This thesis provides essential morphological and morpho-functional insights while developing a computer-assisted image-based workflow for optimizing implant design. A comprehensive analysis identified 59 geometrical features of the native knee joint, extracted automatically from a dataset of 831 knee geometries, and subjected to statistical analysis. Significant gender-specific differences were found in most features. Following classification and normalization of these features, considerable inter-individual variations persisted, indicating the need for patient-specific design solutions for optimal implant design. The overall knee dimensions informed the calculation of necessary implant component sizes, revealing a demand for more sizes than currently available in the market. Additionally, the study focused on morpho-functional analysis by creating parameterized functional surface models of the articulating surfaces, enabling systematic variations of design parameters. A patient-specific biomechanical in silico model of the lower extremity and an experimental in vitro knee testing rig were developed to examine the relationship between design parameters and knee function. The feasibility of this morpho-functional analysis was illustrated through a patient-specific implant case, with kinematic analysis results potentially impacting knee implant design optimization regarding compatibility and sensitivity of design parameters.
