Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Medical Imaging

Background:

• Accurate prediction of abdominal aortic aneurysm (AAA) growth is vital for effective patient management and surgical planning.
• Understanding the complex interplay of hemodynamic factors, intraluminal thrombus (ILT) accumulation, and morphological changes is key to improving AAA growth prediction.
• High inter-patient variability in AAA growth mechanisms necessitates advanced predictive models.

Purpose of the Study:

• To develop and validate a deep learning approach, specifically a patch-based convolutional neural network (CNN), for enhanced AAA growth prediction.
• To integrate physics-based knowledge and multiphysical features into a CNN architecture to capture spatio-temporal dynamics of AAA pathogenesis.
• To evaluate the impact of incorporating features like radius, ILT thickness, and wall shear stress on AAA growth prediction accuracy.

Main Methods:

• A novel parameterization method was developed to enable the use of unstructured multiphysical features within a kernel-based CNN.
• The proposed CNN architecture was trained and tested using data from 54 AAA patients, incorporating features such as radius, ILT thickness, and time-average wall shear stress.
• A 5-fold cross-validation strategy was employed to assess the model's performance using root mean squared error (RMSE) and relative error (RE).

Main Results:

• The study demonstrated the significant effect of leveraging multiphysical features for improved AAA growth prediction.
• The proposed architecture showed superior performance compared to existing state-of-the-art methods in predicting AAA growth.
• Experiments confirmed the model's ability to capture complex spatio-temporal relationships crucial for understanding vascular adaptation in AAA.

Conclusions:

• Integrating physics-based knowledge with deep learning, particularly through multiphysical feature parameterization, significantly enhances AAA growth prediction.
• The developed patch-based CNN approach offers a promising tool for more accurate and reliable prediction of abdominal aortic aneurysm progression.
• This work advances the understanding of AAA pathogenesis and provides a foundation for improved clinical decision-making in AAA management.