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

Bayesian parameter estimation of ligament properties based on tibio-femoral kinematics during squatting

The objective of this study is to estimate the, probably correlated, ligament material properties and attachment sites in a highly non-linear, musculoskeletal knee model based on kinematic data of a knee rig experiment for seven specific specimens. Bayesian parameter estimation is used to account for uncertainty in the limited experimental data by optimization of a high dimensional input parameter space (50 parameters) consistent with all probable solutions. The set of solutions accounts for physiologically relevant ligament strain (ϵ&lt;6%). The transitional Markov Chain Monte Carlo algorithm was used. Alterations to the algorithm were introduced in order to avoid premature convergence. To perform the parameter estimation with feasible computational cost, a surrogate model of the knee model was trained. Results show that there is a large intra- and inter-specimen variability in ligament properties, and that multiple sets of ligament properties fit the experimentally measured tibio-femoral kinematics. Although all parameters were allowed to vary significantly, large interdependence is only found between the reference strain and attachment sites. The large variation between specimens and interdependence between reference strain and attachment sites within one specimen, show the inability to identify a small range of ligament properties representative for the patient population. To limit ligament properties uncertainty in clinical applications, research will need to invest in establishing patient-specific uncertainty ranges and/or accurate in vivo measuring methods of the attachment sites and reference strain and/or alternative (combinations of) movements that would allow identifying a unique solution.</p

In-silico techniques to inform and improve the personalized prescription of shoe insoles

Background: Corrective shoe insoles are prescribed for a range of foot deformities and are typically designed based on a subjective assessment limiting personalization and potentially leading to sub optimal treatment outcomes. The incorporation of in silico techniques in the design and customization of insoles may improve personalized correction and hence insole efficiency.Methods: We developed an in silico workflow for insole design and customization using a combination of measured motion capture, inverse musculoskeletal modelling as well as forward simulation approaches to predict the kinematic response to specific insole designs. The developed workflow was tested on twenty-seven participants containing a combination of healthy participants (7) and patients with flatfoot deformity (20).Results: Average error between measured and simulated kinematics were 4.7 ± 3.1, 4.5 ± 3.1, 2.3 ± 2.3, and 2.3 ± 2.7° for the chopart obliquity, chopart anterior-posterior axis, tarsometatarsal first ray, and tarsometatarsal fifth ray joints respectively.Discussion: The developed workflow offers distinct advantages to previous modeling workflows such as speed of use, use of more accessible data, use of only open-source software, and is highly automated. It provides a solid basis for future work on improving predictive accuracy by adapting the currently implemented insole model and incorporating additional data such as plantar pressure

Medical reverse engineering applications and methods

Understanding, controlling and manipulation of patient data as well as shape, geometry and structure of the biomedical objects are important for developing Biomedical Engineering (BME) applications. Medical Reverse Engineering (MRE) is aimed to use the Reverse Engineering (RE) technology to reconstruct 3D models of the anatomical structures and biomedical objects for design and manufacturing of medical products as well as BME research and development. This paper presents the state of the art applications and methods about MRE. Different concepts and methodologies are provided to understand fundamentally the MRE processes and workflow. The key MRE applications are presented, including personalised implants for bone reconstruction, dental implants and simulations, surgical tools, medical training, vision science and optometry, orthopedics, ergonomics, orthosis, prosthesis, and tissue engineering. The current challenges as well as the hardware and software for MRE application development and research are discussed

Automatic generation of real-time deformable parametric model of the aorta for a VR-based catheterism guidance system

The purpose of this work is twofold: first, to develop a process to automatically create parametric models of the aorta that can adapt to any possible intraoperative deformation of the vessel. Second, it intends to provide the tools needed to perform this deformation in real time, by means of a non-rigid registration method. This dynamically deformable model will later be used in a VR-based surgery guidance system for aortic catheterism procedures, showing the vessel changes in real time