Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces.
Authors
Affiliations (4)
Affiliations (4)
- Department of Applied Mathematics, Technical Medical Centre, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands. Electronic address: [email protected].
- Department of Applied Mathematics, Technical Medical Centre, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands.
- Department of Surgery, Amsterdam University medical center, Location University of Amsterdam,, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Diseases, Amsterdam, The Netherlands.
- Department of Surgery, Amsterdam University medical center, Location University of Amsterdam,, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Department of Surgery, Amsterdam University medical center, location Vrije Universiteit Amsterdam, Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands; Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Diseases, Amsterdam, The Netherlands.
Abstract
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with fatal consequences in >80% of cases. 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.