Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening

Duc N. Do; Minh N. Do; Dang Nguyen; Khanh T. Q. Le; Khoa D. Pham; Hung N. Huynh; Phi Pham-Van-Hoang; Quan K. Huynh; Ramez M. Odat; Perisa Ashar; Ethan Philip Lowder; Minh H. N. Le; Hoang Le; Phat V. H. Nguyen; Quan Le; Jacques Kpodonu; Phat K. Huynh

arXiv · 2604.23385

Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening

Duc N. Do·Minh N. Do·Dang Nguyen·Khanh T. Q. Le·Khoa D. Pham·Hung N. Huynh·Phi Pham-Van-Hoang·Quan K. Huynh·Ramez M. Odat·Perisa Ashar·Ethan Philip Lowder·Minh H. N. Le·Hoang Le·Phat V. H. Nguyen·Quan Le·Jacques Kpodonu·Phat K. Huynh

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Abstract

Transthoracic echocardiography is the reference standard for confirming structural heart disease (SHD), but first-line screening is limited by cost, workflow burden, and specialist availability. We evaluated whether open pretrained electrocardiogram (ECG) foundation models can support echo-confirmed multi-label SHD detection using the public EchoNext Mini-Model benchmark. Six echocardiography-derived abnormalities were targeted: reduced left ventricular ejection fraction, increased left ventricular wall thickness, aortic stenosis, mitral regurgitation, tricuspid regurgitation, and right ventricular systolic dysfunction. Under a common pipeline, we compared engineered ECG features with gradient boosting, end-to-end waveform learning from scratch, and transfer from open ECG foundation models. We then applied in-domain self-supervised adaptation of an ECG foundation model (ECG-FM) on EchoNext waveforms followed by selective supervised fine-tuning, and evaluated trade-offs between discrimination and adaptation cost. Adapted ECG-FM models achieved the best overall performance: peak macro-AUROC 0.8509 and macro-AUPRC 0.4297, while a parameter-efficient operating point preserved AUROC (0.8501) and attained the highest fixed-threshold macro-F1 0.3691. Late fusion with covariates did not improve threshold-independent discrimination, and evaluated LoRA, alternative backbones, and mixture-of-foundations strategies did not surpass the best adapted single-backbone models. These results indicate that for ECG-based case finding and echocardiography triage, combining target-domain self-supervised adaptation with selective supervised updating of a pretrained ECG backbone is the most effective transfer strategy.

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Duc N. Do, Minh N. Do, Dang Nguyen, Khanh T. Q. Le, Khoa D. Pham, Hung N. Huynh, Phi Pham-Van-Hoang, Quan K. Huynh, Ramez M. Odat, Perisa Ashar, Ethan Philip Lowder, Minh H. N. Le, Hoang Le, Phat V. H. Nguyen, Quan Le, Jacques Kpodonu, Phat K. Huynh. 2026-04-25. Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening. https://arxiv.org/abs/2604.23385

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