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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. [ABSTRACT FROM AUTHOR]

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]

The hip-spine relationship is a critical consideration in total hip arthroplasty (THA) procedures. While THA is generally successful in patient, complications such as instability and dislocation can arise. These issues are significantly influenced by the alignment of implant components and the overall balance of the spine and pelvis, known as spinopelvic balance. Patients with alteration of those parameters, in particular rigid spines, often due to fusion surgery, face a higher risk of THA complications, with an emphasis on complications in instability, impingement and dislocation. For these reasons, over the years, computer modelling and simulation techniques have been developed to support clinicians in the different steps of surgery. The aim of the current review is to present current knowledge on hip-spine relationship to serve as a common platform of discussion among clinicians and engineers. The offered overview aims to update the reader on the main critical aspects of the issue, from both a theoretical and practical perspective, and to be a valuable introductory tool for those approaching this problem for the first time. [ABSTRACT FROM AUTHOR]

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose–response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40‐2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development. [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 optimal neuromuscular control (muscle activation strategy that minimises the consumption of metabolic energy) during level walking is very close to that which minimises the force transmitted through the joints of the lower limbs. Thus, any suboptimal control involves an overloading of the joints. Some total knee replacement patients adopt suboptimal control strategies during level walking; this is particularly true for patients with co-morbidities that cause neuromotor control degeneration, such as Parkinson's Disease (PD). The increase of joint loading increases the risk of implant failure, as reported in one study in PD patients (5.44% of failures at 9 years follow-up). One failure mode that is directly affected by joint loading is massive wear of the prosthetic articular surface. In this study we used a validated patient-specific biomechanical model to estimate how a severely suboptimal control could increase the wear rate of total knee replacements. Whereas autopsy-retrieved implants from non-PD patients typically show average polyethylene wear of 17 mm3 per year, our simulations suggested that a severely suboptimal control could cause a wear rate as high as of 69 mm3 per year. Assuming the risk of implant failure due to massive wear increase linearly with the wear rate, a severely suboptimal control could increase the risk associated to that failure mode from 0.1% to 0.5%. Based on these results, such increase would not be not sufficient to justify alone the higher incidence rate of revision in patients affected by Parkinson's Disease, suggesting that other failure modes may be involved. [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]

Background: The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial. Results: One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject. Conclusions: We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration. [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]