Abstract / Summary
Accurate prenatal screening from ultrasound imaging is severely constrained by the extreme spatial heterogeneity of fetal structures and the data scarcity inherent to rare congenital anomalies. To overcome these computational bottlenecks, we present RadarMAE, a data-efficient self-supervised representation learning framework tailored for fetal ultrasound analysis. By employing a novel, structure-aware masking strategy, RadarMAE enables robust pre-training on highly limited clinical datasets. Benchmarking the framework on first-trimester screening for trisomy 21 (Down syndrome), our model achieved exceptional diagnostic performance on an internal cohort, with an area under the curve (AUC) of 0.99, 97% accuracy, 94% sensitivity, and 98% specificity. Crucially, through domain-adversarial adaptation, the framework demonstrates robust cross-institutional generalization under extreme source-target data imbalance, bounding performance degradation to less than or equal to 0.04 across all key metrics in external testing. Furthermore, by integrating spatial registration with explainable AI, we constructed the first population-level facial risk atlas for trisomy 21. This data-driven interpretability framework validates standard clinical markers while revealing the comparable diagnostic weight of underutilized features, such as mandibular dysmorphology. Ultimately, this approach provides a scalable, transparent, and high-performance solution for equitable prenatal diagnostics and phenotypic biomarker discovery.