Parkinson’s Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

中文翻译：

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使用深度学习诊断帕金森病：文献计量分析和文献综述

帕金森病（PD）是一种由多巴胺分泌减少引发的进行性神经退行性疾病。深度学习（DL）在帕金森病诊断研究中得到了广泛关注，该学科发表的论文数量不断增加。与常见的机器学习方法相比，使用深度学习的局部放电检测呈现出更有前景的结果。本文旨在进行文献计量分析和文献综述，重点关注该领域发生的突出发展。为了实现研究目标，我们检索并分析了 Scopus 数据库中可用的研究论文。接下来，我们进行了文献计量分析，通过使用 VOSviewer 软件提供文献计量数据的可视化表示来检查调查研究中关键词、作者和国家的结构。该研究还对文献进行了深入回顾，重点关注不同的 PD 指标、部署方法和绩效指标。结果表明，随着时间的推移以及全球范围内的大量研究，使用深度学习方法进行帕金森病诊断的坚定发展。此外，文献综述还提出了与增量学习相关的深度学习方法的研究差距，特别是与大数据分析相关的研究差距。