Cardiac Echocardiographic Analysis with Multi-Scale Effective Fusion Module: A Novel Stroke Prediction Approach

Purpose: To develop a stroke risk prediction model by integrating echocardiographic images (long-axis, short-axis, four-chamber views) and clinical indicators using a novel multi-scale effective fusion (MSEF) module. Methods: A total of 712 patients with 10,992 images and 27 clinical indicators were included. The MSEF module enhances multi-scale feature fusion by combining deep semantic and shallow high-resolution features. It consists of four components: Global Feature Fusion (GFF), Multi-Feature Reconstruction (MFR), Channel Attention, and Positional Attention, effectively improving small-target feature representation. The fused features and clinical indicators were used to train the stroke prediction model. Results: The proposed MSEF-based model achieved the highest performance, with an Accuracy of 76.8% and F1 Score of 64.7% on the test set. Ablation studies confirmed the importance of Channel Attention and Position Attention in enhancing feature representation. When integrating echocardiographic features with clinical indicators, the model achieved an Accuracy of 80.2% and F1 Score of 72.1% on the test set. Conclusion: The proposed MSEF-based approach effectively integrates imaging and clinical data, improving stroke risk prediction accuracy and offering a promising tool for clinical decision-making.