Virtual Population to Re-assess AAA Risk Using Neck Geometry and Shape Compactness Alongside Maximum Diameter

We present an automated framework to generate 1 demographically stratified virtual populations of abdominal aortic aneurysms (AAAs) and to quantify anatomy-flow relationships via an in silico observational study. Using 258 CTA-derived cases, we generated 182 validated AAA geometries and ran 364 simulations, extracting 11 geometric descriptors and six haemodynamic biomarkers. The automated constraint-aware framework blends statistically grounded sampling, anatomical plausibility and regional morphing to provide a scalable route for reproducible CFD to uncover geometry-biomarker relations at cohort scale. The proximal neck diameter was the strongest determinant of shear, increasing mean WSS (r {approx} 0.77) and peak WSS0.95(r {approx} 0.58) while reducing low-TAWSS area (r {approx} -0.36). Maximum diameter minimally affected peak shear (r {approx} -0.03) but led to moderate increase of low-TAWSS regions (r {approx} +0.20). Compactness indices suppressed oscillatory shear; sphericity and convexity, largely under-explored AAA shape descriptors, showed strong inverse correlation with OSI (r {approx} -0.68, -0.65) and mean WSS (r {approx} -0.47, -0.59). The framework reveals neck calibre and shape compactness, not maximum diameter alone, as dominant modulators of AAA haemodynamics. Subject Areasfluid mechanics, biomechanics, biomedical engineering