Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross‐sectio...
Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross‐sectional and longitudinal magnetic resonance imaging data
About this item
Full title
Author / Creator
Publisher
Hoboken, USA: John Wiley & Sons, Inc
Journal title
Language
English
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Publication information
Publisher
Hoboken, USA: John Wiley & Sons, Inc
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Scope and Contents
Contents
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnet...
Alternative Titles
Full title
Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross‐sectional and longitudinal magnetic resonance imaging data
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10028671
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10028671
Other Identifiers
ISSN
1065-9471
E-ISSN
1097-0193
DOI
10.1002/hbm.26205
