Optimisation of orthopaedic implant design

10 Statistical shape analysis techniques have shown to be efficient tools to build pop11 ulation specific models of anatomical variability. Their use is commonplace as prior 12 models for segmentation, in which case the instance from the shape model that 13 best fits the image data is sought. In certain cases, however, it is not just the most 14 likely instance that must be searched, but rather the whole set of shape instances 15 that meet certain criterion. In this paper we develop a method for the assessment 16 of specific anatomical/morphological criteria across the shape variability found in a 17 population. The method is based on a level set segmentation approach, and used on 18 the parametric space of the statistical shape model of the target population, solved 19 via a multi-level narrow-band approach for computational efficiency. Based on this 20 technique, we develop a framework for evidence-based orthopaedic implant design. 21 To date, implants are commonly designed and validated by evaluating implant bone 22 fitting on a limited set of cadaver bones, which not necessarily span the whole vari23 ability in the population. Based on our framework, we can virtually fit a proposed 24 implant design to samples drawn from the statistical model, and assess which range 25 of the population is suitable for the implant. The method highlights which patterns 26 of bone variability are more important for implant fitting, allowing and easing im27 plant design improvements, as to fit a maximum of the target population. Results 28 are presented for the optimisation of implant design of proximal human tibia, used 29 for internal fracture fixation. 30