Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors

• URL: http://arxiv.org/abs/2603.02616v1
• Date: Tue, 03 Mar 2026 05:39:32 GMT
• Title: Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
• Authors: Ya Zhou, Zhaohong Sun, Tianxiang Hao, Xiangjie Li,
• Abstract summary: Structural heart disease (SHD) is a prevalent condition with many undiagnosed cases.<n>Recent studies show that artificial intelligence (AI)-based analysis of electrocardiograms (ECGs) can detect SHD.<n>Existing methods are fully black-box models, limiting interpretability and clinical adoption.
• Score: 8.817617912039616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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