Structural heart disease (SHD) is a prevalent condition with many undiagnosed cases, and early detection is often limited by the high cost and accessibility constraints of echocardiography (ECHO). Recent studies show that artificial intelligence (AI)-based analysis of electrocardiograms (ECGs) can detect SHD, offering a scalable alternative. However, existing methods are fully black-box models, limiting interpretability and clinical adoption. To address these challenges, we propose an interpretable and effective framework that integrates clinically meaningful ECG foundation-model predictors within a generalized additive model, enabling transparent risk attribution while maintaining strong predictive performance. Using the EchoNext benchmark of over 80,000 ECG-ECHO pairs, the method demonstrates relative improvements of +0.98% in AUROC, +1.01% in AUPRC, and +1.41% in F1 score over the latest state-of-the-art deep-learning baseline, while achieving slightly better performance even with only 30% of the training data. Subgroup analyses confirm robust performance across heterogeneous populations, and the estimated entry-wise functions provide interpretable insights into the relationships between risks of traditional ECG diagnoses and SHD. This work illustrates a complementary paradigm between classical statistical modeling and modern AI, offering a pathway to interpretable, high-performing, and clinically actionable ECG-based SHD screening.

翻译：结构性心脏病（SHD）是一种普遍存在且许多病例未被诊断的疾病，其早期检测常因超声心动图（ECHO）的高成本和可及性限制而受限。近期研究表明，基于人工智能（AI）的心电图（ECG）分析能够检测SHD，提供了一种可扩展的替代方案。然而，现有方法均为完全的黑盒模型，限制了可解释性和临床采纳。为应对这些挑战，我们提出了一种可解释且有效的框架，该框架将具有临床意义的心电图基础模型预测器集成到广义可加模型中，在保持强大预测性能的同时实现透明的风险归因。使用包含超过80,000对ECG-ECHO数据的EchoNext基准测试，该方法在AUROC、AUPRC和F1分数上相较于最新的最先进深度学习基线分别实现了+0.98%、+1.01%和+1.41%的相对提升，并且即使在仅使用30%训练数据的情况下也取得了略优的性能。亚组分析证实了该方法在异质人群中的稳健性能，并且估计的逐项函数为传统心电图诊断风险与SHD之间的关系提供了可解释的见解。这项工作阐释了经典统计建模与现代AI之间的互补范式，为基于心电图的、可解释、高性能且具有临床可操作性的SHD筛查提供了一条路径。