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:
- 金额:$ 60.47万
- 依托单位:
- 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:
- 关键词:3-DimensionalAdultAneurysmAortaAortic AneurysmBiomechanicsCaliberCalibrationCardiovascular systemCause of DeathCessation of lifeCharacteristicsChestClassificationClinicalClinical TrialsComputer ModelsComputer softwareConsumptionDataData FilesData SetDeath RateDevelopmentDevicesDiseaseDissectionEvaluation StudiesEventFaceFinite Element AnalysisFollow-Up StudiesGeneral PopulationGeometryGoalsHealthcare SystemsHumanImageLearningMachine LearningMagicMeasuresMedical RecordsModelingNatural Language ProcessingNoiseOperative Surgical ProceduresOutputPatientsPerformancePopulationPrevalenceProbabilityProcessRiskRisk AssessmentRuptureRuptured thoracic aortic aneurysmShapesSourceStressSymptomsTechniquesTestingThoracic Aortic AneurysmThoracic aortaTimeTissue SampleUncertaintyValidationVisualizationX-Ray Computed Tomographybasebiomechanical testclinical applicationcostdeep neural networkdensityeffectiveness testinghigh riskimprovedin vivoindexingindividual patientinnovationinsightmachine learning modelmethod developmentmixed realitynovelopen sourcepreventprogramsreal time modelreconstructionrepairedspeech 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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Liang Liang其他文献
Liang Liang的其他文献
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{{ truncateString('Liang Liang', 18)}}的其他基金
Functional circuitry and computation of the visual thalamus
视觉丘脑的功能电路和计算
- 批准号:
10577537 - 财政年份:2023
- 资助金额:
$ 60.47万 - 项目类别:
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
- 批准号:
10534234 - 财政年份:2021
- 资助金额:
$ 60.47万 - 项目类别:
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