Statistical shape modeling (SSM) is widely used in biology and medicine as a\nnew generation of morphometric approaches for the quantitative analysis of\nanatomical shapes. Technological advancements of in vivo imaging have led to\nthe development of open-source computational tools that automate the modeling\nof anatomical shapes and their population-level variability. However, little\nwork has been done on the evaluation and validation of such tools in clinical\napplications that rely on morphometric quantifications (e.g., implant design\nand lesion screening). Here, we systematically assess the outcome of widely\nused, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and\nSPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape\nmodels from different tools. We propose validation frameworks for anatomical\nlandmark/measurement inference and lesion screening. We also present a lesion\nscreening method to objectively characterize subtle abnormal shape changes with\nrespect to learned population-level statistics of controls. Results demonstrate\nthat SSM tools display different levels of consistencies, where ShapeWorks and\nDeformetrica models are more consistent compared to models from SPHARM-PDM due\nto the groupwise approach of estimating surface correspondences. Furthermore,\nShapeWorks and Deformetrica shape models are found to capture clinically\nrelevant population-level variability compared to SPHARM-PDM models.\n