效应量从统计角度全面评估从XML获得的解释。RS在不同类别着陆模式的解释中具有出色的统计特征，并且也符合着陆模式识别的临床特征。当前的工作突出了XML方法的适用性，该方法不仅可以满足传统的类别间决策问题，而且在很大程度上解决了着陆模式识别中缺乏透明度的问题。我们提供了一个在着陆分析中实现ML决策结果可解释性的可行框架，为未来的临床诊断和生物力学分析提供了方法参考和坚实基础。
