Previous studies have shown that the rupture properties of an ascending thoracic aortic aneurysm (ATAA) are strongly correlated with the pre-rupture response features. In this work, we present a two-step machine learning method to predict where the rupture is likely to occur in ATAA and what safety reserve the structure may have. The study was carried out using ATAA specimens from 15 patients who underwent surgical intervention. Through inflation test, full-field deformation data and post-rupture images were collected, from which the wall tension and surface strain distributions were computed. The tension–strain data in the pressure range of 9–18 kPa were fitted to a third-order polynomial to characterize the response properties. It is hypothesized that the region where rupture is prone to initiate is associated with a high level of tension buildup. A machine learning method is devised to predict the peak risk region. The predicted regions were found to match the actual rupture sites in 13 samples out of the total 15. In the second step, another machine learning model is utilized to predict the tissue’s rupture strength in the peak risk region. Results suggest that the ATAA rupture risk can be reasonably predicted using tension–strain response in the physiological range. This may open a pathway for evaluating the ATAA rupture propensity using information of in vivo response.

先前的研究表明，升胸主动脉瘤 (ATAA) 的破裂特性与破裂前反应特征密切相关。在这项工作中，我们提出了一种两步机器学习方法来预测 ATAA 中可能发生破裂的位置以及结构可能具有的安全储备。该研究使用来自接受手术干预的 15 名患者的 ATAA 标本进行。通过膨胀试验，采集全场变形数据和破裂后图像，计算壁面张力和表面应变分布。将 9-18 kPa 压力范围内的张力-应变数据拟合到三阶多项式以表征响应特性。据推测，容易发生破裂的区域与高水平的张力积聚有关。设计了一种机器学习方法来预测峰值风险区域。发现预测区域与总共 15 个样本中的 13 个样本中的实际破裂部位相匹配。在第二步中，使用另一个机器学习模型来预测组织在峰值风险区域的破裂强度。结果表明，可以使用生理范围内的张力-应变反应合理地预测 ATAA 破裂风险。这可能为使用以下信息评估 ATAA 破裂倾向开辟一条途径 另一个机器学习模型用于预测峰值风险区域中组织的破裂强度。结果表明，可以使用生理范围内的张力-应变反应合理地预测 ATAA 破裂风险。这可能为使用以下信息评估 ATAA 破裂倾向开辟一条途径 另一个机器学习模型用于预测峰值风险区域中组织的破裂强度。结果表明，可以使用生理范围内的张力-应变反应合理地预测 ATAA 破裂风险。这可能为使用以下信息评估 ATAA 破裂倾向开辟一条途径体内反应。