Pediatric ECG foundation model via adult-to-pediatric fine-tuning: downstream tasks and clinical evaluation

Authors: Yu Cao , Haiyan Xu , Xiaohan Li , Jian Yang , Yuqing Feng , Wei Wang , Haomin Li

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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.

Comments:

20 pages,3 figures

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.07717 [cs.AI]

(or

arXiv:2611.07717v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07717

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arXiv-issued DOI via DataCite (pending registration)

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From: Haomin Li [ view email ]

[v1]

Thu, 5 Nov 2026 09:36:44 UTC (1,517 KB)

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