Special Issue on 'Computational Modelling in Medicine': Guest editor introduction.

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Policy needs and options for a common approach towards modelling and simulation of human physiology and diseases with a focus on the virtual physiological human.

Life is the result of an intricate systemic interaction between many processes occurring at radically different spatial and temporal scales. Every day, worldwide biomedical research and clinical practice produce a huge amount of information on such processes. However, this information being highly fragmented, its integration is largely left to the human actors who find this task increasingly and ever more demanding in a context where the information available continues to increase exponentially. Investments in the Virtual Physiological Human (VPH) research are largely motivated by the need for integration in healthcare. As all health information becomes digital, the complexity of health care will continue to evolve, translating into an ever increasing pressure which will result from a growing demand in parallel to limited budgets. Hence, the best way to achieve the dream of personalised, preventive, and participative medicine at sustainable costs will be through the integration of all available data, information and knowledge

A haptic-enabled multimodal interface for the planning of hip arthroplasty

Multimodal environments help fuse a diverse range of sensory modalities, which is particularly important when integrating the complex data involved in surgical preoperative planning. The authors apply a multimodal interface for preoperative planning of hip arthroplasty with a user interface that integrates immersive stereo displays and haptic modalities. This article overviews this multimodal application framework and discusses the benefits of incorporating the haptic modality in this area

Big Data, Big Knowledge: Big Data for Personalized Healthcare.

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously. While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data. Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence

In silico clinical trials: how computer simulation will transform the biomedical industry

The term ‘in silico clinical trials indicates the use of individualised computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention. This review article summarises the research and technological roadmap developed by the Avicenna Support Action during an 18 month consensus process that involved 577 international experts from academia, the biomedical industry, the simulation industry, the regulatory world, etc. The roadmap documents early examples of in silico clinical trials, identifies relevant use cases for in silico clinical trial technologies over the entire development and assessment cycle for both pharmaceuticals and medical devices, identifies open challenges and barriers to a wider adoption and puts forward 36 recommendations for all relevant stakeholders to consider

The uncontrolled manifold theory could explain part of the inter-trial variability of knee contact force during level walking

Accurate predictions of joint contact forces through computer simulation of musculoskeletal dynamics can provide insight, in a non-invasive manner, into the joint loads of patients with osteoarthritis and healthy controls. The current approach to assume optimal control, in terms of metabolic energy expenditure, remains a major limitation of the prediction of muscle activation patterns that determine joint contact forces. Stochastically optimal muscle control, in the form of a stochastic component superimposed to the optimal control, could potentially explain the inter-trial variability as observed in measured knee contact forces during level walking. A probabilistic approach was used to predict sets of possible muscle activation patterns within a 5 and 10% limit from the optimal muscle activation pattern. The knee contact forces determined by both the optimal and stochastically optimal muscle activation patterns were compared to the corresponding knee contact force patterns measured by an instrumented implant. The range of muscle control patterns captured the inter-trial variability of knee contact forces for most of the gait cycle, suggesting that the probabilistic approach used here is representative of a stochastically optimal control that accounts for co-contraction, whereas during some time intervals a more explicit representation of the motor control strategy is required. These findings underline the importance of stochastically optimal muscle control in the prediction of knee forces within a multi-body dynamics approach

Investigating the mechanical response of paediatric bone under bending and torsion using finite element analysis

Fractures of bone account 25% of all paediatric injuries (Cooper et al. in J Bone Miner Res 19:1976-1981, 2004. https://doi.org/10.1359/JBMR.040902 ). These can be broadly categorised into accidental or inflicted injuries. The current clinical approach to distinguish between these two is based on the clinician's judgment, which can be subjective. Furthermore, there is a lack of studies on paediatric bone to provide evidence-based information on bone strength, mainly due to the difficulties of obtaining paediatric bone samples. There is a need to investigate the behaviour of children's bones under external loading. Such data will critically enhance our understanding of injury tolerance of paediatric bones under various loading conditions, related to injuries, such as bending and torsional loads. The aim of this study is therefore to investigate the response of paediatric femora under two types of loading conditions, bending and torsion, using a CT-based finite element approach, and to determine a relationship between bone strength and age/body mass of the child. Thirty post-mortem CT scans of children aged between 0 and 3 years old were used in this study. Two different boundary conditions were defined to represent four-point bending and pure torsional loads. The principal strain criterion was used to estimate the failure moment for both loading conditions. The results showed that failure moment of the bone increases with the age and mass of the child. The predicted failure moment for bending, external and internal torsions were 0.8-27.9, 1.0-31.4 and 1.0-30.7 Nm, respectively. To the authors' knowledge, this is the first report on infant bone strength in relation to age/mass using models developed from modern medical images. This technology may in future help advance the design of child, car restrain system, and more accurate computer models of children

Effect of integration time on the morphometric, densitometric and mechanical properties of the mouse tibia

Micro-Computed Tomography (microCT) images are used to measure morphometric and densitometric properties of bone, and to develop finite element (FE) models to estimate mechanical properties. However, there are concerns about the invasiveness of microCT imaging due to the X-rays ionising radiation induced by the repeated scans on the same animal. Therefore, the best compromise between radiation dose and image quality should be chosen for each preclinical application. In this study, we investigated the effect of integration time (time the bone is exposed to radiation at each rotation step during microCT imaging) on measurements performed on the mouse tibia. Four tibiae were scanned at 10.4 µm voxel size using four different procedures, characterized by decreasing integration time (from 200 ms to 50 ms) and therefore decreasing nominal radiation dose (from 513 mGy to 128 mGy). From each image, trabecular and cortical morphometric parameters, spatial distribution of bone mineral content (BMC) in the whole tibia and FE-based estimations of stiffness and strength were obtained. A high-resolution scan (4.3 µm voxel size) was used to quantify measurement errors. Integration time had the largest effect on trabecular morphometric parameters (7-28%). Lower effects were observed on cortical parameters (1-3%), BMC (1-10%) distribution, and FE-based estimations of mechanical properties (1-3%). In conclusion, the effect of integration time on image-based measurements has been quantified. This data should be considered when defining the in vivo microCT scanning protocols in order to find the best compromise between nominal radiation exposure and accuracy in the estimation of bone parameters