An abdominal aortic aneurysm (AAA) is a gradual enlargement of the aorta that can cause a life-threatening event when a rupture occurs. Aneurysmal geometry has been proved to be a critical factor in determining when to surgically treat AAAs, but, it is challenging to predict the patient-specific evolution of an AAA with biomechanical or statistical models. The recent success of deep learning in biomedical engineering shows promise for predictive medicine. However, a deep learning model requires a large dataset, which limits its application to the prediction of the patient-specific AAA expansion. In order to cope with the limited medical follow-up dataset of AAAs, a novel technique combining a physical computational model with a deep learning model is introduced to predict the evolution of AAAs. First, a vascular Growth and Remodeling (G&R) computational model, which is able to capture the variations of actual patient AAA geometries, is employed to generate a limited in silico dataset. Second, the Probabilistic Collocation Method (PCM) is employed to reproduce a large in silico dataset by approximating the G&R simulation outputs. A Deep Belief Network (DBN) is then trained to provide fast predictions of patient-specific AAA expansion, using both in silico data and patients' follow-up data. Follow-up Computer Tomography (CT) scan images from 20 patients are employed to demonstrate the effectiveness and the feasibility of the proposed model. The test results show that the DBN is able to predict the enlargements of AAAs with an average relative error of 3.1%, which outperforms the classical mixed-effect model by 65%.
Bibliographical noteFunding Information:
This work has been supported in part by the National Heart, Lung, and Blood Institute of the National Institutes of Health (R01HL115185 and R21HL113857), National Science Foundation CAREER Award (CMMI-1150376), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1A4A1025986) and the Vietnam Education Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Note that this study includes the content which first appeared in the dissertation from one of the authors .
© Copyright © 2020 Jiang, Do, Choi, Lee and Baek.
All Science Journal Classification (ASJC) codes
- Materials Science (miscellaneous)
- Mathematical Physics
- Physics and Astronomy(all)
- Physical and Theoretical Chemistry