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@ARTICLE{Lohner:280259,
      author       = {Lohner, Valerie and Badhwar, Amanpreet and Detcheverry,
                      Flavie E and García, Cindy L and Gellersen, Helena M and
                      Khodakarami, Zahra and Lattmann, Rene and Li, Rui and Low,
                      Audrey and Mazo, Claudia and Metz, Amelie and Parent,
                      Olivier and Phillips, Veronica and Saeed, Usman and Tan,
                      Sean Y W and Tamburin, Stefano and Llewellyn, David J and
                      Rittman, Timothy and Waters, Sheena and Bernal, Jose},
      title        = {{M}achine learning applications in vascular neuroimaging
                      for the diagnosis and prognosis of cognitive impairment and
                      dementia: a systematic review and meta-analysis.},
      journal      = {Alzheimer's research $\&$ therapy},
      volume       = {17},
      number       = {1},
      issn         = {1758-9193},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DZNE-2025-00937},
      pages        = {183},
      year         = {2025},
      abstract     = {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.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.We identified 75 studies: 43 on
                      diagnosis, 27 on prognosis, and 5 on both. Nearly $60\%$ of
                      studies were published in the past two years, reflecting a
                      growing interest in using CSVD markers in ML-based diagnosis
                      and prognosis of neurodegenerative diseases, especially
                      Alzheimer’s dementia. This rising interest may be linked
                      to the strong performance of such models: according to our
                      meta-analysis, ML approaches using CSVD markers perform well
                      in differentiating healthy controls from Alzheimer’s
                      dementia (AUC 0.88 $[95\%-CI$ 0.85–0.92]) and cognitive
                      impairment (AUC 0.84 $[95\%-CI$ 0.74–0.95]). However, the
                      growing interest has not been matched by methodological
                      rigour: only 16 studies met the criteria for inclusion in
                      the meta-analysis due to inconsistent reporting, only five
                      assessed the generalisability of their models on external
                      datasets, and six lacked clear diagnostic criteria.Interest
                      in incorporating CSVD markers into ML models for
                      neurodegenerative disease classification is on the rise, and
                      their performance suggests that this is worth further
                      exploration. Serious methodological issues, including
                      inconsistent reporting, limited generalisability testing,
                      and other potential biases, are unfortunately common and
                      hinder further adoption. Our targeted recommendations
                      provide a roadmap to accelerate the integration of ML into
                      clinical practice.The online version contains supplementary
                      material available at 10.1186/s13195-025-01815-6.},
      keywords     = {Alzheimer’s dementia (Other) / Artificial intelligence
                      (Other) / Cerebral small vessel disease (Other) / Cognitive
                      impairment (Other) / Dementia (Other) / Machine learning
                      (Other) / Neurodegenerative diseases (Other) / Neuroimaging
                      (Other)},
      cin          = {AG Berron / AG Düzel},
      ddc          = {610},
      cid          = {I:(DE-2719)5000070 / I:(DE-2719)5000006},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pmc          = {pmc:PMC12330124},
      pubmed       = {pmid:40775365},
      doi          = {10.1186/s13195-025-01815-6},
      url          = {https://pub.dzne.de/record/280259},
}