胸部X射线设备推理的临床知识图谱
Clinical Knowledge Graphs for Chest X-Ray Device Reasoning
- Sliced Health
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种不确定性感知的临床知识图谱，将胸部X射线设备实例、尖端估计、放置评估及来源信息等作为关联证据，在RANZCR CLiP大规模测试集上验证，实现了保留完整证据的AI推理基础。
AI中文摘要：
胸部X光片通常用于验证导管、管路及其他支持设备的位置。现有的图像模型往往返回标签或分割结果，而报告处理系统则在不访问图像几何信息的情况下对文本进行结构化处理。我们提出了一种具有不确定性意识的临床知识图谱，将设备实例、尖端估计、放置评估、来源信息、报告事件和时间链接表示为独立但相互关联的证据。我们使用完整的RANZCR CLiP测试档案中的保存预测来评估所实现的视觉图谱层，该档案包含来自3,255名患者的30,083项研究，跨越五个不重叠的外部折。图谱构建器生成了914,632个B7证据节点和884,549个类型化关系。所有118,647个B7预测设备节点保留了尖端协方差、放置概率、片段来源和片段计数，而直接的B2基线则未保留这些字段中的任何一个。我们进一步定义了类型化数据契约、不确定性表示、弃权（不执行）规则、报告-图像对齐以及纵向查询机制，以将图谱扩展到包含报告的队列。所报告的图谱物化分析是事后描述性的，并未确立报告对齐、纵向性能或临床实用性。它展示了一个可复现的基础，用于对胸部X射线设备评估进行保留证据的AI推理。
英文摘要：
Chest radiographs are routinely used to verify the position of catheters, tubes, and other support devices. Existing image models often return labels or segmentations, while report-processing systems structure text without access to image geometry. We present an uncertainty-aware clinical knowledge graph that represents device instances, tip estimates, placement assessments, provenance, report events, and temporal links as separate but connected evidence. We evaluate the implemented visual graph layer using saved predictions from the complete RANZCR CLiP test archive, comprising 30,083 studies from 3,255 patients across five non-overlapping outer folds. The graph builder materializes 914,632 B7 evidence nodes and 884,549 typed relationships. All 118,647 B7 predicted-device nodes retain tip covariance, placement probabilities, fragment provenance, and fragment counts, whereas the direct B2 baseline retains none of these fields. We further define typed data contracts, uncertainty representations, abstention rules, report-image grounding, and longitudinal query mechanisms for extending the graph to report-bearing cohorts. The reported graph-materialization analysis is post-hoc descriptive and does not establish report grounding, longitudinal performance, or clinical utility. It demonstrates a reproducible foundation for evidence-preserving AI reasoning over chest X-ray device assessments.
