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000280259 1001_ $$0P:(DE-2719)2811856$$aLohner, Valerie$$b0
000280259 245__ $$aMachine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.
000280259 260__ $$aLondon$$bBioMed Central$$c2025
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000280259 520__ $$aBackground: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% 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.Conclusions: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.
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000280259 650_7 $$2Other$$aAlzheimer’s dementia
000280259 650_7 $$2Other$$aArtificial intelligence
000280259 650_7 $$2Other$$aCerebral small vessel disease
000280259 650_7 $$2Other$$aCognitive impairment
000280259 650_7 $$2Other$$aDementia
000280259 650_7 $$2Other$$aMachine learning
000280259 650_7 $$2Other$$aNeurodegenerative diseases
000280259 650_7 $$2Other$$aNeuroimaging
000280259 7001_ $$00000-0003-3414-3395$$aBadhwar, Amanpreet$$b1
000280259 7001_ $$00000-0002-9909-9754$$aDetcheverry, Flavie E$$b2
000280259 7001_ $$aGarcía, Cindy L$$b3
000280259 7001_ $$0P:(DE-2719)9002125$$aGellersen, Helena M$$b4
000280259 7001_ $$aKhodakarami, Zahra$$b5
000280259 7001_ $$0P:(DE-2719)9001995$$aLattmann, Rene$$b6$$udzne
000280259 7001_ $$aLi, Rui$$b7
000280259 7001_ $$00000-0002-9960-9849$$aLow, Audrey$$b8
000280259 7001_ $$00000-0003-1703-8964$$aMazo, Claudia$$b9
000280259 7001_ $$00000-0001-9104-5383$$aMetz, Amelie$$b10
000280259 7001_ $$00000-0002-3177-0353$$aParent, Olivier$$b11
000280259 7001_ $$00000-0002-4383-9434$$aPhillips, Veronica$$b12
000280259 7001_ $$aSaeed, Usman$$b13
000280259 7001_ $$aTan, Sean Y W$$b14
000280259 7001_ $$00000-0002-1561-2187$$aTamburin, Stefano$$b15
000280259 7001_ $$aLlewellyn, David J$$b16
000280259 7001_ $$00000-0003-1063-6937$$aRittman, Timothy$$b17
000280259 7001_ $$00000-0001-7241-2272$$aWaters, Sheena$$b18
000280259 7001_ $$0P:(DE-2719)9001989$$aBernal, Jose$$b19$$eLast author
000280259 773__ $$0PERI:(DE-600)2506521-X$$a10.1186/s13195-025-01815-6$$gVol. 17, no. 1, p. 183$$n1$$p183$$tAlzheimer's research & therapy$$v17$$x1758-9193$$y2025
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