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In Silico Modeling Literature Roundup

Posted on 29 August 2024 by Kerim Genc
 

The use of in silico modeling is increasingly being recognized as a valuable complement to traditional testing, including for clinical trials and other forms of pre-surgical planning. Since our previous roundup of in silico case studies published by our users, there has been a lot of exciting research applying Simpleware software to different image-based modeling challenges. We have picked several highlights of how Simpleware software is helping researchers and industry develop insights into images acquired from medical image data.

On Effect of Residual Stress on Fracture Behavior of Mandibular Reconstruction Plates

Wan, B., Yoda, N., Zheng, K., Zhang, Z., Wu, C., Clark, J.R., Paradowska, A., Swain, M.V., Li, Q. 2024. . Engineering Fracture Mechanics, 305.

CT based 3D FE model of mandible using Simpleware software (CC BY 4.0)

CT based 3D FE model of mandible with major segmental defect and bridged by a mandibular plate. The blue arrows indicate muscle forces: Lateral pterygoid (LP), temporalis (T), medial pterygoid (MP), and masseter (MA). Yellow triangles show the kinematic constraints around the temporomandibular joints ( by Wan et al. / / Resized from original).

Context

"Titanium reconstruction plates are often used to bridge mandibulectomy defects in load bearing scenarios when bone grafts are not well integrated to the host bone. The residual stress within a standard reconstruction plate is generated when being bent and installed to adapt to a patient-specific anatomical contour and it may have a detrimental effect on the structural stability and reliability of the reconstruction system. 

This study aimed to evaluate the impact of residual stress on the mechanical strength of the reconstructed mandibular system by utilizing both conventional finite element method (FEM) and eXtended Finite Element Method (XFEM). The mechanical stresses introduced by plate pre-bending and screw tightening were first modeled computationally and the residual stress data induced by the surgical procedure was incorporated to the deformed reconstruction plate for the subsequent biomechanical evaluation. 

Static and cyclic loading conditions were then imposed on the mandibular plate models to further investigate two common failure types, namely overloading fracture and fatigue fracture. It is revealed that the residual stress could considerably increase the susceptibility of plate fracture. The simulation results demonstrate that the pre-stresses induced by screw tightening are more substantial than that from plate bending during the surgical procedure. The finding is of important clinical implications for surgeons who are commonly involved in selecting and preparing different forms of fixation plates for mandibular reconstruction. This study helps elucidate the key factors contributing on the failure of reconstruction plates and guide the development of more robust and durable mandibular reconstruction systems."

Use of Simpleware Software

"The 3D computational model of the mandible was created based upon the clinical computed tomography (CT) images by using the commercial code ScanIP (Simpleware, Exeter, UK) as shown in Fig. 1. The segmented mandible and teeth were further processed in Rhinoceros 4.0 (Robert McNeel & Associates, Seattle, USA) to create a parametric model using non-uniform rational B-spline (NURBS). After the surface smoothing and refinement, the 3D solid model was imported to commercial FEA code Abaqus (ABAQUS Inc, Providence, RI)."

Outcomes and Impact

"This study addressed an important clinical issue relevant to the risk factors of a reconstructed mandibular system. The conventional finite element method (FEM) and an extended finite element method (XFEM) were adopted to explore the effects of surgical residual stress on the mechanical strength of the conventional reconstruction plate. For the first time, we considered the mechanical stresses introduced by plate pre-bending and screw tightening; and the residual stress data induced during surgical preparation was then incorporated to the deformed reconstruction plate for the subsequent overloading and fatigue fracture evaluation. The study revealed that the surgical residual stress can considerably increase the susceptibility of plate fracture failure. The simulation results demonstrate that the stresses induced by screw tightening are more substantial than that from plate bending. This study is of considerable clinical implications for surgeons who perform mandibular reconstruction and other forms of fixation plate adaptation. This study will help elucidate the factors contributing to failure of reconstruction plates and provide a guide for the development of more robust and durable mandibular reconstruction systems."

In Silico Mechanics of Stem Cells Intramyocardially Transplanted with a Biomaterial Injectate for Treatment of Myocardial Infarction

Motchon, Y.D., Sack, K.L., Sirry, M.S., Nchejane, N.J., Abdalrahman, T., Nagawa, J., Kruger, M., Pauwels, E., Van Loo, D., De Muynck, A., Van Hoorebeke, L., Davies, N.H., Franz, T., 2024. . Cardiovascular Engineering and Technology.

