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

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 dat...

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Bibliographic Details
Published in:iScience Vol. 27; no. 10; p. 110945
Main Authors: Fan, Fan, Song, Hao, Jiang, Jiu, He, Haoying, Sun, Dong, Xu, Zhipeng, Peng, Sisi, Zhang, Ran, Li, Tian, Cao, Jing, Xu, Juan, Peng, Xiaoxiang, Lei, Ming, He, Chu, Zhang, Junjian
Format: Journal Article
Language:English
Published: United States Elsevier Inc 18.10.2024
Elsevier
Subjects:
Artificial intelligence
Clinical neuroscience
Health technology
Artificial intelligence
Health technology
Clinical neuroscience
ISSN:2589-0042, 2589-0042
Online Access:Get full text
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Summary: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. [Display omitted] •Our model provides clinicians with a clinical decision tool for diagnosing VCI•Our model performed excellently in both internal and external validation•The diagnostic performance of the model can be on par with that of clinical experts•Our model identifies key brain regions and clinical features related to VCI Health technology; Clinical neuroscience; Artificial intelligence
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These authors contributed equally
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ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2024.110945