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A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives
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, an...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689408/ https://www.ncbi.nlm.nih.gov/pubmed/36359550 http://dx.doi.org/10.3390/diagnostics12112708 |
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author | Rana, Arti Dumka, Ankur Singh, Rajesh Panda, Manoj Kumar Priyadarshi, Neeraj |
author_facet | Rana, Arti Dumka, Ankur Singh, Rajesh Panda, Manoj Kumar Priyadarshi, Neeraj |
author_sort | Rana, Arti |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9689408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96894082022-11-25 A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives Rana, Arti Dumka, Ankur Singh, Rajesh Panda, Manoj Kumar Priyadarshi, Neeraj Diagnostics (Basel) Review 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. MDPI 2022-11-05 /pmc/articles/PMC9689408/ /pubmed/36359550 http://dx.doi.org/10.3390/diagnostics12112708 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Rana, Arti Dumka, Ankur Singh, Rajesh Panda, Manoj Kumar Priyadarshi, Neeraj A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives |
title | A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives |
title_full | A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives |
title_fullStr | A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives |
title_full_unstemmed | A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives |
title_short | A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives |
title_sort | computerized analysis with machine learning techniques for the diagnosis of parkinson’s disease: past studies and future perspectives |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689408/ https://www.ncbi.nlm.nih.gov/pubmed/36359550 http://dx.doi.org/10.3390/diagnostics12112708 |
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