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Multimodal dynamic hierarchical clustering model for post-stroke cognitive impairment prediction
Chen Bai1,2, Tan Li3, Yanyan Zheng4
1Neurology Department, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, Zhejiang, 32500, China.
Visual Computing for Industry, Biomedicine, and Art
|September 1, 2025
Summary
This study introduces a new AI model for early prediction of post-stroke cognitive impairment (PSCI). The multimodal dynamic hierarchical clustering network (MDHCNet) accurately identifies brain changes linked to cognitive decline after stroke.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Imaging
Background:
- Post-stroke cognitive impairment (PSCI) is a frequent and severe outcome of stroke.
- Early prediction of PSCI is crucial for personalized rehabilitation strategies.
- Current prediction methods face challenges in integrating multimodal data and capturing complex brain alterations.
Purpose of the Study:
- To develop an accurate and interpretable AI model for early prediction of PSCI.
- To leverage multimodal neuroimaging and clinical data for improved prediction.
- To introduce the multimodal dynamic hierarchical clustering network (MDHCNet).
Main Methods:
- Constructing brain graphs from diffusion-weighted imaging, magnetic resonance angiography, and T1/T2-weighted MRI.
- Integrating multimodal brain graphs with clinical features using a hierarchical cross-modal fusion module.
- Utilizing a graph neural network architecture (MDHCNet) for PSCI prediction.
Main Results:
- MDHCNet demonstrated superior performance compared to existing deep learning models in a real-world stroke cohort.
- Ablation studies confirmed the effectiveness of multimodal data fusion.
- Saliency-based interpretation identified key brain regions associated with cognitive impairment.
Conclusions:
- MDHCNet offers an effective and explainable approach for early PSCI prediction.
- The model has the potential to guide individualized clinical decision-making in stroke rehabilitation.
- This work advances the application of AI in understanding and managing neurological disorders.
