Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

Abstract Background Amyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does...

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Détails bibliographiques
Auteurs principaux: Ashwin V. Venkataraman, Wenjia Bai, Alex Whittington, James F. Myers, Eugenii A. Rabiner, Anne Lingford-Hughes, Paul M. Matthews, for the Alzheimer’s Disease Neuroimaging Initiative
Format: article
Langue:EN
Publié: BMC 2021
Sujets:
Alzheimer’s
Amyloid clusters
Amyloid PET
Machine learning
Clustering
Automated decision
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
Accès en ligne:https://doaj.org/article/232b9cbd125145b38e07fa18b48eaefd
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https://doaj.org/article/232b9cbd125145b38e07fa18b48eaefd

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