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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.

项目总结