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Review
. 2022 Nov 5;12(11):2708.
doi: 10.3390/diagnostics12112708.

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives

Arti Rana  1 , Ankur Dumka  2   3 , Rajesh Singh  4   5 , Manoj Kumar Panda  6 , Neeraj Priyadarshi  7
Affiliations
Review

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives

Arti Rana et al. Diagnostics (Basel). .

Abstract

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.

Keywords: MRI; Parkinson’s disease; artificial neural network; deep learning; diagnosis; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Structure of Proposed Work.
Figure 2
Figure 2
PRISMA criteria.
Figure 3
Figure 3
Distribution of journals from Web of Science from Core Collection.
Figure 4
Figure 4
Distribution of articles based on publishers.
Figure 5
Figure 5
Role of artificial intelligence in healthcare.
Figure 6
Figure 6
Categories of machine learning algorithm.
Figure 7
Figure 7
Representation of deep learning model using CNN and ANN.
Figure 8
Figure 8
Categorization of Parkinson’s disease.
See this image and copyright information in PMC

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