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Related Experiment Video

Updated: Oct 4, 2025

Quantitative Micro-CT Analysis of Aortopathy in a Mouse Model of β-aminopropionitrile-induced Aortic Aneurysm and Dissection

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Published on: July 16, 2018

Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator-regression neural

Minglang Yin^1,2, Ehsan Ban³, Bruno V Rego³

¹Center for Biomedical Engineering, Brown University, Providence, RI 02912, USA.

Journal of the Royal Society, Interface

|February 9, 2022

Summary

A new AI model, DeepONet, accurately predicts aortic dissection progression based on the distribution of connective tissue struts. This advances understanding of how microstructural variations influence aortic wall mechanics and dissection risk.

Keywords:

DeepONet aortic dissection damage mechanics neural networks operator regression phase field finite elements soft tissue

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Area of Science:

Biomedical Engineering
Computational Mechanics
Materials Science

Background:

Aortic dissection involves medial layer delamination, influenced by the aorta's microstructure.
Spatial variations in interlamellar struts affect dissection propensity and mechanical behavior.

Purpose of the Study:

To develop a data-driven surrogate model for aortic dissection progression.
To predict the mechanical consequences of varying strut distributions using artificial intelligence.

Main Methods:

Utilized DeepONet, an operator-regression neural network, for modeling.
Generated in silico data via phase-field finite-element modeling of fluid injection and delamination.
Trained the surrogate model on diverse strut distributions.

Main Results:

DeepONet accurately predicted pressure-volume curves and damage progression for various strut configurations.
The neural network effectively captured the relationship between microstructure and mechanical properties.
Demonstrated the model's capability to handle diverse histological microstructures.

Conclusions:

DeepONet provides an effective tool for analyzing aortic dissection mechanics.
The model can quantify biological variability and improve inverse design in biomechanics.
Facilitates prediction of mechanical properties from experimental data.