发表机构
Philips Ultrasound（飞利浦超声）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ZAGNet利用图神经网络建模肺部超声区域间关系，处理缺失区域，在714名受试者数据集上，实变AUC达0.803，胸腔积液AUC达0.893，较池化方法提升显著。
AI 中文摘要
患者级肺部超声（LUS）诊断需要整合跨多个解剖区域采集的发现，然而临床检查常涉及可变且不完整的扫描方案，存在区域缺失。现有诊断AI方法主要分析单个帧或视频循环，依赖最大池化或平均池化等启发式聚合策略，忽略了患者级推断中的区域间关系。本文提出ZAGNet，一种区域感知图神经网络，将时间跟踪的病理发现表示为图节点，通过解剖区域邻接关系连接。图变换器网络在相邻肺区域间传播上下文信息，同时虚拟全局节点聚合图级特征，仅使用患者级监督即可预测患者级实变和胸腔积液。ZAGNet通过在图结构上计算，无需固定输入格式或大小，即可适应区域缺失。我们在一个多中心数据集上评估ZAGNet，该数据集包含714名受试者（20,256个LUS视频循环），检查覆盖前、后和侧胸区域，区域数从4到16不等。对于实变诊断，ZAGNet的AUC为0.803，而最大池化为0.677，平均池化为0.674。对于胸腔积液，AUC从0.804（最大池化）和0.815（平均池化）提升至0.893。这分别代表了实变和胸腔积液高达19%和11%的改进。结果表明，基于图的区域间推理为自动化患者级LUS评估提供了有效且临床一致的框架。
英文摘要
Patient-level lung ultrasound (LUS) diagnosis requires integrating findings acquired across multiple anatomical zones, yet clinical examinations frequently involve variable and incomplete scanning protocols with missing zones. Existing diagnostic AI methods primarily analyze individual frames or video loops, relying on heuristic aggregation strategies such as max or mean pooling that ignore inter-zone relationships for patient-level inference. This paper presents ZAGNet, a Zone- Aware Graph Neural Network that represents temporally tracked pathology findings as graph nodes connected by anatomical zone adjacency. A graph transformer network propagates contextual information across neighboring lung regions, while a virtual global node aggregates graph-level features to predict patientlevel consolidation and pleural effusion using only patient-level supervision. ZAGNet accommodates missing zones by computing on a graph structure without fixed input format or size. We evaluate ZAGNet on a multicenter dataset of 714 subjects (20,256 LUS video loops) with exams varying from 4 to 16 zones across anterior, posterior, and lateral thoracic regions. For consolidation diagnosis, ZAGNet achieved an AUC of 0.803 compared to 0.677 (max pooling) and 0.674 (mean pooling). For pleural effusion, AUC increased to 0.893 from 0.804 (max pooling) and 0.815 (mean pooling). These represent improvements of up to 19% and 11% for consolidation and pleural effusion respectively. The results demonstrate that graph-based inter-zone reasoning provides an effective and clinically consistent framework for automated patient-level LUS assessment.
CommentsThe 2026 IEEE International Ultrasonics Symposium (IUS)
