METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining \"Parkinson\'s disease\" AND (\"healthy\" or \"control\") AND \"diagnosis\", within the Groups and Outcome domains. Additional search terms included \"Algorithm\", \"Technology\" and \"Performance\".
RESULTS: From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors\' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours.
CONCLUSIONS: Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson\'s disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.
方法:遵循系统评价和荟萃分析指南的首选报告项目,我们搜索了PubMed12月之间发表的文章,2007年7月,2023年,使用搜索字符串组合“帕金森氏病”和(“健康”或“控制”)和“诊断”,在组和结果域中。其他搜索词包括“算法”,“技术”和“性能”。
结果:从89项确定的研究中,根据搜索字符串,47项符合纳入标准,根据作者的专业知识纳入了另外4项研究。步态成为机器学习模型分析的最常见参数,支持向量机是流行的算法。结果表明,使用随机森林等复杂算法,具有很好的准确性,支持向量机,和K-最近的邻居。
结论:尽管机器学习算法显示了前景,现实世界的应用程序可能仍然面临限制。这篇综述表明,将机器学习与可穿戴传感器集成有可能改善帕金森病的诊断。这些工具可以为临床医生提供客观数据,可能有助于早期检测。