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Aseptic loosening is the most common failure mode for total hip arthroplasty, and the design of the implant plays a significant role in influencing the longevity and stability of the implant. Finite Element (FE) models have been demonstrated to be powerful numerical tools that allow for generating information supporting the device's safety and/or efficacy during pre-clinical assessment. Different authors have proposed FE studies aiming to simulate the long-term stability of the femoral stem; however, multiple improvements are still necessary for translating computational methodologies into clinical practice. This paper provides a comprehensive overview of the modelling procedures for predicting aseptic loosening risk, focusing on cementless femoral stems. The main modelling assumptions, including bone and implant geometry, materials, boundary conditions, and bone–implant interface contact, were summarised and presented. The limitations of various modelling assumptions and their impact on the simulation results were also discussed. The analysis suggests that more rigorous clinical validation for osseointegration models and failure criteria used to determine loosening of the implant should be clearly defined, and efforts should be made to identify the appropriate limit of tolerable conditions. [ABSTRACT FROM AUTHOR]

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing. [ABSTRACT FROM AUTHOR]

Pathologies such as cancer metastasis and osteoporosis strongly affect the mechanical properties of the vertebral bone and increase the risk of fragility fractures. The prediction of the fracture risk with a patient-specific model, directly generated from the diagnostic images of the patient, could help the clinician in the choice of the correct therapy to follow. But before such models can be used to support any clinical decision, their credibility must be demonstrated through verification, validation, and uncertainty quantification. In this study we describe a procedure for the generation of such patient-specific finite element models and present a first validation of the kinematics of the spine segment. Quantitative computed tomography images of a cadaveric lumbar spine segment presenting vertebral metastatic lesions were used to generate the model. The applied boundary conditions replicated a specific experimental test where the spine segment was loaded in compression-flexion. Model predictions in terms of vertebral surface displacements were compared against the full-field experimental displacements measured with Digital Image Correlation. A good agreement was obtained from the local comparison between experimental data and simulation results (R2 > 0.9 and RMSE% <8%). In conclusion, this work demonstrates the possibility to apply the developed modelling pipeline to predict the displacement field of human spine segment under physiological loading conditions, which is a first fundamental step in the credibility assessment of these clinical decision-support technology. [ABSTRACT FROM AUTHOR]

The term “In Silico Trial” indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative. [ABSTRACT FROM AUTHOR]

The application of the submodeling technique to finite element (FE) wear analyses has been recently proposed as an efficient solution to reduce the computational cost of the simulations and provide accurate numerical results. However, the method was validated only on single point contact cases. The present study proposes a generalization of the wear submodeling procedure that can be used to speed up FE wear simulations with multipoint contacts. The modified global–local procedure is applied and evaluated on a double contact pin on plate wear test using three‐dimensional models developed in Ansys® mechanical APDL. Three different model geometries with different curvature radii at the contact regions were considered in order to replicate possible critical scenarios. Results suggest that an additional wear simulation step where the global model is used to simulate the first wear cycles is needed to correctly transfer the boundary conditions to the local models. The new proposed strategy demonstrates the possibility to extend the method to more general FE wear simulations by significantly reducing their computational cost. [ABSTRACT FROM AUTHOR]

Agent‐based models (ABMs) are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for ABMs that aims at evaluating the numerical errors associated with the model. A step‐by‐step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS‐TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS‐TB and, in general, with any other ABMs. [ABSTRACT FROM AUTHOR]

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models. [ABSTRACT FROM AUTHOR]

Summary: Finite element simulation is a powerful tool to study and predict the wear behavior of different mechanical systems. However, the high computational cost required to obtain accurate numerical solutions is a crucial aspect especially for large and complex models. The present study proposes a possible solution to overcome this limitation by exploiting the potentiality of the submodeling technique, whose application to wear problem is still rare in the literature. A global‐local approach for wear analysis is presented and validated for a simple pin‐on‐disk wear test. In a preliminary step, different submodeling techniques were implemented and compared. They differed for the quantity (nodal forces and/or displacements) transferred from the global model to the local one. Numerical models have been developed in Ansys® mechanical APDL using its new wear simulation and mesh smoothing routines. Results suggest that the proposed strategy can significantly reduce the computational cost of finite element wear models. Some interesting points on the use of submodeling in contact problems are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the extent of gradient-induced vibrations, and the magnitude of motion-induced displacement forces ("Lenz effect"), in conductive nonmagnetic orthopedic prostheses., Methods: The investigation is carried out through numerical simulations, for a 3 T scanner. For gradient-induced torques and vibrations, a knee and a shoulder implant are considered, at dB/dt equal to 42 T/s (rms). For motion-induced forces associated with the Lenz effect, a knee and a hip implant are studied, considering a patient who translates on the examination couch, or walks next to it., Results: Gradient-induced torques may be within the same order of magnitude as the worst case gravitational torque defined in the ASTM standards. However, for all investigated cases, they result to be lower. In vacuum, the extent of the corresponding vibration reduces with frequency. At the lowest investigated frequency (270 Hz), it keeps below 25 μm. For an implant partially embedded in bone, the extent of the vibration increases with frequency. Nevertheless, the displacement is far lower than the worst case observed in vacuum (negligible in contact with the bone; ˜1 μm or less where the implant emerges from the bone). The Lenz effect induced by the motion of the patient through the stationary magnetic field produces forces on the order of a few millinewtons (i.e., at least two orders of magnitude lower than the implant weight)., Conclusion: Comparing the results with mechanical loads caused by ordinary activities of daily living, and with the levels of tolerable micromotions, a good safety margin is confirmed., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Total knee replacement (TKR) is one of the most performed orthopedic surgeries to treat knee joint diseases in the elderly population. Although the survivorship of knee implants may extend beyond two decades, the poor outcome rate remains considerable. A recent computational approach used to better understand failure modes and improve TKR outcomes is based on the combination of musculoskeletal (MSK) and finite element models. This combined multiscale modeling approach is a promising strategy in the field of computational biomechanics; however, some critical aspects need to be investigated. In particular, the identification and quantification of the uncertainties related to the boundary conditions used as inputs to the finite element model due to a different definition of the MSK model are crucial. Therefore, the aim of this study is to investigate this problem, which is relevant for the model credibility assessment process. Three different generic MSK models available in the OpenSim platform were used to simulate gait, based on the experimental data from the fifth edition of the "Grand Challenge Competitions to Predict in vivo Knee Loads." The outputs of the MSK analyses were compared in terms of relative kinematics of the knee implant components and joint reaction (JR) forces and moments acting on the tibial insert. Additionally, the estimated knee JRs were compared with those measured by the instrumented knee implant so that the "global goodness of fit" was quantified for each model. Our results indicated that the different kinematic definitions of the knee joint and the muscle model implemented in the different MSK models influenced both the motion and the load history of the artificial joint. This study demonstrates the importance of examining the influence of the model assumptions on the output results and represents the first step for future studies that will investigate how the uncertainties in the MSK models propagate on disease-specific finite element model results., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Curreli, Di Puccio, Davico, Modenese and Viceconti.)