Prediction of Abdominal Aortic Aneurysm Expansion using Bayesian Calibration

 

Executive Summary

 

Ensuring that surgeries for Abdominal Aortic Aneurysms (AAA) are performed at the appropriate time is important to reduce the high mortality rates associated with this serious disease.  During the past decade, a computational platform called a Growth & Remodeling (G&R) model has been developed to aid in this process.  MSU researchers have invented a method for calibrating the parameters that determine the behavior of the G&R model, significantly enhancing its practical usefulness.  The novel MSU technology employs detailed information based on patient-specific calibration and provides a more accurate patient-specific indicator of AAA rupture potential to assist in making crucial medical decisions.

 

Description of Technology

 

The G&R model takes biological processes into account, modeling each of the multiple constituents within artery walls (elastin, collagen, and smooth muscle) individually. The wall stress is used as feedback in order to simulate the real biological processes that affect the development of each of these constituents, providing a more realistic model. Bayesian Calibration adjusts the G&R model to the patient by incorporating patient-specific data, including but not limited to past and current medical images, patient health history, age, and gender. Various uncertainties are also incorporated into the prediction. This results in a more accurate and robust predictor of patient risk for AAA rupture, which aids clinicians and patients in the decision making process.

 

Key Benefits

  • More accurate and robust predictor of Abdominal Aortic Aneurysm rupture potential
  • Method for calibrating the parameters that determine the behavior of the G&R model
  • Valuable asset to both clinicians and patients

 

Applications

  • Medical treatment of Abdominal Aortic Aneurysms

 

Patent Status: 

 

Copyright material

 

Licensing Rights Available:

 

Full licensing rights available

 

Inventors:

 

Justin Mrkva, Liangliang Zhang, Jongeun Choi, Chae Young Lim, Tapabrata Maiti, Seungik Baek

 

Tech ID:

 

TEC2015-0005

 

Patent Information:

For Information, Contact:

Bradley Shaw
Technology Manager
Michigan State University
shawbr@msu.edu
Keywords: