A machine learning-based classification approach on Parkinson's disease diffusion tensor imaging datasets.
DTI
Machine learning
Neuroimaging
Parkinson’s disease
Substantia nigra
Journal
Neurological research and practice
ISSN: 2524-3489
Titre abrégé: Neurol Res Pract
Pays: England
ID NLM: 101767802
Informations de publication
Date de publication:
2020
2020
Historique:
received:
20
07
2020
accepted:
26
10
2020
entrez:
16
12
2020
pubmed:
17
12
2020
medline:
17
12
2020
Statut:
epublish
Résumé
The presence of motor signs and symptoms in Parkinson's disease (PD) is the result of a long-lasting prodromal phase with an advancing neurodegenerative process. The identification of PD patients in an early phase is, however, crucial for developing disease-modifying drugs. The objective of our study is to investigate whether Diffusion Tensor Imaging (DTI) of the Substantia nigra (SN) analyzed by machine learning algorithms (ML) can be used to identify PD patients. Our study proposes the use of computer-aided algorithms and a highly reproducible approach (in contrast to manually SN segmentation) to increase the reliability and accuracy of DTI metrics used for classification. The results of our study do not confirm the feasibility of the DTI approach, neither on a whole-brain level, ROI-labelled analyses, nor when focusing on the SN only. Our study did not provide any evidence to support the hypothesis that DTI-based analysis, in particular of the SN, could be used to identify PD patients correctly.
Identifiants
pubmed: 33324945
doi: 10.1186/s42466-020-00092-y
pii: 92
pmc: PMC7654034
doi:
Types de publication
Journal Article
Langues
eng
Pagination
46Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsThe authors have no competing or conflicting interest to report.
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