目的 探讨多模态深度学习融合心脏超声与心电图特征对冠心病患者心源性猝死的预测价值。方法 选取2024年1月—2025年6月收治的60例冠心病患者，所有患者均接受心脏超声、心电图检查及多模态深度学习模型预测，随访6个月记录心源性猝死事件。根据随访结果分为事件组（n=15）和非事件组（n=45），比较两组临床资料及各项指标差异。结果 多模态深度学习模型预测敏感度为86.67%，特异度为91.11%，准确率为90.00%，AUC为0.923，显著优于单独心脏超声（AUC=0.761）和单独心电图（AUC=0.788）。结论 多模态深度学习融合心脏超声与心电图特征能够有效预测冠心病患者心源性猝死风险，预测性能优于传统单一模态评估方法。

      Objective To investigate the predictive value of multimodal deep learning integrating echocardiography and electrocardiogram features for sudden cardiac death in patients with coronary heart disease．Methods A total of 60 patients with coronary heart disease admitted from January 2024 to June 2025 were enrolled．All patients underwent echocardiography，electrocardiogram examination，and multimodal deep learning model prediction，with a 6-month follow-up to record sudden cardiac death events．According to the follow-up results，patients were divided into the event group（n=15） and non-event group（n=45），and clinical data and various indicators were compared between the two groups．Results The multimodal deep learning model achieved a sensitivity of 86.67%，specificity of 91.11%，accuracy of 90.00%，and AUC of 0.923，which were significantly superior to echocardiography alone（AUC=0.761） and electrocardiogram alone（AUC=0.788）．Conclusions Multimodal deep learning integrating echocardiography and electrocardiogram features can effectively predict the risk of sudden cardiac death in patients with coronary heart disease，with predictive performance superior to traditional single-modality assessment methods．