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.

翻译：低左心室射血分数（LEF）在进展为症状性心力衰竭前常未被发现，凸显了可扩展筛查策略的必要性。尽管人工智能心电图（AI-ECG）已展现出潜力，但现有方法要么依赖可解释性有限的全端到端黑箱模型，要么依赖基于商业ECG测量算法的表格系统且性能欠佳。我们提出了基于ECG的预测因子驱动LEF检测框架（ECGPD-LEF），该结构化框架将基础模型衍生的诊断概率与可解释建模相结合，用于从ECG中检测LEF。基于包含72,475个ECG-超声心动图对的基准EchoNext数据集训练，并在预设的独立内部（n=5,442）和外部（n=16,017）队列中评估，该框架在中等LEF检测中实现了稳健的区分能力（内部AUROC 88.4%，F1分数64.5%；外部AUROC 86.8%，F1分数53.6%），在各人口统计学和临床亚组中均持续优于基准数据集提供的官方端到端基线模型。可解释性分析识别出驱动LEF风险估计的高影响预测因子，包括正常心电图、不完全性左束支传导阻滞及前侧壁心内膜下损伤。值得注意的是，这些预测因子无需任务特定重新训练即可独立实现零样本式推断（内部AUROC 75.3–81.0%；外部AUROC 71.6–78.6%），表明心室功能障碍本质上编码于结构化诊断概率表征中。该框架在预测性能与机制透明度之间达成平衡，通过额外预测因子实现可扩展增强，并可无缝集成至现有AI-ECG系统。