Neural Operator Learning to Predict Aneurysmal Growth and Outcomes

项目总结

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.