Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

Abstract Introduction Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods...

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Bibliographic Details
Main Authors: Patricio Andres Donnelly‐Kehoe, Guido Orlando Pascariello, Adolfo M. García, John R. Hodges, Bruce Miller, Howie Rosen, Facundo Manes, Ramon Landin‐Romero, Diana Matallana, Cecilia Serrano, Eduar Herrera, Pablo Reyes, Hernando Santamaria‐Garcia, Fiona Kumfor, Olivier Piguet, Agustin Ibanez, Lucas Sedeño
Format: Article
Language:English
Published: Wiley 2019-12-01
Series:Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
Subjects:
Dementia
bvFTD
Data‐driven computational approaches
Classifiers
Neuroimaging
Online Access:https://doi.org/10.1016/j.dadm.2019.06.002

Internet

https://doi.org/10.1016/j.dadm.2019.06.002

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