用AI构建肺动脉数字孪生，自动提取肺栓塞关键生物标志物。
A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers
- 基于CT图像生成肺动脉树的有向图数字孪生，自动标注层级结构。
- 可计算局部（血管形态、阻塞率）与全局（栓塞体积分布、评分）生物标志物。
- 适合急诊快速评估血栓负荷，替代人工耗时评分和不可靠血液检测。
肺栓塞是导致急性心血管综合征的主要原因之一，临床诊断依赖于计算机断层扫描肺动脉造影后的风险分层，将30天死亡风险分为三类。该分层通常基于右心室与左心室直径比值及两种心脏酶的血清水平。然而，在急诊环境中，血液生物标志物并非总能获取，且手动计算如Qanadli和Mastora等既定严重程度评分耗时，极少在常规临床实践中应用。本研究提出一种自动化流程，对肺动脉树建模为有向图，标注其层级结构并表征肺栓塞状态。该流程通过人工智能生成的二值掩码（包括血管、血栓、肺组织和肺叶），构建患者特异性的肺动脉数字孪生。流程可提取图像衍生生物标志物，包括局部动脉级特征（形态学信息、层级位置、血栓体积、阻塞程度）以及全局患者级指标（自动计算的Qanadli和Mastora评分，按肺叶与层级划分的总栓塞体积分布）。通过与现有流程、解剖学预期及人工评分对比验证，表明该管道能够自动生成解剖上准确的数字孪生与严重程度评分，具有高度一致性。这支持了这些图像衍生生物标志物在自动提供血栓负荷与空间分布精确信息方面的潜力。
原文摘要 · Abstract (English)
Pulmonary embolism, the obstruction of a pulmonary artery by a blood clot, is one of the leading causes of acute cardiovascular syndrome. In clinical practice, therapeutic decisions after diagnosis via computed tomography pulmonary angiography rely on risk stratification, which categorizes 30-day mortality risk into three categories. This stratification depends on the right-to-left ventricular diameter ratio and blood levels of two cardiac enzymes. However, blood biomarkers are not always available in emergency settings, and manual calculation of established severity scores - such as Qanadli and Mastora - is time-consuming and rarely performed in clinical routine practice. This study introduces an automated pipeline that models a directed graph representation of the pulmonary arterial tree, labeling its hierarchical structure and characterizing pulmonary embolism. The pipeline derives image-based biomarkers, including local artery-level features (morphological information, hierarchical position, clot volume, and resulting obstruction) and global patient-level biomarkers such as automatically calculated severity scores (Qanadli and Mastora) and the total embolic volume distribution by lobes and hierarchical levels. Using artificial-intelligence-generated binary masks of arteries, emboli, lungs, and lobes, it creates a patient digital twin of the arterial structure. Validation of the pipeline through comparison to an existing pipeline, anatomical expectations, and manual severity score calculations demonstrates the pipeline's ability to automatically generate anatomically accurate digital twins and severity scores with strong agreement. This supports the potential of these image-derived biomarkers to automatically provide rapid, precise information on thrombotic burden and spatial clot distribution.
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