Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces

2025-06-10

AI Summary

Ruptured abdominal aortic aneurysm (AAA) carries high mortality; current surveillance—based solely on maximum diameter thresholds (≥55 mm in men, ≥50 mm in women)—ignores 3D morphological heterogeneity, yielding coarse risk stratification and non-personalized follow-up intervals. To address this, we propose the first method for direct local growth prediction on the native 3D vascular surface. Our approach employs an SE(3)-equivariant Transformer architecture that jointly encodes multi-physics features and irregular longitudinal CT angiography (CTA) data, enabling geometry-preserving and anatomically faithful growth modeling without parametric surface distortion. In internal validation, median diameter prediction error was 1.18 mm, and accuracy for surgical indication classification within two years reached 93%. External validation confirmed robust generalizability across independent cohorts. This work establishes a clinically deployable paradigm for precision AAA monitoring grounded in intrinsic 3D geometry and longitudinal dynamics.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20%. Current clinical guidelines recommend elective surgical repair when the maximum AAA diameter exceeds 55 mm in men or 50 mm in women. Patients that do not meet these criteria are periodically monitored, with surveillance intervals based on the maximum AAA diameter. However, this diameter does not take into account the complex relation between the 3D AAA shape and its growth, making standardized intervals potentially unfit. Personalized AAA growth predictions could improve monitoring strategies. We propose to use an SE(3)-symmetric transformer model to predict AAA growth directly on the vascular model surface enriched with local, multi-physical features. In contrast to other works which have parameterized the AAA shape, this representation preserves the vascular surface's anatomical structure and geometric fidelity. We train our model using a longitudinal dataset of 113 computed tomography angiography (CTA) scans of 24 AAA patients at irregularly sampled intervals. After training, our model predicts AAA growth to the next scan moment with a median diameter error of 1.18 mm. We further demonstrate our model's utility to identify whether a patient will become eligible for elective repair within two years (acc = 0.93). Finally, we evaluate our model's generalization on an external validation set consisting of 25 CTAs from 7 AAA patients from a different hospital. Our results show that local directional AAA growth prediction from the vascular surface is feasible and may contribute to personalized surveillance strategies.

Problem

• Predict local growth of abdominal aortic aneurysms (AAAs) using geometric deep learning.
• Improve AAA monitoring by considering 3D shape and multi-physical features.
• Develop personalized surveillance strategies to enhance clinical decision-making.

Innovation

• SE(3)-symmetric transformer model for AAA growth
• Local multi-physical features on vascular surface
• Geometric deep learning preserves anatomical structure