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However, segmentation of the flow domain is not exact and geometric uncertainty can be expected which propagates through the computational model, leading to uncertainty in model output. Seventy\u2010four aortic\u2010valves were segmented from computed tomography images at peak systole. Statistical shape modeling was used to obtain an approximate parameterization of the original segmentations. This parameterization was used to train a meta\u2010model that related the first five shape mode coefficients and flowrate to the CFD\u2010computed transvalvular pressure\u2010drop. Consequently, shape uncertainty in the order of 0.5 and 1.0\u2009mm was emulated by introducing uncertainty in the shape mode coefficients. A global variance\u2010based sensitivity analysis was performed to quantify output uncertainty and to determine relative importance of the shape modes. 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