Neural Operator Learning to Predict Aneurysmal Growth and Outcomes

神经算子学习预测动脉瘤的生长和结果

• 批准号: 10636358
• 负责人: Roland Assi
• 金额: $ 70.5万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10636358
• 关键词: Acute; Adherence; Affect; Aging; Algorithms; Aneurysm; Aorta; Aortic Diseases; Arteries; Attention; Basic Science; Biomechanics; Biomedical Engineering; Blood Vessels; Body Surface Area; Caring; Cessation of life; Clinical; Clinical Data; Clinical Management; Clinical Research; Collagen; Collection; Computer Models; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Diameter; Disease; Dissection; Distal; Elastic Fiber; Emergency Situation; Event; Female; Foundations; Frequencies; Generations; Genetic; Genetic Predisposition to Disease; Goals; Grant; Growth; Guidelines; Human; Hypertension; Image; Incidence; Individual; Intervention; Knowledge; Learning; Lesion; Life; Machine Learning; Medical Genetics; Medical Imaging; Medical Surveillance; Medicine; Methods; Modeling; Morbidity - disease rate; Mus; Natural History; Neural Network Simulation; Operative Surgical Procedures; Outcome; Paper; Pathology; Patient Care; Patients; Persons; Pharmacotherapy; Phenocopy; Physicians; Physics; Postdoctoral Fellow; Predisposition; Prevalence; Prognosis; Prosthesis; Regulation; Research; Resolution; Risk; Risk Factors; Rupture; Sampling; Scientist; Shapes; Students; Surgeon; Syndrome; Techniques; Testing; Thoracic Aortic Aneurysm; Thoracic aorta; Time; Tissues; Training; Uncertainty; United States; Work; biomechanical model; biophysical model; clinical care; clinical imaging; design; disability; drug efficacy; experience; falls; generative adversarial network; human data; improved; in silico; in vivo; innovation; machine learning algorithm; machine learning prediction algorithm; male; mechanotransduction; mortality; mouse model; neural; neural network; next generation; novel; predictive modeling; premature; prophylactic; prospective; repaired; risk prediction; sex; sobriety; tool

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.

项目概要 尽管医学遗传学、医学成像和外科手术不断进步，胸主动脉 动脉瘤 (TAA) 日益成为显着的发病率和死亡率的原因。 揭示了这种疾病的复杂性，这种疾病通常在老年人中零星出现，并且无法控制 高血压是主要危险因素之一，同时也出现在具有遗传或遗传因素的年轻人中 标准方法（包括多元回归）未能改善。 危及生命的急性主动脉综合征（夹层和破裂）的预测和当前的 AHA/ACC 指南 最近的数据表明，基于最大主动脉直径无法预测风险，这使情况进一步复杂化。 虽然可以挽救生命，但用人工移植物对近端主动脉进行手术修复会增加远端动脉瘤的发生率 主动脉疾病和急性事件，因此强调需要适当安排手术时间——也就是说， 由于遵守现行指南或过早干预而造成不必要的延误可能会增加风险 患者迫切需要一种更好的方法来预测胸主动脉生长和潜在结果。 该提案意义重大，因为它旨在解决这一未满足的临床需求；它具有创新性，因为我们提出了一个 新颖的机械生物学和生物力学数据驱动方法来开发下一代（神经算子 基于）机器学习工具，可以更好地预测 TAA 的生长和某些结果，包括药物疗效。 我们将结合现有鼠类和人类数据的新颖再利用，生成约 25000 个新的合成数据 集和独特的新小鼠数据（12 个 TAA 模型）的收集，以确定最佳的机器学习 方法，然后结合现有和前瞻性临床影像数据（约 300 名患者）来训练和测试最终结果 神经网络（深度算子神经网络，或 DeepONet）。 使用标准神经网络根本不可能做到这一点，我们将利用多保真度训练，使两者都降低。 与高保真真实或相对不准确的模型相结合时，可以在训练中使用分辨率数据和相对不准确的模型 通过功能先验（信息最丰富的贝叶斯先验）进行合成数据和不确定性量化 通过结合历史数据、生物物理模型和 GAN（生成对抗网络）来学习。 独特的组合使我们能够用很少的样本（例如，2或3个新的医学图像）来学习后验，因此 鉴于我们的经验，可以用最少的（临床）信息对新病例进行预测。 由医师科学家、生物工程师和应用数学家组成的高度协作的团队，拥有强大的跟踪能力 成功研究（资助、论文）以及对不同学生、博士后和住院医师的培训的记录。