Machine learning-based biomechanical analysis for thoracic aortic aneurysm rupture risk assessment

基于机器学习的生物力学分析胸主动脉瘤破裂风险评估

基本信息

批准号：
10365444
负责人：
Liang Liang
金额：
$ 60.47万
依托单位：
UNIVERSITY OF MIAMI CORAL GABLES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-12-03 至 2026-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10365444
关键词：
3-Dimensional Adult Aneurysm Aorta Aortic Aneurysm Biomechanics Caliber Calibration Cardiovascular system Cause of Death Cessation of life Characteristics Chest Classification Clinical Clinical Trials Computer Models Computer software Consumption Data Data Files Data Set Death Rate Development Devices Disease Dissection Evaluation Studies Event Face Finite Element Analysis Follow-Up Studies General Population Geometry Goals Healthcare Systems Human Image Learning Machine Learning Magic Measures Medical Records Modeling Natural Language Processing Noise Operative Surgical Procedures Output Patients Performance Population Prevalence Probability Process Risk Risk Assessment Rupture Ruptured thoracic aortic aneurysm Shapes Source Stress Symptoms Techniques Testing Thoracic Aortic Aneurysm Thoracic aorta Time Tissue Sample Uncertainty Validation Visualization X-Ray Computed Tomography base biomechanical test clinical application cost deep neural network density effectiveness testing high risk improved in vivo indexing individual patient innovation insight machine learning model method development mixed reality novel open source prevent programs real time model reconstruction repaired speech recognition

项目摘要

PROJECT SUMMARY Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm (TAA) is a leading cause of death in adults. The progression of TAA is a silent process, yet rupture/dissection can occur suddenly, which often causes death. The deadly events are preventable by elective surgical repair, and the current criterion for surgical intervention states that surgery should be performed when TAA maximum diameter reaches 5 to 5.5 cm. However, this criterion cannot assess the risk of smaller TAAs (diameter≤5cm). It is estimated that there are millions of TAA patients in the U.S. with smaller TAAs, and these patients are unfortunately ignored by the current criterion. Thus, in this project, we propose an innovative approach of integrating machine learning (ML) and computational biomechanics for risk assessment of smaller TAAs. To achieve this goal, we will develop (1) ML models for automated thoracic aorta geometry reconstruction from 3D clinical CT images, which will enable a fast and streamlined analysis of TAA risk, (2) ML models for realtime TAA stress analysis, and (3) a probabilistic risk index that fuses the measured and computed patient characteristics (e.g. geometry, stress, material strength, etc) and takes into account uncertainties from different sources. The proposed approach will be developed and validated on an existing dataset of 1000 patients and a new dataset to be assembled from a longitudinal follow-up study of 600 patients, which will be the first large-scale study of machine learning-based biomechanical analysis for TAA risk assessment. This study will lead to a breakthrough in the fields of cardiovascular computational modeling and applied machine learning, provide new insights on how to better assess TAA risk, and reduce death by the silent and sudden killer of TAA disease.

项目概要主动脉瘤疾病一直位居美国前 20 位死因之列。胸主动脉瘤（TAA）是成人死亡的主要原因。 TAA 的形成是一个无声的过程，但破裂/剥离可能会突然发生，这通常会导致死亡。这些致命事件可以通过选择性手术修复来预防，目前的手术标准干预指出，当 TAA 最大直径达到 5 至 5 时，应进行手术。 5.5 cm，但该标准不能评估较小TAA（直径≤5cm）的风险。据估计，美国有数百万 TAA 患者的 TAA 较小，这些患者不幸的是，当前的标准忽略了这一点，因此，在这个项目中，我们提出了一种创新。集成机器学习 (ML) 和计算生物力学的风险方法为了实现这一目标，我们将开发 (1) 自动化机器学习模型。根据 3D 临床 CT 图像重建胸主动脉几何形状，这将实现快速、准确的胸主动脉几何重建简化 TAA 风险分析，(2) 用于实时 TAA 压力分析的 ML 模型，以及 (3) 融合测量和计算的患者特征的概率风险指数（例如几何形状、应力、材料强度等）并考虑不同的不确定性所提出的方法将在 1000 个现有数据集上进行开发和验证。患者和根据 600 名患者的纵向随访研究收集的新数据集，这将是第一个基于机器学习的 TAA 生物力学分析的大规模研究这项研究将在心血管领域带来突破。计算建模和应用机器学习，为如何更好地提供新的见解评估 TAA 风险，并减少 TAA 疾病这一无声而突然的杀手造成的死亡。