Models from biventricular rat geometry using Simpleware software (CC BY 4.0)

Geometries and models developed from the biventricular rat geometry to the microstructure extracted from the left ventricle mid-wall with transplanted cells in the injectate region. (a) Biventricular geometry showing myocardium (green translucent) and injectate (beige). (b) Meshed biventricular geometry with a cross-section illustrating the difference in mesh density between myocardium (yellow) and injectate (purple). (c) Meshed biventricular geometry illustrating the infarct region nodes (blue). (d) Development of a microstructural model of a left ventricle mid-wall region. Left: Biventricular geometry showing the size and location of the microstructural region. Middle: Microstructural geometry comprising myocardium, injectate and nine transplanted cells. Right: Cellular geometry with membrane, cytoplasm, and nucleus. (e) Meshed microstructural geometry with coarse mesh for the myocardium and injectate and fine mesh for the cells. (f) Cross-sectional view of meshed geometry of single cell showing membrane (red), cytoplasm (pink) and nucleus (yellow). (g) The biventricular FE mesh¡¯s basal nodes (red) were fixed as the boundary condition for simulations ( by Motchon et al. / / Resized from original).

Context

"Purpose: Biomaterial and stem cell delivery are promising approaches to treating myocardial infarction. However, the mechanical and biochemical mechanisms underlying the therapeutic benefits require further clarification. This study aimed to assess the deformation of stem cells injected with the biomaterial into the infarcted heart.

Methods: A microstructural finite element model of a mid-wall infarcted myocardial region was developed from ex vivo microcomputed tomography data of a rat heart with left ventricular infarct and intramyocardial biomaterial injectate. Nine cells were numerically seeded in the injectate of the microstructural model. The microstructural and a previously developed biventricular finite element model of the same rat heart were used to quantify the deformation of the cells during a cardiac cycle for a biomaterial elastic modulus (Einj) ranging between 4.1 and 405,900 kPa.

Results: The transplanted cells' deformation was largest for Einj = 7.4 kPa, matching that of the cells, and decreased for an increase and decrease in Einj. The cell deformation was more sensitive to Einj changes for softer (Einj ¡Ü 738 kPa) than stiffer biomaterials.

Conclusions: Combining the microstructural and biventricular finite element models enables quantifying micromechanics of transplanted cells in the heart. The approach offers a broader scope for in silico investigations of biomaterial and cell therapies for myocardial infarction and other cardiac pathologies."

Use of Simpleware Software

"The biventricular geometry was reconstructed from the ?CT image stack using semi-automated segmentation tools, including region-growing, level-set thresholding, and manual actions (Simpleware, Synopsys). The resulting geometry captured the essential morphology of the left and right ventricles and microstructural details of the dispersed injectate in the LV free wall (Fig. 1a). The meshed geometry comprised 147,240 (mesh density 302.8 mm3 ) and 58,902 (3,852.3 mm3 ) quadratic tetrahedral elements in the myocardial and injectate region, respectively (Fig. 1b). The meshed geometry was imported in Abaqus 6.14-3 CAE (Dassault Syst¨¨mes, Providence, RI, USA) and the infarct region was approximated by identifying the nodes surrounding the biomaterial injectate (Fig. 1c). The myofibre orientation varying from ?50¡ã at the epicardium to 80¡ã at the endocardium [36] was implemented with a rule-based approach."

"A microstructural geometry of mid-wall volume of 748 ¦Ìm x 748 ¦Ìm x 722 ¦Ìm in the LV infarct (Fig. 1d) with higher spatial resolution was reconstructed by resampling of the cardiac ?CT image data with reduced spacing (7.8 ¦Ìm compared to 30 ¦Ìm in x, y, and z direction for the biventricular geometry) (Simpleware).

Fifteen cells comprising membrane, cytoplasm and nucleus were numerically seeded at random locations in the injectate region of the microstructural geometry with a custom Python script in Simpleware ScanIP. For each cell, three concentric spherical surfaces with diameters of 60, 55, and 20 ¦Ìm were created for the membrane, cytoplasm, and nucleus (Fig. 1d right). A Boolean subtraction resulted in a thickness of 5 ¦Ìm and 35 ¦Ìm for the membrane and the cytoplasm, respectively. The tree-component assembly was placed in the injectate. Of these 15 cells, six were located in the myocardium or near model boundaries and interfaces and were not considered for the analysis.

