Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Journal:
arXiv
Published Date:
Jun 10, 2025
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.
