Rana Arti, Dumka Ankur, Singh Rajesh, Panda Manoj Kumar, Priyadarshi Neeraj
Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India.
Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India.
Diagnostics (Basel). 2022 Nov 5;12(11):2708. doi: 10.3390/diagnostics12112708.
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
根据世界卫生组织（WHO）的定义，帕金森病（PD）是一种脑部神经退行性疾病，除了会引发阿尔茨海默病（AD）、精神问题、失眠、焦虑和感觉异常等其他问题外，还会导致运动症状，包括运动迟缓、僵硬、震颤和平衡失调。为了解决这些问题并改进帕金森病的诊断程序，已经建立了包括人工智能（AI）、机器学习（ML）和深度学习（DL）在内的技术，用于对帕金森病患者和具有相似治疗表现的正常对照（NC）进行分类。在本文中，我们对截至2022年9月发表的研究文章进行了文献综述，以便对用于诊断帕金森病的数据集、各种模态、实验设置和架构的使用情况进行深入分析。该分析涵盖了总共217篇研究出版物，列出了各种数据集、方法和特征。这些研究结果表明，机器学习/深度学习方法和新型生物标志物在医学决策中的应用前景广阔，能够实现对帕金森病更系统、更全面的检测。最后，我们强调了面临的挑战，并就选择可能用于未来帕金森病研究中与结构磁共振成像（sMRI）、DaTSCAN和单光子发射计算机断层扫描（SPECT）数据进行亚组分析和关联分析的方法提供了适当建议。
