Effective first-trimester screening for congenital heart disease (CHD) remains an unmet clinical need, hindered by technical constraints and the lack of validated diagnostic tools. While artificial intelligence (AI) offers promise, its progress is restricted by data scarcity and privacy concerns surrounding data sharing. Federated learning (FL) offers a promising paradigm for collaborative model training without exposing sensitive patient data. In this study, we establish a Federated Congenital Heart Disease Learning to enable cross-hospital collaboration in early CHD diagnosis. A major challenge arises from inter-hospital heterogeneity, where variations in ultrasound devices, scanning protocols, and patient demographics lead to significant feature distribution shifts, resulting in poor performance. To address this, we introduce federated prototypes that align both clinical concept and disease subtype representations across participating sites, effectively calibrating local updates and enhancing global consistency. Experiments conducted across four tertiary hospitals demonstrate that our method achieves a 10.3% improvement in F1 score, 5.1% increase in sensitivity, and 1.0% improvement in specificity over state-of-the-art federated approaches. These results highlight our effectiveness in improving generalization under real-world clinical heterogeneity. Our implementation and benchmarking resources are publicly available at: https://github.com/WenkeHuang/FLCHD.
Huang et al. (Fri,) studied this question.