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Updated: Jun 10, 2025
A Mouse Model for Vascular Cognitive Impairment and Dementia Based on Needle-guided Asymmetric Bilateral Common Carotid Artery Stenosis
Published on: November 22, 2024
Development and validation of a multimodal deep learning framework for vascular cognitive impairment diagnosis
Fan Fan1, Hao Song1, Jiu Jiang2
1Department of Neurology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei province, China.
Insights
A new AI tool accurately detects vascular cognitive impairment in cerebrovascular disease patients. This explainable model uses limited data, matching clinician performance for early dementia detection.
Area of Science:
- Neurology
- Artificial Intelligence
- Medical Imaging
Background:
- Cerebrovascular disease (CVD) is a major cause of dementia globally.
- Accurate detection of vascular cognitive impairment (VCI) in CVD patients is challenging.
Purpose of the Study:
- To develop a multimodal deep learning framework for VCI detection in CVD patients.
- To create an accurate and interpretable clinical decision support tool.
Main Methods:
- Collected clinical and neuroimaging data from 307 CVD subjects.
- Developed a hybrid model combining vision transformer and extreme gradient boosting.
- Utilized a framework incorporating six clinical and two neuroimaging features.
Main Results:
- The hybrid model showed robust performance on internal and external datasets.
- Diagnostic accuracy was comparable to expert clinicians.
- The model identified key brain regions and clinical features contributing to VCI diagnosis.
Conclusions:
- An accurate and explainable AI tool for VCI detection in CVD patients has been developed.
- The tool enhances transparency and interpretability in VCI diagnosis.
- This approach aids in early identification and management of VCI in CVD.
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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