Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.
Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy.
J Neurol. 2024 Oct;271(10):6452-6470. doi: 10.1007/s00415-024-12611-x. Epub 2024 Aug 14.
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.
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".
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.
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.
目前帕金森病的诊断基于临床评估。尽管有临床特征，但遗憾的是，误诊率仍然很高。临床评估的低诊断准确性主要归因于缺乏用于客观运动表现评估的定量生物标志物。非侵入性技术，如可穿戴传感器，结合机器学习算法，可以对运动表现进行定量和客观评估，无论是在诊所还是在家中都可能具有优势。我们对嵌入智能设备中的机器学习算法在帕金森病诊断中的应用进行了系统评价。
根据系统评价和荟萃分析报告的首选条目，我们在 PubMed 中搜索了 2007 年 12 月至 2023 年 7 月发表的文章，使用了一个组合了“帕金森病”和“健康”或“对照”以及“诊断”的搜索字符串，在“群组”和“结果”领域内进行搜索。其他搜索词包括“算法”、“技术”和“性能”。
从 89 项已确定的研究中，有 47 项基于搜索字符串符合纳入标准，另外 4 项研究是基于作者的专业知识纳入的。机器学习模型分析的最常见参数是步态，支持向量机是最常见的算法。结果表明，复杂算法（如随机森林、支持向量机和 K 最近邻）具有有前景的准确性。
尽管机器学习算法显示出了希望，但实际应用可能仍面临限制。本综述表明，将机器学习与可穿戴传感器相结合具有改善帕金森病诊断的潜力。这些工具可以为临床医生提供客观数据，可能有助于早期发现。
