Background: Objects that manifest in several characteristic shapes, or morphotypes, are typically caused by some hidden variable. For example, the gender of a person influences the width of their pelvis. This is important when reconstructing natural shapes, e.g., in knee implant design. The aim of this study was to identify such morphotypes.
Methods: This work investigated the shapes of roughly 1000 knee joints acquired from computed tomography, including the distal femur and proximal tibia. Two comprehensive feature sets were utilized to describe the bone shapes, one based on morphological measurements and the other on statistical shape model (SSM) weights. We normalized the data by size and performed a cluster analysis with different algorithms, namely k-means and high dimensional data clustering. The clusters were evaluated using several metrics.
Results: The data showed a low tendency to form clusters. Only one of 12 experiments slightly exceeded the thresholds for actual clusters suggested by the literature. k-Means outperformed high dimensional data clustering in all cases.
Conclusion: After anisotropic normalization by size, which removes size and aspect ratio related differences, the data exhibited no morphotypes. This showed that there are no relevant hidden variables, e.g., gender, body type or ethnicity, which influence the shape of the knee joint. Instead, knee shape is highly individual. Investigating the three-dimensional shape, variations occur for a wide range of different shape parameters, not just for anterior-posterior and mediolateral size.
Keywords: Cluster analysis; Implant design; Knee joint; Knee replacement; Morphology; Statistical shape model.
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