The resulting micro-structural geometry containing myocardium, injectate, and nine cells with membrane, cytoplasm and nucleus was meshed and imported into Abaqus. The mesh comprised 320,653 10-node tetrahedral elements (C3D10M), i.e. 2,552, 168,746, and 149,355 elements for the myocardium, injectate and the nine cells, respectively. The mesh size varied among the nine cells based on the automated meshing process (Simpleware), and element numbers were 6,101¡À42 for the membrane, 8,710¡À299 for the cytoplasm, and 1,784¡À82 for the nucleus."

Outcomes and Impact

"The current study is the first to quantify the deformation of therapeutic cells intramyocardially transplanted into an infarcted rat heart using a biomaterial injectate. The developed microstructural finite element model of the myocardium and biomaterial injectate at cellular length scale enables quantifying micromechanics of transplanted cells during a cardiac cycle. The coupled microstructural and biventricular cardiac finite element models can provide a point of departure for an in silico method including the mechanotransduction and signalling in the transplanted cells - with a broader scope of advancing therapeutic biomaterial and cell injections for MI and other cardiac conditions such as heart failure."

A Computational Modelling Tool for Prediction of Head Reshaping Following Endoscopic Strip Craniectomy and Helmet Therapy for the Treatment of Scaphocephaly

Deliege, L., Carriero, A., Ong, J., James, G., Jeelani, O., Dunaway, D., Stoltz, P., Hersh, D., Martin, J., Carroll, K., Chamis, M., Schievano, S., Bookland, M., Borghi, Al., 2024. . Computers in Biology and Medicine, 177.

Surface deviation for head reshaping using Simpleware software (CC BY 4.0)

Surface deviation between the FE prediction and the end-of-treatment optical scans ( by Deliege et al. / / Resized from original).

Context

"Background: Endoscopic strip craniectomy followed by helmet therapy (ESCH) is a minimally invasive approach for correcting sagittal craniosynostosis. The treatment involves a patient-specific helmet designed to facilitate lateral growth while constraining sagittal expansion. In this study, finite element modelling was used to predict post-treatment head reshaping, improving our comprehension of the necessary helmet therapy duration.

Method: Six patients (aged 11 weeks to 9 months) who underwent ESCH at Connecticut Children's Hospital were enrolled in this study. Day-1 post-operative 3D scans were used to create skin, skull, and intracranial volume models. Patient-specific helmet models, incorporating areas for growth, were designed based on post-operative imaging. Brain growth was simulated through thermal expansion, and treatments were modelled according to post-operative Imaging available. Mechanical testing and finite element modelling were combined to determine patient-specific mechanical properties from bone samples collected from surgery.

Validation compared simulated end-of-treatment skin surfaces with optical scans in terms of shape matching and cranial index estimation.

Results: Comparison between the simulated post-treatment head shape and optical scans showed that on average 97.3 ¡À 2.1 % of surface data points were within a distance range of ?3 to 3 mm. The cranial index was also accurately predicted (r = 0.91).

Conclusions: In conclusion, finite element models effectively predicted the ESCH cranial remodeling outcomes up to 8 months postoperatively. This computational tool offers valuable insights to guide and refine helmet treatment duration. This study also incorporated patient-specific material properties, enhancing the accuracy of the modeling approach.""

Use of Simpleware Software

"After testing, each patient's sample was scanned using a SkyScan1172 Bruker micro-CT scanner at a pixel size of 8.93 ¦Ìm and applying an aluminum filter to reduce artifacts and improve the image quality [26] (50 kV voltage, 201 ¦ÌA current and approximately 4000 slices per sample). The resulting stack of 2D images was reconstructed using Sky Scan's volumetric NRecon reconstruction software and segmented to isolate the bone tissue in Simpleware ScanIP? (Synopsis, Mountain View, CA).

Two beam configurations were considered: a porous model (referred here as Micro-CT model) extracted directly from the CT reconstruction; a second model obtained in Simpleware ScanIP? by filling in the porous cavities (initially using the Close operations and then manually adjusting when necessary) and thus consisted in a solid, non-porous approximation of the beam (referred here as Solid model). The porosity (defined as the volume of the pores over the total volume) [27] of each MicroCT model was also computed in ScanIP. Spearman correlation was used to assess correlation between patient porosity and age."

