Abstract：
BACKGROUND: The diagnosis of Parkinson\'s disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson\'s disease diagnosis.
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.
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.
摘要：
背景：帕金森病的诊断目前基于临床评估。尽管有临床特点，不幸的是,错误率仍然很大。临床评估的低体内诊断准确性主要依赖于缺乏用于客观运动性能评估的定量生物标志物。非侵入性技术，例如可穿戴传感器，再加上机器学习算法，定量和客观地评估电机性能，与可能的好处无论是在诊所和在家里设置。我们对嵌入智能设备的机器学习算法在帕金森病诊断中的文献进行了系统回顾。
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。
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。
