AI 中文总结
研究旨在结合物理信息神经网络与3D血管几何学习用于脑动脉瘤检测及多模态破裂风险预测。通过基于PointNeXt的检测器识别动脉瘤，利用物理信息神经网络生成血流动力学描述符，多模态模型整合多种变量预测风险，取得良好检测及预测效果，提供了定量多模态评估策略。
AI 中文摘要
脑动脉瘤是颅内动脉的局部扩张，可能破裂并导致蛛网膜下腔出血。当前评估依赖于对成像和临床风险因素的人工解读，但将血管形状、血流相关信息和患者水平变量整合到统一的定量模型中仍具有挑战性。本研究开发了一个模块化框架，用于使用3D血管几何学习、物理信息血流动力学描述符和临床变量进行脑动脉瘤检测和破裂风险预测。基于PointNeXt的检测器首先从血管点云识别动脉瘤的存在。对于动脉瘤阳性病例，一个非定常物理信息神经网络然后在规定的Navier-Stokes残差和边界条件约束下生成几何条件压力、速度、壁面切应力（WSS）、时间平均WSS、振荡切应力指数（OSI）和相对停留时间描述符。多模态模型然后整合血管形态、物理信息血流动力学描述符和临床变量以产生破裂风险评分。动脉瘤检测器在接收器操作特征曲线（AUROC）下的合并交叉验证面积为0.959，精确召回曲线（AUPRC）下的面积为0.859。对于破裂风险预测，固定的70/30晚期融合在评估模型中表现最佳，合并AUROC为0.827，AUPRC为0.732，在Holm校正配对DeLong测试后超过所有比较模型（所有调整p<0.05）。特征分析确定OSI分布、动脉瘤位置、径向几何和TAWSS描述符是横截面破裂风险判别重要因素。这些结果共同为特定病例的动脉瘤评估提供了一种定量的多模态策略。
英文摘要
Cerebral aneurysms are localized dilations of intracranial arteries that may rupture and cause subarachnoid hemorrhage. Current assessment relies on human interpretation of imaging and clinical risk factors, but integrating vascular shape, flow-related information, and patient-level variables into a unified quantitative model remains challenging. This study develops a modular framework for cerebral aneurysm detection and rupture-risk prediction using 3D vascular geometry learning, physics-informed hemodynamic descriptors, and clinical variables. A PointNeXt-based detector first identified aneurysm presence from vascular point clouds. For aneurysm-positive cases, an unsteady physics-informed neural network then generated geometry-conditioned pressure, velocity, wall shear stress (WSS), time averaged WSS, oscillatory shear index (OSI), and relative residence time descriptors under prescribed Navier-Stokes residual and boundary-condition constraints. Multimodal models then integrated vascular morphology, physics-informed hemodynamic descriptors, and clinical variables to produce rupture-risk scores. The aneurysm detector achieved pooled out-of-fold area under the receiver operating characteristic curve (AUROC) of 0.959 and area under the precision-recall curve (AUPRC) of 0.859. For rupture-risk prediction, fixed 70/30 late fusion achieved the highest performance among evaluated models, with pooled AUROC of 0.827 and AUPRC of 0.732, exceeding all comparison models after Holm-corrected paired DeLong testing (all adjusted p < 0.05). Feature analysis identified OSI distribution, aneurysm location, radial geometry, and TAWSS descriptors as important contributors to cross-sectional rupture-risk discrimination. Together, these results provide a quantitative, multimodal strategy for case-specific aneurysm assessment.