"Intermediate and end-of-treatment shape predictions were validated by comparing with post-treatment optical scans. The two surfaces were aligned and cut along the plane passing through nasion and auditory meatuses to discard the face; both surfaces were then imported in Simpleware ScanIP? to generate the surface deviation. The percentage of surface points within the [?3; +3]mm interval, usually considered in maxillofacial surgery planning [34], was assessed. Pressure values observed between the skin and the helmet were also recorded. The Cranial index (CI) defined as the ratio of head width and the head length, calculated for each predicted head shape was compared to pre-operative values from clinical notes."

"On average, the 6 beams collected were approximately 49 mm in length, 2.2 mm in width and 2.1 mm in thickness. Results of the three-point bending test FE models were analysed and the elastic modulus for each sample was estimated twice, once simulating deflection on the beam model extracted from the MicroCT images, (EMicroCT), once repeating the same simulation but assuming solid beam configuration (ESolid). The elastic moduli corresponding to the MicroCT bone model was 1761.2 ¡À 819.2 MPa while for the solid model was 1173.2 ¡À 596.2 MPa. Table 2 summarizes all the results. Samples porosity was calculated in Simpleware and possible correlation with the age was suggested by spearman correlation test (r = ?0.77, p = 0.053)."

"ScanIP and Geomagic Wrap were used for generating and editing the TKR model because as they were already available in the lab and the authors had prior knowledge of their use."

Outcomes and Impact

"Craniosynostosis, a congenital deformity involving premature cranial suture closure, affects 1 in 2500 newborns. The most common type, sagittal craniosynostosis (SC), is managed with endoscopic strip craniectomy (ESC) and subsequent helmet therapy (ESCH). ESC involves removing the fused suture, allowing lateral skull expansion driven by the growing brain. Helmets, designed from post-operative 3D scans, restrict growth in specific areas, promoting overall cranial reshaping. However, uncertainties persist regarding optimal treatment parameters.

This study addresses these gaps through a comprehensive approach, integrating experimental and computational methods. Six patients (11 weeks¨C9 months old) who underwent ESCH at Connecticut Children's Hospital were enrolled. Patient-specific helmet models, incorporating growth areas, were designed based on post-operative imaging. Finite element modeling simulated brain growth through thermal expansion, and mechanical testing determined patient-specific bone properties. Validation compared simulated post-treatment head shapes with optical scans, demonstrating 97.3 ¡À 2.1 % accuracy within a ?3 to 3 mm distance range. The cranial index was also accurately predicted (r = 0.91).

Finite element models effectively predicted ESCH cranial remodeling outcomes up to 8 months postoperatively, providing valuable insights into treatment duration. This computational tool, incorporating patient-specific material properties, enhances modeling accuracy. The study contributes to understanding the biomechanics of cranial remodeling, optimizing surgical and orthotic interventions for SC. The findings guide and refine helmet treatment duration, ultimately improving functional and cosmetic outcomes for affected individuals."

Computationally Enhanced, Haemodynamic Case Study of Neointimal Hyperplasia Development in a Dialysis Access Fistula

Bartlett, M., Bonfanti, M., Diaz-Zuccarini, V., Tsui, J., 2024. . Reviews in Cardiovascular Medicine, 25(1).

Observation sites for analysis of the radial artery and cephalic vein (CC BY 4.0)

Observation sites used for analysis. Radial Artery: A, B, C, D. Cephalic Vein: a, b, c, d, e, f ( by Bartlett et al. / / Resized from original).

Context

"Background: Oscillatory wall shear stress and related metrics have been identified as potential predictors of dialysis access outcomes; however, the absence of a simple non-invasive method for measuring these haemodynamic forces has been prohibitive to their adoption into routine clinical practice. We present a computationally enhanced, single patient case study, offering a unique insight into the haemodynamic environment surrounding the development of flow limiting neointimal hyperplasia within the efferent vein of a previously functional arteriovenous fistula (AVF). 

Methods: Computational fluid dynamics (CFD) simulations were used to create a quantitative map of oscillatory shear stress as well as enabling visualisation of streamline patterns within the AVF. CFD data was compared to ultrasound-based turbulence quantification and examined alongside structural and functional changes in the access site over time. 

