Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1989
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dc.contributor.authorBailey, Neil-
dc.contributor.authorHoy, Kate-
dc.date2020-10-29-
dc.date.accessioned2021-06-10T04:57:11Z-
dc.date.available2021-06-10T04:57:11Z-
dc.date.issued2021-01-
dc.identifier.citation132 (1), pp.207-209en_US
dc.identifier.issn1388-2457en_US
dc.identifier.urihttp://hdl.handle.net/11434/1989-
dc.description.abstractEditorial discussing the retrospective and exploratory study by Ferri et al (2020) published in this issue, which tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer’s disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.en_US
dc.publisherElsevieren_US
dc.subjectArtificial Neural Networksen_US
dc.subjectANNsen_US
dc.subjectAlzheimer's diseaseen_US
dc.subjectDementiaen_US
dc.subjectADDen_US
dc.subjectResting-state Electroencephalogramen_US
dc.subjectrsEEGen_US
dc.subjectStructural Magnetic Resonance Imagingen_US
dc.subjectsMRIen_US
dc.subjectEpworth Centre for Innovation in Mental Health, Epworth Healthcare and Monash University Department of Psychiatry, Camberwell, Victoriaen_US
dc.subjectEpworth Internal Medicine Clinical Institute, Epworth HealthCare, Victoria, Australiaen_US
dc.subjectNeurosciences Clinical Instituteen_US
dc.titleThe promise of artificial neural networks, EEG, and MRI for Alzheimer's disease.en_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1016/j.clinph.2020.10.009en_US
dc.identifier.journaltitleClinical Neurophysiologyen_US
dc.description.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/33176985/en_US
dc.type.contenttypeTexten_US
Appears in Collections:Neurosciences

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