PEACE: Cross-modal Enhanced Pediatric-Adult ECG Alignment for Robust Pediatric Diagnosis

TL;DR

PEACE is a novel framework that enhances pediatric ECG diagnosis by aligning adult ECG data through cross-modal semantic supervision, improving transfer learning in low-resource settings.

Contribution

The paper introduces PEACE, a structured cross-modal alignment method that leverages semantic descriptors and curriculum optimization for adult-to-pediatric ECG transfer.

Findings

PEACE achieves up to 90.89% AUC in zero-shot settings.

It reaches 96.65% AUC on PTB-XL dataset.

Semantic supervision improves transfer performance in low-resource scenarios.

Abstract

Automated pediatric electrocardiogram (ECG) diagnosis remains challenging because models trained predominantly on adult data suffer from substantial cross-population mismatch, while pediatric labels are often scarce. We present PEACE (Pediatric-Adult ECG Alignment via Cross-modal Enhancement), a structured cross-modal alignment framework for adult-to-pediatric ECG transfer. PEACE integrates tri-axial clinical semantic decomposition, label-query feature extraction, and curriculum-gated optimization to align transferable adult ECG representations with pediatric diagnostic targets. Since ZZU-pECG provides no paired clinical reports, we generate label-conditioned semantic descriptors using Gemini with concise clinical prompts and use them only as auxiliary training supervision; inference remains ECG-only. On ZZU-pECG, PEACE achieves 59.39%, 79.03%, and 90.89% AUC under zero-shot, 50-shot, and full fine-tuning settings, respectively, and reaches 96.65% AUC on the shared PTB-XL label space. These results suggest that structured clinical semantic supervision can improve low-resource adult-to-pediatric ECG transfer, while prospective clinical validation and more explicit age-aware modeling remain necessary before real-world deployment.