Dibbern Kevin N, Krzak Maddalena G, Olivas Alejandro, Albert Mark V, Krzak Joseph J, Kruger Karen M
Department of Pediatrics, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53223, USA.
Bioengineering (Basel). 2025 May 30;12(6):591. doi: 10.3390/bioengineering12060591.
The recent proliferation of novel machine learning techniques in quantitative marker-based 3D gait analysis (3DGA) has shown promise for improving interpretations of clinical gait analysis. The objective of this study was to characterize the state of the literature on using machine learning in the analysis of marker-based 3D gait analysis to provide clinical insights that may be used to improve clinical analysis and care.
A scoping review of the literature was conducted using the PubMed and Web of Science databases. Search terms from eight relevant articles were identified by the authors and added to by experts in clinical gait analysis and machine learning. Inclusion was decided by the adjudication of three reviewers.
The review identified 4324 articles matching the search terms. Adjudication identified 105 relevant papers. The most commonly applied techniques were the following: support vector machines, neural networks (NNs), and logistic regression. The most common clinical conditions evaluated were cerebral palsy, Parkinson's disease, and post-stroke.
ML has been used broadly in the literature and recent advances in deep learning have been more successful in larger datasets while traditional techniques are robust in small datasets and can outperform NNs in accuracy and explainability. XAI techniques can improve model interpretability but have not been broadly used.
最近，基于定量标记的三维步态分析（3DGA）中新型机器学习技术的大量涌现，已显示出有望改善临床步态分析的解读。本研究的目的是描述在基于标记的三维步态分析中使用机器学习的文献现状，以提供可用于改善临床分析和护理的临床见解。
使用PubMed和Web of Science数据库对文献进行范围综述。作者从八篇相关文章中确定了检索词，并由临床步态分析和机器学习专家进行补充。纳入由三位评审员裁定。
该综述确定了4324篇与检索词匹配的文章。裁定确定了105篇相关论文。最常用的技术如下：支持向量机、神经网络（NNs）和逻辑回归。评估的最常见临床病症是脑瘫、帕金森病和中风后。
机器学习在文献中已被广泛使用，深度学习的最新进展在更大的数据集中更成功，而传统技术在小数据集中很稳健，并且在准确性和可解释性方面可以优于神经网络。可解释人工智能（XAI）技术可以提高模型的可解释性，但尚未得到广泛应用。
