Enhanced stroke risk prediction in hypertensive patients through deep learning integration of imaging and clinical data

作者：Hui Li, Tianyu Zhang, Guochao Han, Zonghui Huang, Huiyu Xiao, Yicheng Ni, Bo Liu, Wennan Lin, Lin Yuan · 发表于：BMC Medical Informatics and Decision Making · 年份：2025 · DOI：10.1186/s12911-025-03120-6 · 被引用次数：12 · 研究领域：Acute Ischemic Stroke Management、Cardiovascular Health and Disease Prevention、Artificial Intelligence in Healthcare

BACKGROUND: Stroke is one of the leading causes of death and disability worldwide, with a significantly elevated incidence among individuals with hypertension. Conventional risk assessment methods primarily rely on a limited set of clinical parameters and often exclude imaging-derived structural features, resulting in suboptimal predictive accuracy. OBJECTIVE: This study aimed to develop a deep learning-based multimodal stroke risk prediction model by integrating carotid ultrasound imaging with multidimensional clinical data to enable precise identification of high-risk individuals among hypertensive patients. METHODS: A total of 2,176 carotid artery ultrasound images from 1,088 hypertensive patients were collected. ResNet50 was employed to automatically segment the carotid intima-media and extract key structural features. These imaging features, along with clinical variables such as age, blood pressure, and smoking history, were fused using a Vision Transformer (ViT) and fed into a Radial Basis Probabilistic Neural Network (RBPNN) for risk stratification. The model's performance was systematically evaluated using metrics including AUC, Dice coefficient, IoU, and Precision-Recall curves. RESULTS: The proposed multimodal fusion model achieved outstanding performance on the test set, with an AUC of 0.97, a Dice coefficient of 0.90, and an IoU of 0.80. Ablation studies demonstrated that the inclusion of ViT and RBPNN modules significantly enhanced predictive accuracy. Subgroup a...