Federal University of São Carlos, Department of Computing, São Carlos, Brazil.
University of Western São Paulo, Brazil.
Artif Intell Med. 2019 Apr;95:48-63. doi: 10.1016/j.artmed.2018.08.007. Epub 2018 Sep 7.
In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end.
In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results.
The review showed the majority of works make use of signal-based data, which are often acquired by means of sensors. Also, we have observed the increasing number of works that employ virtual reality and e-health monitoring systems to increase the life quality of PD patients. Despite the different approaches found in the literature, almost all of them make use of some sort of machine learning mechanism to aid the automatic PD diagnosis.
The main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016.
在这项工作中，我们对最近的使能技术进行了系统回顾，作为一种工具，用于诊断、治疗和提高帕金森病（PD）患者的生活质量，并分析了这方面的未来趋势和新方法。
在本次综述中，我们汇编了在一些成熟数据库（如 Science Direct、IEEEXplore、PubMed、Plos One、Multidisciplinary Digital Publishing Institute [MDPI]、Association for Computing Machinery [ACM]、Springer 和 Hindawi Publishing Corporation）上发表的多篇文献。对每一篇选定的文献都进行了仔细分析，以确定其目标、方法和结果。
综述表明，大多数研究都利用基于信号的数据，这些数据通常是通过传感器获取的。此外，我们还观察到越来越多的文献采用虚拟现实和电子健康监测系统来提高 PD 患者的生活质量。尽管文献中存在不同的方法，但几乎所有方法都使用某种机器学习机制来辅助 PD 的自动诊断。
本调查的主要重点是考虑计算机辅助诊断，以及它们在处理 PD 识别问题时的有效性。此外，本次综述的主要贡献是仅考虑非常近期的文献，主要是 2015 年和 2016 年的文献。
