Time-dependent in silico modelling of orthognathic surgery to support the design of biodegradable bone plates

Orthognathic surgery is performed to realign the jaws of a patient through several osteotomies. The state-of- the-art bone plates used to maintain the bone fragments in place are made of titanium. The presence of these non-degradable plates can have unwanted side effects on the long term (e.g. higher infection risk) if they are not removed. Using a biodegradable material such as magnesium may be a possible solution to this problem. However, biodegradation leads to a decrease of mechanical strength, therefore a time-dependent computational approach can help to evaluate the performance of such plates. In the present work, a computational framework has been developed to include biodegradation and bone healing algorithms in a finite element model. It includes bone plates and the mandible, which are submitted to masticatory loads during the early healing period (two months following the surgery). Two different bone plate designs with different stiffnesses have been tested. The stiff design exhibited good mechanical stability, with maximum Von Mises stress being less than 40% of the yield strength throughout the simulation. The flexible design shows high stresses when the bone healing has not started in the fracture gaps, indicating possible failure of the plate. However, this design leads to a higher bone healing quality after two months, as more cartilage is formed due to higher strains exerted in fracture gaps. We therefore conclude that in silico modelling can support tuning of the design parameters to ensure mechanical stability and while promoting bone healing

Integration of cortical thickness data in a statistical shape model of the scapula

Knowledge about bone morphology and bone quality of the scapula throughout the population is fundamental in the design of shoulder implants. In particular, regions with the best bone stock (cortical bone) are taken into account when planning the supporting screws, aiming for an optimal fixation. As an alternative to manual measurements, statistical shape models (SSMs) have been commonly used to describe shape variability within a population. However, explicitly including cortical thickness information in an SSM of the scapula still remains a challenge. Therefore, the goal of this study is to combine scapular bone shape and cortex morphology in an SSM. First, a method to estimate cortical thickness, based on HU (Hounsfield Unit) profile analysis, was developed and validated. Then, based on the manual segmentations of 32 healthy scapulae, a statistical shape model including cortical information was created and evaluated. Generalization, specificity and compactness were calculated in order to assess the quality of the SSM. The average cortical thickness of the SSM was 2.0¿±¿0.63¿mm. Generalization, specificity and compactness performances confirmed that the combined SSM was able to capture the bone quality changes in the population. In this work we integrated information on the cortical thickness in an SSM for the scapula. From the results we conclude that this methodology is a valuable tool for automatically generating a large population of scapulae and deducing statistics on the cortex. Hence, this SSM can be useful to automate implant design and screw placement in shoulder arthroplasty

Magneto-optic contact for application in an amplifying waveguide optical isolator

We present the development of a metal-semiconductor contact for a TM-mode amplijying waveguide optical isolator and show that it is a compromise between good (magneto-)optical performance and good electrical behavior

Standard Cruciate-Retaining Total Knee Arthroplasty Implants can Reproduce Native Kinematics

Total knee arthroplasty (TKA) is a common procedure that has become the standard of treatment for severe cases of knee osteoarthritis. Biomechanics and quality of movement similar to healthy were found to improve patient-reported outcomes. In this study, an evaluated musculoskeletal model predicted ligament, contact and muscle forces together with secondary tibiofemoral kinematics. An artificial neural network applied to the musculoskeletal model searched for the optimal implant position in a given range that will minimize the root-mean-square-error (RMSE) between post- TKA and native experimental tibiofemoral kinematics during a squat. We found that, using a cruciate-retaining implant, native kinematics could be accurately reproduced (average RMSE 1.47 mm (± 0.89 mm) for translations and 2.89° (± 2.83°) for rotations between native and optimal TKA alignment). The required implant positions changes maximally 2.96 mm and 2.40o. This suggests that when using pre- operative planning, off-the-shelf CR implants allow for reproducing native knee kinematics post-operatively