FADA 解决了中低收入国家超声技师短缺导致的产前筛查缺口，做医疗 AI 或边缘部署的团队可以直接在手机上跑完整流程，值得关注其开源代码和模型。
FADA 是一个基于 Qwen3.5-VL 构建的统一视觉语言模型，能够通过单一流程完成胎儿超声图像的临床解读、分类、检测和分割，无需外部标签。它通过选择性蒸馏技术从四个领域专用基础模型中提取知识，在分割任务上达到 0.8820 平均 Dice，检测 mAP@0.50 为 0.7671，解读合规率 100%。专家验证显示，在 237 张图像上，临床指导模式下 73.5% 的解读获得满分。该系统可在单张消费级 GPU 上训练，并能在搭载骁龙 7 Gen 1 的智能手机上离线运行完整流程约 60 秒，为资源受限地区的产前超声筛查提供了可行方案。
FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model
A global shortage of trained sonographers limits prenatal ultrasound screening in low- and middle-income countries, where over half of pregnant women receive no skilled sonography. Current deep learning approaches address detection, segmentation, or classification in isolation, each demanding a separate model and expert-specified labels at inference. We present FADA, a unified vision-language model built on Qwen3.5-VL that performs clinical interpretation, classification, detection, and segmentation through a single interpretation-first pipeline without external labels. FADA distills knowledge from four domain-specific foundation models (FetalCLIP, UltraSAM, USF-MAE, UltraFedFM) via offline pre-computed feature caching. Selective distillation, which applies feature alignment only to annotation tasks while interpretation relies on standard fine-tuning, consistently outperforms full distillation across most evaluation axes. The recommended variant, FADA-SKD, achieves 0.8820 mean Dice for segmentation, 0.7671 mAP@0.50 for detection, and 100% structured interpretation compliance. Expert sonographer validation across 237 images confirms clinically acceptable outputs in both autonomous and human-in-the-loop modes, with 73.5% of interpretations scoring perfectly under clinician guidance. The system is trainable on a single consumer GPU and deployable without cloud connectivity. We validate edge deployment by running the compressed 0.8B model on a commodity smartphone (Qualcomm Snapdragon 7 Gen 1, 12 GB RAM) using llama.cpp with GGUF quantization, completing the full 5-phase pipeline in approximately 60 seconds entirely offline. This establishes a practical pathway for integrating AI-assisted fetal assessment with portable ultrasound devices, directly addressing diagnostic access gaps in resource-constrained settings. Code, models, and data are available at https://github.com/mahmoodphd/FADA.