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

Liangliang Zhang, Zhenxiang Jiang, Jongeun Choi, Chae Young Lim, Tapabrata Maiti, Seungik Baek

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 expansion process as well as in computing the propagated uncertainty. We demonstrate how to achieve the proposed aims by solving the formulated Bayesian calibration problems for cases with the synthetic G&R model output data and real medical patient-specific CT data. We compare and discuss the performance of predictions and computation time under different sampling cases of the model output data and patient data, both of which are simulated by the G&R computation. Furthermore, we apply our Bayesian calibration to real patient-specific serial CT data and validate our prediction. The accuracy and efficiency of the proposed method is promising, which appeals to computational and medical communities.

Original languageEnglish
Article number8629977
Pages (from-to)2537-2550
Number of pages14
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number6
DOIs
Publication statusPublished - 2019 Nov

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All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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