Neural Operator Learning to Predict Aneurysmal Growth and Outcomes

项目总结 尽管在医学遗传学、医学成像和外科干预方面不断进步，但胸主动脉 动脉瘤(TAA)越来越多地导致严重的发病率和死亡率。大型临床研究 揭示这种疾病的复杂性，这种疾病通常在年龄较大的人中零星出现，并未得到控制 高血压是关键的危险因素之一，同时也存在于有遗传或 与生俱来的性格。标准方法(包括多元回归)未能得到改善 威胁生命的急性主动脉综合征(夹层和破裂)的预测和当前AHA/ACC指南 基于最大主动脉直径不能预测风险。使情况进一步复杂化的是，最近的数据显示， 尽管使用人工血管修复近端主动脉是挽救生命的手术，但增加了远端的发生率。 主动脉疾病和急性事件，因此强调了适当选择手术时间的必要性-即 由于遵守当前指南或过早干预而造成的不必要延误可能会增加 病人。迫切需要一种更好的方法来预测胸主动脉的生长和潜在的结果。 这项建议意义重大，因为它旨在解决这一未得到满足的临床需求；它具有创新性，因为我们提出了一种 一种新的机械生物学和生物力学数据驱动方法，用于开发下一代(神经算子) 基于)机器学习工具，可以更好地预测TAA的增长和某些结果，包括药物疗效。 我们将结合现有的小鼠和人类数据的新用途，生成约25000个新的合成数据 集合和收集独特的新鼠类数据(12种型号的TAA)，以确定最佳的机器学习 方法，然后结合现有和预期的临床影像数据(约300名患者)来训练和测试最终的 神经网络(深度算子神经网络，或DeepONet)。我们提出的独特的元学习框架 这在标准神经网络中是不可能实现的。我们将利用多保真度培训，使两个低 分辨率数据和相对不准确的模型可以在结合高保真的真实或 通过函数先验(信息量最大的贝叶斯先验)来量化合成数据和不确定性 通过结合历史数据、生物物理模型和GAN(生成性对抗网络)学习。这 独特的组合允许我们用很少的样本(例如，2到3个新的医学图像)来学习后方，因此 可以利用最少的(临床)信息对新病例进行预测。这个项目是可能的，因为我们的 由医生科学家、生物工程师和应用数学家组成的高度协作的团队，具有强大的跟踪能力 不同学生、博士后和住院医生的成功研究(助学金、论文)和培训记录。

PROJECT SUMMARY Despite continuing advances in medical genetics, medical imaging, and surgical interventions, thoracic aortic aneurysms (TAAs) are increasingly responsible for significant morbidity and mortality. Large clinical studies reveal the complexity of the disease, which typically presents sporadically in older individuals, with uncontrolled hypertension amongst the key risk factors, while also presenting in younger individuals having genetic or congenital predispositions. Standard methods (including multivariate regressions) have failed to improve prediction of life-threatening acute aortic syndromes (dissection and rupture) and current AHA/ACC guidelines based on maximum aortic diameter fail to predict risk. Further complicating the situation, recent data show that, although life-saving, surgical repair of the proximal aorta with a prosthetic graft increases incidence of distal aortic disease and acute events, thus emphasizing the need to time surgery appropriately – that is, either unnecessary delays due to adherence to current guidelines or pre-mature intervention may increase risk to patients. There is a dire need for a better approach for predicting thoracic aortic growth and potential outcomes. This proposal is significant for it is designed to resolve this unmet clinical need; it is innovative for we propose a novel mechanobiological and biomechanical data-driven approach to develop a next-generation (neural operator based) machine learning tool that can better predict TAA growth and certain outcomes, including drug efficacy. We will combine a novel repurposing of extant murine and human data, generation of ~25000 new synthetic data sets, and collection of unique new murine data (12 models of TAAs) to identify the best machine learning approach, then combine extant and prospective clinical imaging data (~300 patients) to train and test the final neural network (a deep operator neural network, or DeepONet). Our proposed unique meta-learning framework is simply not possible with standard neural networks. We will exploit multi-fidelity training so that both low resolution data and relatively inaccurate models can be used in training when combined with high-fidelity real or synthetic data and uncertainty quantification via functional priors (the most informative Bayesian priors) that are learned by combining historical data, biophysical models, and GANs (generative adversarial networks). This unique combination allows us to learn posteriors with few samples (e.g., 2 or 3 new medical images), hence predictions can be made for new cases with minimal (clinical) information. This project is possible given our highly collaborative team of physician-scientists, bioengineers, and applied mathematicians having a strong track record of successful research (grants, papers) and training of diverse students, post-docs, and residents.