%0 Journal Article
%A Lohner, Valerie
%A Badhwar, Amanpreet
%A Detcheverry, Flavie E
%A García, Cindy L
%A Gellersen, Helena M
%A Khodakarami, Zahra
%A Lattmann, Rene
%A Li, Rui
%A Low, Audrey
%A Mazo, Claudia
%A Metz, Amelie
%A Parent, Olivier
%A Phillips, Veronica
%A Saeed, Usman
%A Tan, Sean Y W
%A Tamburin, Stefano
%A Llewellyn, David J
%A Rittman, Timothy
%A Waters, Sheena
%A Bernal, Jose
%T Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.
%J Alzheimer's research & therapy
%V 17
%N 1
%@ 1758-9193
%C London
%I BioMed Central
%M DZNE-2025-00937
%P 183
%D 2025
%X Background:Cerebral small vessel disease (CSVD) is a common neurological condition that contributes to strokes, dementia, disability, and mortality worldwide. We conducted a systematic review and meta-analysis to investigate the use of neuroimaging CSVD markers in machine learning (ML) based diagnosis and prognosis of cognitive impairment and dementia, and identify both methodological changes over time and barriers to clinical translation.Methods:Following the PRISMA guidelines, we systematically searched for original studies that used both neuroimaging CSVD markers and ML methods for diagnosing and prognosing neurodegenerative diseases (preregistration in PROSPERO: CRD42022366767). Each paper was independently reviewed by a pair of reviewers at all stages, with a third consulted to resolve conflicts. We meta-analysed the effectiveness of ML models to distinguish healthy controls from Alzheimer’s dementia and cognitive impairment, using area under the curve (AUC) as the performance metric.Results:We identified 75 studies: 43 on diagnosis, 27 on prognosis, and 5 on both. Nearly 60
%K Alzheimer’s dementia (Other)
%K Artificial intelligence (Other)
%K Cerebral small vessel disease (Other)
%K Cognitive impairment (Other)
%K Dementia (Other)
%K Machine learning (Other)
%K Neurodegenerative diseases (Other)
%K Neuroimaging (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40775365
%R 10.1186/s13195-025-01815-6
%U https://pub.dzne.de/record/280259