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Forecasting Patient-Specific Abdominal Aortic Aneurysm Geometry with Mixed-Effects Models
Journal article   Open access   Peer reviewed

Forecasting Patient-Specific Abdominal Aortic Aneurysm Geometry with Mixed-Effects Models

Juan C. Restrepo, Maria L. Bolanos, Seungik Baek, Satish C. Muluk, Mark K. Eskandari, Vikram S. Kashyap, Eanas Yassa and Ender A. Finol
Diagnostics (Basel), v 16(9), 1409
06 May 2026
DOI: https://doi.org/10.3390/diagnostics16091409
PMID: 42122111
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url
https://doi.org/10.3390/diagnostics16091409View
Published, Version of Record (VoR) Open

Abstract

abdominal aortic aneurysms geometry forecasting mixed-effects contrast-enhanced CTA patient-specific modeling aneurysm remodeling heterogeneous remodeling 3D reconstruction
Background/Objectives: Abdominal aortic aneurysm (AAA) surveillance is based largely on monitoring the maximum diameter, a single scalar metric that obscures regional remodeling and offers limited information on the location and time dependency of the growth rate. The present work addresses this limitation with a geometry-based patient-specific framework that learns local, linear evolution from longitudinal clinical imaging, yielding 3D forecasts of AAA geometry at arbitrary future times. Methods: Lumen and outer wall surfaces are represented on a centerline-anchored cylindrical grid, with subsequent implementation of individualized linear mixed-effects models. The model is explicitly interpretable as the fixed effects predict global trends and the random effects represent regional heterogeneity. In a multicenter cohort of 79 patients, we evaluated forecasts using spatial similarity (with the 95th percentile of the Hausdorff distance—HD95) and clinically relevant global geometric scalars such as maximum diameter and volume. Results: When forecasting a future AAA geometry, the model achieved sub-millimetric HD95 spatial errors and less than 6% error for the aforementioned global scalars. The model was deployed in an interactive application named the Aneurysm Forecasting Studio, which allows a user to visualize the AAA in an explorable forecast space. Conclusions: During typical clinical surveillance intervals, AAA geometric remodeling is reasonably approximated as locally linear in time, enabling transparent, fast forecasts that support surveillance optimization, threshold timing, and digital twin-based interventional planning.

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