Numerical and Probabilistic Methods for Modeling of Abdominal Aortic Aneurysm Rupturing Risk

Authors

  • Dr. Nawal Siddig

DOI:

https://doi.org/10.17762/msea.v71i2.96

Abstract

This study concerns to apply numerical methods and derives a comparative Abdominal Aortic Aneurysm (AAA). An accuracy comparison has been made using the index of rupturing risk versus one computed index. The study examines some factors such as age, Mean Arterial Pressure (MAP), stress-strain via the arterial wall stress (?), wall strength(S), and wall thickness (t). The qualms of AAA wall thickness are the most prevailing endogenous factors, as well as the wall stress and wall strength. A comparison between the Probabilistic Wall Rupture Index (PWRI), diameter-based index, and the output of the numerical method was done considering prediction power. Data records were taken from patients who underwent CT-Scan and ultrasound. Ten patients without rupturing cases were included in the statistical assessments. A matched diameter of a ruptured sample (n=7) and an unruptured sample (n=7) AAA was then considered. All the uncertainties that were observed in the thickness of the AAA wall and its strength were considered based originally on Laplace modified equation and PRRI analysis. The criteria for fitting a straight line to the data points were used for data graphing regarding the random nature of the collected data points.  Statistical analysis (and plotting) of data was performed using MinitabTM, SPSSTM, and MatlabTM. The collagen-to- elastin ratio was taken as a measure of the AAA wall stiffness and tensile strength. The obtained results show that modified equations give a considerable coincide and minimum error compared with the original equation, and PRRI indicated a strong sensitivity more than the numerical models.

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Published

2022-04-25

How to Cite

Siddig, D. N. . (2022). Numerical and Probabilistic Methods for Modeling of Abdominal Aortic Aneurysm Rupturing Risk. Mathematical Statistician and Engineering Applications, 71(2), 340–354. https://doi.org/10.17762/msea.v71i2.96

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Articles