Probabilistic noninvasive prediction of wall properties of abdominal aortic aneurysms using Bayesian regression.
Multiple patient-specific parameters, such as wall thickness, wall strength, and constitutive properties, are required for the computational assessment of abdominal aortic aneurysm (AAA) rupture risk. Unfortunately, many of these quantities are not easily accessible and could only be determined by invasive procedures, rendering a computational rupture risk assessment obsolete. This study investigates two different approaches to predict these quantities using regression models in combination with a multitude of noninvasively accessible, explanatory variables. We have gathered a large dataset comprising tensile tests performed with AAA specimens and supplementary patient information based on blood analysis, the patients medical history, and geometric features of the AAAs. Using this unique database, we harness the capability of state-of-the-art Bayesian regression techniques to infer probabilistic models for multiple quantities of interest. After a brief presentation of our experimental results, we show that we can effectively reduce the predictive uncertainty in the assessment of several patient-specific parameters, most importantly in thickness and failure strength of the AAA wall. Thereby, the more elaborate Bayesian regression approach based on Gaussian processes consistently outperforms standard linear regression. Moreover, our study contains a comparison to a previously proposed model for the wall strength.
Institute for Computational Mechanics, Technische Universität München, Boltzmannstr. 15, 85747, Garching b. München, Germany.
Jonas
J
Biehler
Mechanics & High Performance Computing Group, Technische Universität München, Parkring 35, D-85748, Garching b. München, Germany.
Sebastian
S
Kehl
Mechanics & High Performance Computing Group, Technische Universität München, Parkring 35, D-85748, Garching b. München, Germany.
Michael W
MW
Gee
Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
Fadwa
F
Schmies
Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
Jaroslav
J
Pelisek
Institute for Computational Mechanics, Technische Universität München, Boltzmannstr. 15, 85747, Garching b. München, Germany.
Andreas
A
Maier
Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
Christian
C
Reeps
Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
Hans-Henning
HH
Eckstein
Institute for Computational Mechanics, Technische Universität München, Boltzmannstr. 15, 85747, Garching b. München, Germany. wall@lnm.mw.tum.de.
Wolfgang A
WA
Wall
D001012:Aorta, Abdominal / Q000473:pathology*; D017544:Aortic Aneurysm, Abdominal / Q000473:pathology*; D001499:Bayes Theorem; D006801:Humans; D008955:Models, Cardiovascular; D012044:Regression Analysis
Abdominal aortic aneurysm; Bayesian regression; Wall properties