Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Rana, Arti, Dumka, Ankur, Singh, Rajesh, Panda, Manoj Kumar, Priyadarshi, Neeraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784836526730903552
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
work_keys_str_mv AT ranaarti acomputerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT dumkaankur acomputerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT singhrajesh acomputerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT pandamanojkumar acomputerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT priyadarshineeraj acomputerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT ranaarti computerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT dumkaankur computerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT singhrajesh computerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT pandamanojkumar computerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives
AT priyadarshineeraj computerizedanalysiswithmachinelearningtechniquesforthediagnosisofparkinsonsdiseasepaststudiesandfutureperspectives