Federated learning for prenatal detection of interrupted aortic arch using fetal ultrasound imaging

This study presents the first application of federated learning (FL) for prenatal detection of Interrupted Aortic Arch (IAA) using fetal ultrasound images. To address the challenges of data scarcity, privacy constraints, and inter-institutional variability, we develop a federated learning IAA detection method and systematically evaluate three representative strategies (FedAvg, FedProx, and FedBABU) across five clinical centres. Results show that FL improves model performance over local training in recall and F1-score in data-scarce centres. Among FL algorithms, FedAvg and FedProx consistently outperform FedBABU in stability and generalisation. Among the three CNN architectures compared — ResNet-50, EfficientNet-B3, and DenseNet-121 — DenseNet-121 demonstrates superior overall performance, particularly in non-independent and identically distributed (Non-IID) scenarios. Our framework demonstrates the feasibility of collaborative AI for rare disease detection without data sharing, laying the foundation for scalable, real-world prenatal screening of congenital heart defects.