Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm

Chung, Timothy K.; Liang, Nathan L.; Vorp, David A.

doi:10.1016/j.apples.2022.100104

Cited by 4 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DICOM data was performed using a semi-automated approach with a U-NET convolutional neural network and custom MATLAB code (Mathworks, Natwick, MA, USA) [ 18 , 19 ] ( Figure 1 ). The U-NET was trained using Amazon Web Service’s (Amazon, Seattle, WA, USA) Elastic Compute Cloud and local workstations using multiple graphics processing units [ 20 , 21 ]. All image sets were input into the U-NET and were manually verified.…”

Section: Methodsmentioning

confidence: 99%

An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms

Chung

Gueldner²,

Kickliter³

et al. 2022

Bioengineering

Self Cite

View full text Add to dashboard Cite

(1) Abdominal aortic aneurysm (AAA) biomechanics-based metrics often reported may be over/under-estimated by including non-aneurysmal regions in the analyses, which is typical, rather than isolating the dilated sac region. We demonstrate the utility of a novel sac-isolation algorithm by comparing peak/mean wall stress (PWS, MWS), with/without sac isolation, for AAA that were categorized as stable or unstable in 245 patient CT image sets. (2) 245 patient computed tomography images were collected, segmented, meshed, and had subsequent finite element analysis performed in preparation of our novel sac isolation technique. Sac isolation was initiated by rotating 3D surfaces incrementally, extracting 2D projections, curve fitting a Fourier series, and taking the local extrema as superior/inferior boundaries for the aneurysmal sac. The PWS/MWS were compared pairwise using the entire aneurysm and the isolated sac alone. (3) MWS, not PWS, was significantly different between the sac alone and the entire aneurysm. We found no statistically significant difference in wall stress measures between stable (n = 222) and unstable (n = 23) groups using the entire aneurysm. However, using sac-isolation, PWS (24.6 ± 7.06 vs. 20.5 ± 8.04 N/cm2; p = 0.003) and MWS (12.0 ± 3.63 vs. 10.5 ± 4.11 N/cm2; p = 0.022) were both significantly higher in unstable vs. stable groups. (4) Our results suggest that evaluating only the AAA sac can influence wall stress metrics and may reveal differences in stable and unstable groups of aneurysms that may not otherwise be detected when the entire aneurysm is used.

show abstract

Section: Methodsmentioning

confidence: 99%

An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms

Chung

Gueldner²,

Kickliter³

et al. 2022

Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Despite promising achievements, the reliability of these indicators depends on the accuracy of the computational biomechanical simulations used to calculate them, which are closely linked to the modelling framework. Significant advances have been made over early models based on Laplace's law, including the use of advanced constitutive laws, three-dimensional patient-specific geometry and fluid-structure interaction simulations [22][23][24][25][26][27]. Nevertheless, the complex pathogenesis of the disease requires a more thorough examination of the mechanical behaviour of the aortic wall.…”

Section: Introductionmentioning

confidence: 99%

A two-scale numerical study on the mechanobiology of abdominal aortic aneurysms

Dalbosco,

Terzano,

Carniel

et al. 2023

J. R. Soc. Interface.

View full text Add to dashboard Cite

Abdominal aortic aneurysms (AAAs) are a serious condition whose pathophysiology is related to phenomena occurring at different length scales. To gain a better understanding of the disease, this work presents a multi-scale computational study that correlates AAA progression with microstructural and mechanical alterations in the tissue. Macro-scale geometries of a healthy aorta and idealized aneurysms with increasing diameter are developed on the basis of existing experimental data and subjected to physiological boundary conditions. Subsequently, microscopic representative volume elements of the abluminal side of each macro-model are employed to analyse the local kinematics at the cellular scale. The results suggest that the formation of the aneurysm disrupts the micromechanics of healthy tissue, which could trigger collagen growth and remodelling by mechanosensing cells. The resulting changes to the macro-mechanics and microstructure of the tissue seem to establish a new homeostatic state at the cellular scale, at least for the diameter range investigated.

show abstract

Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis

Troka,

Wojnicz,

Szepietowska

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm

Cited by 4 publications

References 39 publications

An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms

An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms

A two-scale numerical study on the mechanobiology of abdominal aortic aneurysms

Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis

Contact Info

Product

Resources

About