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

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.