WO2016102025A1

WO2016102025A1 - Bone implant and a method for its manufacture comprising generating a plurality of fixation configurations

Info

Publication number: WO2016102025A1
Application number: PCT/EP2014/079305
Authority: WO
Inventors: Frederik Gelaude; Peter VANDEN BERGHE
Original assignee: Mobelife N.V.
Priority date: 2014-12-24
Filing date: 2014-12-24
Publication date: 2016-06-30

Abstract

Method for manufacturing an implantable bone implant (310) arranged to at least partially fit on a location on a bone (320) of a patient, wherein the method comprises the steps of: providing a numerical three-dimensional bone model of at least a part of the bone of the patient comprising the location on the bone, providing a numerical three-dimensional implant model of the bone implant, generating a numerical three-dimensional combinational model of the bone implant on the location on the bone on the basis of the bone model and the implant model, generating a plurality of screw configurations on the basis of at least the combinational model, wherein each screw configuration prescribes at least the trajectory and length of at least one screw for fixing the implant to the bone, analyzing each of the generated screw configurations for determining a fixation score which is representative for the measure of implant fixation on the bone for each of the generated screw configurations, selecting the screw configuration having the highest fixation score and designing screw holes in the implant in accordance with said screw configuration and manufacturing the implant.

Description

BONE IMPLANT AND A METHOD FOR ITS MANUFACTURE COMPRISING GENERATING A PLURALITY OF FIXATION CONFIGURATIONS

The present invention relates to a method for manufacturing an implantable bone implant arranged to at least partially fit on a location on a bone of a patient. The invention further relates to an implant manufactured with the method according to the invention.

In 2007, 14.469 patients were treated with a primary total hip prosthesis and more than 1800 patients underwent a revision of their implant. The last 10 years, the amount of planned surgeries (non-traumatic) shows a mean growth of 3.5% per year. The largest growth is found in the population younger than 60 years old. This makes that hip revision surgeries are becoming more prevalent and that the younger patients are more demanding towards the quality/life span of the implant and the surgery.

Standard implants for primary hip arthroplasty, generally do not contain holes for screws in the acetabular components. In standard implants for revision however, extra fixation is necessary and can be achieved using bone screws. These bone screws will be inserted in the predefined holes of the standard implants. The location, direction and the amount of screws used is decided upon during the operation. In custom implants however, the screw configuration can be predetermined during the preoperative planning. For example, a bone quality map gives an indication of the local bone quality, on the basis of which good fixation of screws can be obtained. The planning of the screw configuration and the design and manufacturing process of the implant based thereon, is however a difficult and labour intensive process.

It is therefore a goal of the present invention, amongst other goals, to provide an improved implant manufacturing method, more particularly wherein the screw configuration is defined in an objective way, with preferably less designer-dependency. This goal, amongst other goals, is met by a method according to appended claims 1. More specifically, this goal, amongst other goals, is met by a method for manufacturing a bone implant to be connected to a location on a bone of a patient, preferably to at least partially fit on the location on a bone of a the patient, wherein the method comprises the steps of: - providing a numerical three-dimensional patient model of at least a part of the patient including the location on the bone to which the implant is to be connected;

- generating a plurality of fixation configurations on the basis of the patient model, wherein each fixation configuration prescribes at least the trajectory and length of at least one fixation means for fixing the implant to the bone;

- analysing each of the generated fixation configurations for determining a fixation score which is representative for the measure of implant fixation on the bone for each of the generated fixation configurations;

- selecting the fixation configuration having the highest fixation score and designing the implant to receive the fixation means of said fixation configuration for fixing said implant in accordance with said fixation configuration; and

- manufacturing the implant.

According to the invention, a plurality of fixation configurations is automatically generated, wherein each of the fixation configurations defines the manner in which the implant can be fixed to the bone. A fixation configuration prescribes the manner of fixation of the implant to the bone by at least prescribing for each fixation means, for instance a screw or pin, the trajectory, i.e. the origin and direction of a fixation means, and the length thereof. Other characteristics of the fixation means may however also be included, such as screw type, i.e. tapering or non-tapering, thread characteristics and diameter, which influence the connection. With the automated method, for instance by computer implementation, according to the invention, an optimal fixation configuration for an implant can be efficiently and reliably designed and preferably subsequently manufactured.

The fixation means preferably comprise at least one screw, at least one pin or a combination thereof. The implant may then be designed to have screw holes for receiving the pins or screws in accordance with the fixation configuration. The holes are thereto accordingly oriented and sized.