Results: This work further supports the notion that flow limiting neointimal hyperplasia development in vascular access fistulae, occurs in response to oscillatory wall shear stress, and provides proof of concept for the idea that non-invasive ultrasound turbulence quantification tools could play a role in predicting vascular access outcomes. Conclusions: In addition to providing insight into the haemodynamic environment surrounding the development of flow limiting neointimal hyperplasia, we hope that this paper will promote discussion and further thinking about how our learnings from in-silico studies can be incorporated into clinical practice through novel uses of existing diagnostic tools."

Use of Simpleware Software

"The geometry for the simulation was based on the CEMRA of the upper limb. The data were exported in Digital Imaging and Communications in Medicine (DICOM) format and axial image slices were loaded into Simpleware? Scan IP (Synopsys Inc, Irvine, CA, USA)."

Outcomes and Impact

"This enhanced, computational case study provides further support for the notion of oscillatory shear forces playing a major role in the process of accelerated NIH formation, leading to dialysis access failure. These oscillatory forces can be located and quantified with the use of OSI, as well as other useful metrics such as HOLMES, however these values can only be obtained from complex, computationally expensive CFD models, which are not currently a viable tool for individual patient diagnostics or surveillance. The application of USTIR to the case presented here demonstrates the potential of using Doppler ultrasound to identify regions, which may be prone to these deleterious haemodynamic forces, which we cannot directly measure in the clinical setting. In addition to informing protocol design for further research into the diagnostic power in USTIR in vascular access surveillance, we hope that this work will encourage further collaboration between clinical and engineering staff, and will aid the translation of knowledge obtained from in-silico research into tools more suited to point of care testing."

Reappraisal of Clinical Trauma Trials: The Critical Impact of Anthropometric Parameters on Fracture Gap Micro Mechanics - Observations from a Simulation Based Study

Roland, M., Diebels, S., Orth, M., Pohlemann, T., Bouillon, B., Tjardes, T., 2023. . Scientific Reports, 13(20450).

Illustration of the simulation concept of clinical trauma trials (CC BY 4.0)

Illustration of the simulation concept and the underlying in silico population: (A) and (B) are showing the coronal and the sagittal representation; (C) is one slice of the image stack in axial direction showing the two-rod calibration phantom; (D) shows the geometrical model of avatar 0, the result of the image processing steps; (E) illustrates the forces acting on the knee joint during a step forward. The data is taken from the OrthoLoad database referenced to the patient with the ID ¡°K8L¡±34. The points in time S1 to S5 are the selected landmarks for the simulation workflow. (F) Shows one model for the mechanobiological regulations based on mechanical quantities; for each FE simulation, the relevant strain quantities for each FE tetrahedral mesh cell were evaluated and assigned to the plane shown here, adopted from, in analogy to Braun and colleagues and Orth and colleagues (G) Comparison between the generated in silico population and the Destatis Microcensus 2017 data for Germany. The generated trial cohort is within the expected range for all parameters and describes the addressed population group quite accurately. ( by Roland et al. / / Resized from original).

Context

"The evidence base of surgical fracture care is extremely sparse with only few sound RCTs available. It is hypothesized that anthropometric factors relevantly influence mechanical conditions in the fracture gap, thereby interfering with the mechanoinduction of fracture healing. Development of a finite element model of a tibia fracture, which is the basis of an in silico population (n?=?300) by systematic variation of anthropometric parameters. Simulations of the stance phase and correlation between anthropometric parameters and the mechanical stimulus in the fracture gap. Analysis of the influence of anthropometric parameters on statistical dispersion between in silico trial cohorts with respect to the probability to generate two, with respect to anthropometric parameters statistically different trial cohorts, given the same power assumptions. The mechanical impact in the fracture gap correlates with anthropometric parameters; confirming the hypothesis that anthropometric factors are a relevant entity. 

On a cohort level simulation of a fracture trial showed that given an adequate power the principle of randomization successfully levels out the impact of anthropometric factors. From a clinical perspective these group sizes are difficult to achieve, especially when considering that the trials takes advantage of a ¡°laboratory approach¡±, i.e. the fracture type has not been varied, such that in real world trials the cohort size have to be even larger to level out the different configurations of fractures gaps. Anthropometric parameters have a significant impact on the fracture gap mechanics. The cohort sizes necessary to level out this effect are difficult or unrealistic to achieve in RCTs, which is the reason for sparse evidence in orthotrauma. New approaches to clinical trials taking advantage of modelling and simulation techniques need to be developed and explored."

