用心脏超声和病历预测心脏病治疗方案，不需有创检查。
TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction
- 用表格数据引导视频注意力，融合多模态信息提升判断力。
- 在9000例数据上准确率67.6%，干预预测准确率达88.6%。
- 适合医疗资源不足地区，帮助快速筛选需做造影的患者。
冠状动脉造影仍是急性冠脉综合征（ACS）诊断的金标准，但其高成本和侵入性可能导致患者暴露于操作风险并延迟诊断，进而延误治疗。本文提出TREAT-Net，一种基于多模态深度学习的ACS治疗预测框架，利用非侵入性数据，包括超声心动图视频和结构化临床记录。TREAT-Net通过表格引导的交叉注意力机制增强视频解读，并采用晚期融合机制对齐各模态预测结果。在超过9000例ACS病例的数据集上训练，该模型优于单模态及未融合基线，达到67.6%的平衡准确率和71.1%的AUROC。跨模态一致性分析显示干预预测准确率为88.6%。结果表明，TREAT-Net可作为非侵入性工具，实现及时准确的患者分诊，尤其适用于缺乏冠状动脉造影资源的群体。
原文摘要 · Abstract (English)
Coronary angiography remains the gold standard for diagnosing Acute Coronary Syndrome (ACS). However, its resource-intensive and invasive nature can expose patients to procedural risks and diagnostic delays, leading to postponed treatment initiation. In this work, we introduce TREAT-Net, a multimodal deep learning framework for ACS treatment prediction that leverages non-invasive modalities, including echocardiography videos and structured clinical records. TREAT-Net integrates tabular-guided cross-attention to enhance video interpretation, along with a late fusion mechanism to align predictions across modalities. Trained on a dataset of over 9000 ACS cases, the model outperforms unimodal and non-fused baselines, achieving a balanced accuracy of 67.6% and an AUROC of 71.1%. Cross-modality agreement analysis demonstrates 88.6% accuracy for intervention prediction. These findings highlight the potential of TREAT-Net as a non-invasive tool for timely and accurate patient triage, particularly in underserved populations with limited access to coronary angiography.
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