用可解释框架从心电图精准识别左室射血分数降低
Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
- 融合基础模型诊断概率与可解释建模，提升心电图筛查效能
- 内部外部验证均超越基线模型，外部队列AUROC达86.8%
- 关键预测因子可零样本推理，适合临床集成与医生理解
左室射血分数（LEF）降低常在心衰症状出现后才被发现，亟需可扩展的筛查手段。现有基于人工智能的心电图（AI-ECG）方法多依赖黑箱端到端模型或依赖商业测量算法的表格系统，性能有限。本文提出基于心电图的预测驱动低射血分数检测框架（ECGPD-LEF），整合基础模型生成的诊断概率与可解释建模，从心电图中检测LEF。该框架在包含72,475对心电图-超声心动图数据的EchoNext基准数据集上训练，并在预定义的独立内部队列（n=5,442）和外部队列（n=16,017）上评估。结果显示，该框架对中度LEF具有稳健判别能力（内部队列AUROC 88.4%，F1 64.5%；外部队列AUROC 86.8%，F1 53.6%），在各人口统计学和临床亚组中持续优于官方提供的端到端基线模型。可解释性分析识别出高影响预测因子，包括正常心电图、不完全左束支传导阻滞及前侧壁心内膜下损伤，这些特征驱动了LEF风险估计。值得注意的是，仅凭这些预测因子即可实现零样本式推断（内部队列AUROC 75.3–81.0%；外部队列AUROC 71.6–78.6%），表明心室功能障碍信息已内在编码于结构化诊断概率表示中。该框架兼顾预测性能与机制透明性，支持通过新增预测因子进行可扩展增强，并可无缝集成至现有AI-ECG系统。
原文摘要 · Abstract (English)
Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for scalable screening strategies. Although artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise, existing approaches rely solely on end-to-end black-box models with limited interpretability or on tabular systems dependent on commercial ECG measurement algorithms with suboptimal performance. We introduced ECG-based Predictor-Driven LEF (ECGPD-LEF), a structured framework that integrates foundation model-derived diagnostic probabilities with interpretable modeling for detecting LEF from ECG. Trained on the benchmark EchoNext dataset comprising 72,475 ECG-echocardiogram pairs and evaluated in predefined independent internal (n=5,442) and external (n=16,017) cohorts, our framework achieved robust discrimination for moderate LEF (internal AUROC 88.4%, F1 64.5%; external AUROC 86.8%, F1 53.6%), consistently outperforming the official end-to-end baseline provided with the benchmark across demographic and clinical subgroups. Interpretability analyses identified high-impact predictors, including normal ECG, incomplete left bundle branch block, and subendocardial injury in anterolateral leads, driving LEF risk estimation. Notably, these predictors independently enabled zero-shot-like inference without task-specific retraining (internal AUROC 75.3-81.0%; external AUROC 71.6-78.6%), indicating that ventricular dysfunction is intrinsically encoded within structured diagnostic probability representations. This framework reconciles predictive performance with mechanistic transparency, supporting scalable enhancement through additional predictors and seamless integration with existing AI-ECG systems.
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