Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease.
Alzheimer's disease
computer‐aided diagnosis
neurobiological basis
neuroscientifically interpretable biomarkers
structural magnetic resonance imaging
Journal
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
ISSN: 2198-3844
Titre abrégé: Adv Sci (Weinh)
Pays: Germany
ID NLM: 101664569
Informations de publication
Date de publication:
Jul 2020
Jul 2020
Historique:
received:
22
02
2020
revised:
01
05
2020
entrez:
28
7
2020
pubmed:
28
7
2020
medline:
28
7
2020
Statut:
epublish
Résumé
Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter (
Identifiants
pubmed: 32714766
doi: 10.1002/advs.202000675
pii: ADVS1853
pmc: PMC7375255
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2000675Informations de copyright
© 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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