arXiv:2611.07717v1 Announce Type: new
Abstract: Pediatric electrocardiogram (ECG) interpretation is complicated by age-dependent electrophysiological changes that limit direct transfer of adult AI models. We asked whether an adult ECG foundation model could be adapted to pediatric ECGs. Using 339,563 12-lead ECGs from 238,189 children, we compared direct application, pediatric fine-tuning, and training from scratch under an identical architecture. Direct transfer was poor (mean AUC 0.8256). Fine-tuning reached 0.9695, above de novo training (0.9661), and attained 0.9079 using only 1% of training data. On 10,999 external ECGs, fine-tuning achieved a mean AUC of 0.9385 across 30 labels. It generalized to age regression (MAE 1.12 years) and congenital heart disease classification (AUC 0.9562). Across 17 clinicians and 100 cases, diagnostic accuracy rose from 56.8% to 65.4% (p < 0.001) and did not decline when AI erred. Adult ECG foundation models require pediatric adaptation; fine-tuning offers a data-efficient route to scalable pediatric ECG AI.
arxiv
Adult-to-Pediatric Fine-Tuning Improves ECG Foundation Model Performance
Researchers adapted an adult ECG foundation model to pediatric ECGs using 339,563 12-lead recordings from 238,189 children, comparing direct transfer, pediatric fine-tuning, and training from scratch. Pediatric fine-tuning achieved a mean AUC of 0.9695, above direct transfer at 0.8256 and de novo training at 0.9661, and reached 0.9079 using only 1% of training data. On 10,999 external ECGs, fine-tuning achieved a mean AUC of 0.9385 across 30 labels and also generalized to age regression and congenital heart disease classification. In a study with 17 clinicians and 100 cases, diagnostic accuracy rose from 56.8% to 65.4% and did not decline when the AI erred.
It provides concrete evidence that adult ECG foundation models need pediatric adaptation and that fine-tuning is a data-efficient route to scalable pediatric ECG AI.
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