Transfer Learning for ECG-Based Age Estimation from Adult to Pediatric Populations

Electrocardiogram (ECG)-based age prediction has emerged as a promising tool in medical AI, providing insights into physiological aging and potential health risks. While existing deep learning models have shown strong performance on adult populations using 10-second ECG recordings, their applicability to pediatric subjects remains largely unexplored. In this study, we tackled restrains set by the limited availability of pediatric ECG data by adopting a transfer learning approach: we first trained a convolutional neural network on single heartbeats from adult ECGs taken from the PTB-XL database. Then, we fine-tuned it on pediatric ECGs collected at the Buzzi Children Hospital, Milan, Italy. Our model achieved a RMSE of 10.32 years and a MAE of 8.03 years on adult data, which were found comparable to prior works trained on longer segments of ECG signals. In the pediatric dataset, the model achieved a RMSE of 2.67 years and a MAE of 1.88 years. These results suggest that meaningful age-related features can be extracted even from single heartbeats and that transfer learning enables effective adaptation across age groups, offering a practical solution for pediatric age estimation or in other contexts where available data might be typically more scarce.