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

Abstract 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 ≈ 0.77) and peak WSS0.95(r ≈ 0.58) while reducing low-TAWSS area (r ≈ −0.36). Maximum diameter minimally affected peak shear (r ≈ −0.03) but led to moderate increase of low-TAWSS regions (r ≈ +0.20). Compactness indices suppressed oscillatory shear; sphericity and convexity, largely under-explored AAA shape descriptors, showed strong inverse correlation with OSI (r ≈ −0.68, −0.65) and mean WSS (r ≈ −0.47, −0.59). The framework reveals neck calibre and shape compactness, not maximum diameter alone, as dominant modulators of AAA haemodynamics. Subject Areas fluid mechanics, biomechanics, biomedical engineering Competing Interest Statement The authors have declared no competing interest.