Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration.

ABSTRACT: Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modeling. The objectives of this study are to develop a prediction framework that first calibrates the physical AAA G&R model using patient-specific serial computed tomography (CT) scan images, predicts the expansion of an AAA in the future, and quantifies the associated uncertainty in the prediction. We adopt a Bayesian calibration method to calibrate parameters in the G&R computational model and predict the magnitude of AAA expansion. The proposed Bayesian approach can take different sources of uncertainty; therefore, it is well suited to achieve our aims in predicting the AAA expa

SUBMITTER: Zhang L 

PROVIDER: S-EPMC6890695 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature