发表机构
University of Nebraska Medical Center（内布拉斯加大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出两阶段多示例学习框架，通过自监督预训练和心脏帧识别实现全超声检查级别的先天性心脏病筛查，在内部测试集上AUC达0.985，外部数据经无标签适应后AUC从0.513提升至0.944。
AI 中文摘要
先天性心脏病（CHD）是最常见的出生缺陷，然而很大一部分病例在产前超声检查中仍未被发现，部分原因是当前的人工智能方法假设关键诊断帧已由临床医生或视图分类器从检查中分离出来。我们去除这一假设，直接在整次超声检查的层面处理CHD筛查。我们提出一个两阶段框架：首先通过自监督掩码自编码器在未标记的胎儿超声数据上进行预训练，学习可迁移的帧表示；然后使用疾病鲁棒模块识别心脏帧，并通过基于Transformer的多示例学习（MIL）模型聚合这些帧，该模型仅依据检查级别的标签即可生成病例级别的诊断。该模型进一步返回其得分最高的帧供临床医生审查，并通过一个层次化分类头区分危重与非危重CHD。在我们多源开发队列（FUSE）的内部测试集上，所提出的心脏门控MIL模型达到了0.985的曲线下面积（AUC）和0.990的特异性，优于复现的NATMED集成（AUC 0.861，特异性0.600）和FetalCLIP基础模型（AUC 0.867，特异性0.710）。在独立的外部队列上，所有模型最初的表现接近随机水平，但无标签的CORAL适应将所提出模型的AUC从0.513提升至0.944，而全检查和视图依赖的基线方法则无法恢复。这些结果表明，结合疾病鲁棒心脏帧识别的全检查MIL是实现产前CHD筛查准确且可部署的途径。
英文摘要
Congenital heart disease (CHD) is the most common birth defect, yet a large fraction of cases remain undetected on prenatal ultrasound, in part because current artificial-intelligence methods assume that the key diagnostic frames have already been isolated from a study, by a clinician or by a view classifier. We remove that assumption and address CHD screening directly at the level of the whole ultrasound study. We propose a two-stage framework that first learns transferable frame representations by self-supervised masked-autoencoder pre-training on unlabeled fetal ultrasound, then identifies cardiac frames with a disease-robust module and aggregates them with a transformer-based multiple instance learning (MIL) model that produces a case-level diagnosis from study-level labels alone. The model further returns its highest-scoring frames for clinician review, and a hierarchical head separates critical from non-critical CHD. On the internal test set of our multi-source development cohort (FUSE), the proposed cardiac-gated MIL model reaches an area under the curve (AUC) of 0.985 with a specificity of 0.990, outperforming the reproduced NATMED ensemble (AUC 0.861, specificity 0.600) and the FetalCLIP foundation model (AUC 0.867, specificity 0.710). On an independent external cohort, all models initially perform near chance, but label-free CORAL adaptation raises the proposed model from an AUC of 0.513 to 0.944, whereas whole-study and view-dependent baselines do not recover. These results indicate that whole-study MIL with disease-robust cardiac-frame identification is an accurate and deployable route to prenatal CHD screening.
