ArticleiScience2024
Development and validation of a multimodal deep learning framework for vascular cognitive impairment diagnosis.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- Deep Learning for Classifying and Cognitive Profiling of Subcortical Vascular Cognitive Impairment.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.Scientific reports · 2025Article
- Deep learning and high-resolution magnetic resonance vascular wall imaging: current challenges and future perspectives.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebrovascular disease (CVD) is the second leading cause of dementia worldwide. The accurate detection of vascular cognitive impairment (VCI) in CVD patients remains an unresolved challenge. We collected the clinical non-imaging data and neuroimaging data from 307 subjects with CVD. Using these data, we developed a multimodal deep learning framework that combined the vision transformer and extreme gradient boosting algorithms. The final hybrid model within the framework included only two neuroimaging features and six clinical features, demonstrating robust performance across both internal and external datasets. Furthermore, the diagnostic performance of our model on a specific dataset was demonstrated to be comparable to that of expert clinicians. Notably, our model can identify the brain regions and clinical features that significantly contribute to the VCI diagnosis, thereby enhancing transparency and interpretability. We developed an accurate and explainable clinical decision support tool to identify the presence of VCI in patients with CVD.
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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.