Comprehensive review of literature on Parkinson’s disease diagnosis

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2024-09-28 DOI:10.1016/j.compbiolchem.2024.108228

P. Pradeep, Kamalakannan J.

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引用次数: 0

Abstract

PD is one of the neurodegenerative illnesses affects 1–2 individuals per 1000 people over the age of 60 and has a 1 % prevalence rate. It affects both the non-motor and motor aspects of movement, including initiation, execution, and planning. Prior to behavioral and cognitive abnormalities like dementia, movement-related symptoms including stiffness, tremor, and initiation issues may be observed. Patients with PD have substantial reductions in social interactions, quality of life (QoL), and familial ties, as well as significant financial burdens on both the individual and societal levels. The healthcare industry is mostly using ML approaches with the modalities like image, signal, and data as well. Therefore, this survey aims to conduct a review of 50 articles on Parkinson disease diagnosis using different modalities. The survey includes (i) Classifying multimodal articles on Parkinson disease diagnosis (image, signal, data) using various machine learning, deep learning, and other approaches. (ii) Analyzing different datasets, simulation tools used in the existing papers. (iii)Examining certain performance measures, assessing the best performance, and chronological review of reviewed paper. Finally, the review determines the research gaps and obstacles in this research topic.

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帕金森病诊断文献综述

帕金森病是神经退行性疾病之一，每 1000 名 60 岁以上的老人中就有 1-2 人患病，发病率为 1%。它影响运动的非运动和运动方面，包括启动、执行和计划。在出现痴呆等行为和认知异常之前，可能会出现与运动相关的症状，包括僵硬、震颤和启动问题。帕金森氏症患者的社会交往、生活质量（QoL）和家庭关系都会大大降低，个人和社会也会承受巨大的经济负担。医疗保健行业在图像、信号和数据等模式上也大多采用了 ML 方法。因此，本调查旨在对使用不同模式诊断帕金森病的 50 篇文章进行综述。调查内容包括 (i) 使用各种机器学习、深度学习和其他方法对有关帕金森病诊断的多模态文章（图像、信号和数据）进行分类。(ii) 分析现有论文中使用的不同数据集和模拟工具。(iii)检查某些性能指标，评估最佳性能，并按时间顺序回顾已发表的论文。最后，综述确定了本研究课题的研究空白和障碍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.