Use of Simpleware Software

"Image processing with Simpleware ScanIP (Synopsys, Mountain View, CA, USA) followed a five-step workflow:

  1. Segmentation of four masks: (a) intramedullary nail, (b) bone, (c) fracture gap, i.e., the space between the cortical edges of the fractured bone, and (d) callus area, i.e., newly formed bone around but not in the fracture gap (Fig. 1D) via adaptive thresholding w.r.t the calibration phantom, supplemented by a morphological close filter with isotropic values (two pixels in every spatial direction) for the fracture gap and the callus area.
  2. Mask smoothing (recursive anisotropic Gaussian filter) with the following values: fracture gap and callus area mask (one pixel in x- and y-direction, i.e., the image plane and two pixels in the z-direction), intramedullary nail and the bone mask (two pixels in x- and y-direction, and three pixels in the z-direction).
  3. Island removal for each mask combined with a cavity fill and a fill gaps procedure with priority order.
  4. Visual control of segmentation results (TT) to ensure that all physiologically and mechanically relevant areas of the fracture and the newly formed bone are appropriately mapped (Fig. 1D).
  5. Generation of the FE meshes for the simulation (Abaqus, Dassault Syst¨¨mes, Velizy-Villacoublay, France) using quadratic finite elements (C3D10, ten-node tetrahedral element with four integration points) with adaptive mesh resolution for each mask. Homogenous material parameters for the implant (medical titanium alloy), the fracture gap and callus area (both modelled as initially connective tissue) were taken from the literature.
  6. Homogeneous material parameters based on the Hounsfield units with respect to the calibration phantom were chosen for each, cortical and trabecular bone. Thus, the method demonstrated by Trabelsi and colleagues for the femur was adapted for use on the tibia. After calibrating the grayscale values, the density-modulus relationship for the tibia given by Rho and colleagues was used to assign every mesh cell with an isotropic material model.

This sequence was performed on the real clinical image data, partly manually, partly automated, and reviewed by the authors after each step. After that, the workflow could be automated via scripting in the Simpleware ScanIP software for the generation of the n=300 avatars."

Outcomes and Impact

"The present analysis ends up in a paradox. From the perspective of the individuum, anthropometric parameters do have a relevant impact on fracture gap mechanics. In terms of trial cohorts this effect can be compensated for by increasing the size of trial cohorts, i.e. power calculation of RCTs works.

The discussion on whether randomised trials are the tools of choice to generate clinically meaningful evidence in orthopaedic trauma surgery is long-standing, as there are only few fracture RCTs, with an even lesser number successfully recruiting the scheduled number of patients, and many never getting published. Notably, the implementation of supportive infrastructure, e.g. centres for clinical studies, in many places, did not increase the number of successful fracture RCTs.

Given the fact that in this analysis, for proof of concept purposes, a single fracture was used, the size of trial cohorts naturally increases in a real-world trial with fracture patters showing morphological variability, not to speak about the systematic implementation of anthropometric variables. The achievement of adequate sample sizes and hence statistical power is a notorious issue in real world fracture trials. Farrow et al. report on 25 orthopaedic trials published in high impact journals. More than half of these did not meet the estimated sample size for the primary outcome criterion, and only 56% of these studies provided adequate justification for the minimum clinically important difference (MCID) in the population assessed.

With simulation approaches based on routine computed tomography data of osteosynthetically treated fractures coming within reach for clinical routine application, a new type of clinical data becomes available. The observations reported in this study provide insight into the mechanical conditions in the space where mechanobiology is translated to fracture healing, i.e. the fracture gap.

Given these considerations, a new perspective on fractures becomes possible. A meso-scale focused description of a fracture would then understand a fracture as an aggregation of voxels which are subjected to different mechanical loading scenarios depending on the geometry of the fracture, anthropometric measures, the osteosynthesis and patient behaviour. Each voxel that experiences a mechanical loading within the mechanobiological optimal window has the option to proceed to bone formation given the modulating biological factors permitting this.

This new perspective, i.e. thinking fracture care from the fracture gap, paves the way for a fracture classification that focuses on the mechanobiological conditions in the fracture gap, rather than on the preoperative morphology of the fracture. Future research will show whether this results in a restructuring of fracture classification, an additional fracture classification, a tool to guide postoperative weight bearing, or a tool to categorise surgical performance. Apart from applications in clinical medicine the approach developed here might also offer the opportunity to conduct virtual trials to facilitate the financial and ethical aspects of implant development."

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