Computational Model Validation of Contact Mechanics in Total Ankle Arthroplasty

Digital twin systems for musculoskeletal applications: A current concepts review

Digital twin (DT) systems, which involve creating virtual replicas of physical objects or systems, have the potential to transform healthcare by offering personalised and predictive models that grant deeper insight into a patient's condition. This review explores current concepts in DT systems for musculoskeletal (MSK) applications through an overview of the key components, technologies, clinical uses, challenges, and future directions that define this rapidly growing field. DT systems leverage computational models such as multibody dynamics and finite element analysis to simulate the mechanical behaviour of MSK structures, while integration with wearable technologies allows real-time monitoring and feedback, facilitating preventive measures, and adaptive care strategies. Early applications of DT systems to MSK include optimising the monitoring of exercise and rehabilitation, analysing joint mechanics for personalised surgical techniques, and predicting post-operative outcomes. While still under development, these advancements promise to revolutionise MSK care by improving surgical planning, reducing complications, and personalising patient rehabilitation strategies. Integrating advanced machine learning algorithms can enhance the predictive abilities of DTs and provide a better understanding of disease processes through explainable artificial intelligence (AI). Despite their potential, DT systems face significant challenges. These include integrating multi-modal data, modelling ageing and damage, efficiently using computational resources and developing clinically accurate and impactful models. Addressing these challenges will require multidisciplinary collaboration. Furthermore, guaranteeing patient privacy and protection against bias is extremely important, as is navigating regulatory requirements for clinical adoption. DT systems present a significant opportunity to improve patient care, made possible by recent technological advancements in several fields, including wearable sensors, computational modelling of biological structures, and AI. As these technologies continue to mature and their integration is streamlined, DT systems may fast-track medical innovation, ushering in a new era of rapid improvement of treatment outcomes and broadening the scope of preventive medicine. Level of Evidence: Level V.

Developing and Validating a Model of Humeral Stem Primary Stability, Intended for In Silico Clinical Trials

In silico clinical trials (ISCT) can contribute to demonstrating a device's performance via credible computational models applied on virtual cohorts. Our purpose was to establish the credibility of a model for assessing the risk of humeral stem loosening in total shoulder arthroplasty, based on a twofold validation scheme involving both benchtop and clinical validation activities, for ISCT applications. A finite element model computing bone-implant micromotion (benchtop model) was quantitatively compared to a bone foam micromotion test (benchtop comparator) to ensure that the physics of the system was captured correctly. The model was expanded to a population-based approach (clinical model) and qualitatively evaluated based on its ability to replicate findings from a published clinical study (clinical comparator), namely that grit-blasted stems are at a significantly higher risk of loosening than porous-coated stems, to ensure that clinical performance of the stem can be predicted appropriately. Model form sensitivities pertaining to surgical variation and implant design were evaluated. The model replicated benchtop micromotion measurements (52.1 ± 4.3 µm), without a significant impact of the press-fit ("Press-fit": 54.0 ± 8.5 µm, "No press-fit": 56.0 ± 12.0 µm). Applied to a virtual population, the grit-blasted stems (227 ± 78µm) experienced significantly larger micromotions than porous-coated stems (162 ± 69µm), in accordance with the findings of the clinical comparator. This work provides a concrete example for evaluating the credibility of an ISCT study. By validating the modeling approach against both benchtop and clinical data, model credibility is established for an ISCT application aiming to enrich clinical data in a regulatory submission.

Total Ankle Replacement: Biomechanics of the Designs, Clinical Outcomes, and Remaining Issues

The present review paper aimed at discussing the current major issues in total ankle replacement, both the technical and biomechanical concepts, and the surgical and clinical concerns. Designers shall target at the same time restoration of natural ankle kinematics and congruity of the artificial surfaces throughout the range of motion. Surgeons are recommended to expand biomechanical knowledge on ankle joint replacement, and provide appropriate training and key factors to make arthroplasty a good alternative to arthrodesis. Moreover, adequate selection of patients and careful rehabilitation are critical. In the future, custom-made prosthesis components and patient-specific instrumentation are major developments for more complex cases.

Use of Real-World Data for Enhancing Model Credibility: Applications to Medical Device Development

Evaluating the credibility of computational models used in medical device development is increasingly important as medical devices become more complex and modeling takes on a more critical role in the device development process. While bench-testing based comparisons are common for assessing model credibility and have many advantages, such as control over test specimens and the ability to quantify outputs, the credibility assessments performed with bench tests often do not evaluate the clinical relevance of key aspects of model form (such as boundary conditions, constitutive models/properties, and geometries) selected when simulating in vivo conditions.

Real-world data (outcomes data generated through clinical use of a device) offer an opportunity to assess the applicability and clinical relevance of a computational model. Although real-world data are frequently less controlled and more qualitative than benchtop data, real-world data are often a direct assessment of a particular clinical complication and therefore of high clinical relevance. Further, real-world data have the potential to reveal failure modes not previously identified in pre-clinical failure modes analysis, thereby motivating testing advancements. To review the use of clinical data in medical device modeling, this paper presents a series of examples related to tibial tray fracture that incorporate varying levels of benchtop data and real world data when evaluating model credibility. The merits and drawbacks of the credibility assessment for each example are discussed in order to provide practical and actionable guidance on the use of real world data for establishing and demonstrating model credibility.