Advanced analyses of soft biological tissues have shown substantial subject-specific variability in mechanical properties [1]. Such variability is also easily observed at a geometrical or morphological level, and has been reported also in mechanical tests on biological tissue samples [1, 2]. While there is wide interest in reproducing accurate geometries for subject-specific modeling, constitutive parameters for mechanical models often use averaged data from mechanical tests [3]. Outliers are typically neglected, and only the ‘mean’ tissue behavior is considered. However, due to an increased interest in using multi-scale and finite element (FE) models for medical device testing and surgical planning [4], understanding of the variability of the outlier tests becomes increasingly important. In particular, by using detailed mechanistic constitutive models, it might be possible to classify the different mechanical behaviors observed on the basis of the changes in the constitutive parameters. This process could lead to the definition of a library of different ‘healthy’ or ‘diseased’ constitutive parameters to be used in computational analyses.

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