Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links TAA development to dysfunctions in the aortic mechanotransduction axis, including loss of elastic fiber integrity and cell-matrix connections. Because distinct insults create different mechanical vulnerabilities, there is a critical need to identify interacting factors that drive progression. Here, we use a finite element framework to generate synthetic TAAs from hundreds of heterogeneous insults spanning varying degrees of elastic fiber damage and impaired mechanosensing. From these simulations, we construct spatial maps of localized dilatation and distensibility to train neural networks that predict the initiating combined insult. We compare several architectures (Deep Operator Networks, UNets, and Laplace Neural Operators) and multiple input data formats to define a standard for future subject-specific modeling. We also quantify predictive performance when networks are trained using only geometric data (dilatation) versus both geometric and mechanical data (dilatation plus distensibility). Across all networks, prediction errors are significantly higher when trained on dilatation alone, underscoring the added value of distensibility information. Among the tested models, UNet consistently provides the highest accuracy across all data formats. These findings highlight the importance of acquiring full-field measurements of both dilatation and distensibility in TAA assessment to reveal the mechanobiological drivers of disease and support the development of personalized treatment strategies.

翻译：胸主动脉瘤（TAAs）源于主动脉壁多种机械及力学生物学紊乱，这些紊乱增加了夹层或破裂的风险。有证据表明TAA的发生与主动脉力传导轴功能障碍相关，包括弹性纤维完整性丧失和细胞-基质连接破坏。由于不同的损伤会造成不同的机械脆弱性，因此亟需识别驱动疾病进展的相互作用因素。本文采用有限元框架，通过数百种涵盖不同程度弹性纤维损伤和机械感受功能受损的异质性损伤，生成合成TAA模型。基于这些模拟数据，我们构建了局部扩张性与可扩张性的空间分布图，用以训练预测初始复合损伤的神经网络。我们比较了多种架构（Deep Operator Networks、UNets和Laplace Neural Operators）及多类输入数据格式，旨在为未来个体特异性建模建立标准。同时，我们量化了网络仅使用几何数据（扩张性）与联合使用几何和机械数据（扩张性加可扩张性）时的预测性能。在所有网络中，仅使用扩张性数据训练时预测误差显著更高，凸显了可扩张性信息的附加价值。在测试模型中，UNet在所有数据格式下均保持最高预测精度。这些发现强调了在TAA评估中获取扩张性与可扩张性全场测量数据的重要性，这有助于揭示疾病的力学生物学驱动机制，并为个性化治疗策略的制定提供支持。