ArticleCerebral circulation - cognition and behavior2023
Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models.
Article in Cerebral circulation - cognition and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.Alzheimer's research & therapy · 2025Pooled it
- Magnetic Resonance Imaging in Cerebral Small Vessel Disease-Related Depression: From Visual Scoring to Artificial Intelligence.CNS neuroscience & therapeutics · 2026Review
- Predicting 90-day mortality in patients with HBV-ACLF: A comparison of machine learning models and logistic regression.Medicine · 2026Article
- Deep learning for early detection of cerebral small vessel disease using self-supervised graph embeddings and retinal image analysis.Scientific reports · 2026Article
- Prediction of recurrence and functional status in young ischemic stroke patients: Comparison of machine learning and traditional statistical methods.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.Frontiers in neurology · 2024Article
- Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning.Journal of personalized medicine · 2023Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods. Methods: We included three cohorts with varying SVD severity (RUN DMC, Results: We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature. Conclusions: When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.