Development and validation of a multimodal deep learning framework for vascular cognitive impairment diagnosis

作者：Fan Fan, Hao Song, Jiu Jiang, Haoying He, Dong Sun, Zhipeng Xu, Sisi Peng, Ran Zhang, Tianrui Li, Jing Cao, Juan Xu, Xiaoxiang Peng, Ming Lei, Chu He, Junjian Zhang · 发表于：iScience · 年份：2024 · DOI：10.1016/j.isci.2024.110945 · 被引用次数：11 · 研究领域：Acute Ischemic Stroke Management、Cerebrovascular and Carotid Artery Diseases、Dementia and Cognitive Impairment Research

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.