The plurality of fixation configurations is generated on the basis of a three-dimensional model of the patient, including the bone with which the implant in to connect. According to a preferred embodiment, the step of providing the three-dimensional bone model comprises the step of obtaining an image of the bone and defect therein. Digital patient-specific image information can be provided by any suitable means known in the art, such as for example a computer tomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, an ultrasound scanner, or a combination of Roentgenograms. A summary of medical imaging has been described in

"Fundamentals of Medical imaging", by P. Suetens, Cambridge University Press, 2002. For example, the step of obtaining an image of the bone and the defect therein may for example comprise the steps of obtaining 2D datasets of the bone and reconstructing a 3D virtual bone model from said 2D datasets. Indeed, the first step in a planning is the construction of a 3D virtual model of the bone. This reconstruction starts with sending a patient to a radiologist for scanning, e.g. for a scan that generates medical volumetric data, such as a CT, MRI scan or the like. The output of the scan can be a stack of two-dimensional (2D) slices forming a 3D data set. The output of the scan can be digitally imported into a computer program and may be converted using algorithms known in the field of image processing technology to produce a 3D computer model of a relevant bone. Preferably, a virtual 3D model is constructed from the dataset using a computer program such as Mimics(TM) as supplied by Materialise N.V., Leuven, Belgium. Computer algorithm parameters are based on accuracy studies, as for instance described by Gelaude at al. (2008; Accuracy assessment of CT-based outer surface femur meshes Comput. Aided Surg. 13(4): 188- 199). A more detailed description for making a perfected model is disclosed in U.S. Patent No. 5,768, 134 entitled 'Method for making a perfected medical model on the basis of digital image information of a part of the body'. Once the three-dimensional model of the bone is reconstructed for instance as disclosed in Gelaude et al. (2007; Computer-aided planning of reconstructive surgery of the innominate bone: automated correction proposals Comput. Aided Surg. 12(5): 286-94), the trajectories can for instance be generated on the basis of this model.

The fixation configuration may then be generated by identifying suitable locations for the fixation means, for instance on a location in the patient model where the implant is in contact with the bone and/or on which location on the implant is sufficient room to design and manufacture a means for receiving and guiding fixations means in accordance with the fixation configuration, for instance a screw hole for receiving a screw.

According to a preferred embodiment, the step of generating the fixation configurations includes defining an origin of a trajectory and generating a plurality of trajectories originating from said origin, each having a different trajectory and length. For each possible location for a fixation means, for instance an entry point in the implant in contact with the bone, different trajectories are generated, each having a different direction and length. The resulting trajectories may then, when seen together, have a conical distribution with the entry point located on the tip of the cone.

In order to prevent that fixation means intersect, trajectories that intersect, or that extend to close to each other, are preferably excluded in a fixation configuration. In order to decrease the number of possible fixation configurations in terms of possible locations for the fixation means to decrease the needed computational power while still providing reliable fixation configurations, it is preferred when in the generation process use is made of predetermined areas which are preferred for receiving fixation means. This limits the number of possible trajectories and therewith the number of trajectories as a whole. The preferred locations are preferably defined in terms of fixation regions. For a particular type of implant on a bone, different fixation regions may be identified in which it is preferred that fixation means will be located. Therefore, a further preferred embodiment of the method further comprises the steps of:

- providing a representative model which is representative of at least the part of the patient including the location on the bone to which the implant is to be connected, wherein the representative model defines a plurality of fixation regions which prescribe regions on the bone for fixation;

fitting the representative model on the patient model for identifying the corresponding fixation regions in the patient model,

wherein the step of generating the fixation configurations comprises defining at least the trajectories of the fixation means on the basis of the fixation regions.

Preferably, the representative model or template model is fitted on the patient model, such that the fixation regions and the possible locations of the means for receiving and guiding the fixation means in the implant can be efficiently determined.

A further limitation of the number of possible fixation configuration, while making use of the experience in the design of earlier implants, can be achieved by prescribing the preferred number of fixation means for a particular fixation region. If it is for instance known that it is sufficient for a particular flange of an implant to be connected with only two screws as fixation means in a particular region of the bone, the method can be limited to generate only fixation configurations having two fixation means, or perhaps less, for that given fixation region. Therefore, according to a preferred embodiment, the representative model further defines a number of fixation means per fixation region, preferably for each fixation region, wherein the step of generating the fixation configurations includes generating a number of trajectories in each fixation region in accordance with the predefined number of fixation means per fixation region.

To prevent fixation means intersecting parts of the bone or surrounding tissues, it is preferred if the step of generating the trajectories includes a filtering step of filtering out trajectories intersecting, or extending close to, predefined excluded regions. Similar to the fixation regions as defined above, it is hereby preferred if these excluded zones are defined in a representative or template model such that these zones can be efficiently defined and perhaps updated in the method according to the invention. The method therefore preferably comprises the steps of:

- providing a representative model which is representative of at least the part of the patient including the location to which the device is to be connected, wherein the representative model defines the excluded regions;

fitting the representative model on the patient model for identifying the corresponding excluded regions in the patient model,

wherein the step of filtering is based on the identified excluded regions in the patient model.

According to a preferred embodiment, the representative model comprises a Statistical Shape Model (SSM). For creating such a SSM, a model set of healthy bones, (e.g., hemi-pelvises) may be collected. For example, a database of images of healthy bones may be generated by taking images of patients with healthy bones. In some embodiments, the database may include both male and female patients. The database may be generated to account for natural variations in bones among different groups of individuals such as by age, gender, race, etc. The images may then be segmented into segmentation masks. For example, segmentation of volumetric images in the form of CT-scans may be performed using Mimics software made by Materialise NV, Belgium as mentioned above for the generation of the thee-dimensional models. From the segmentation masks, a triangular mesh may be calculated, for example, using the Marching Cubes algorithm. The triangular meshes may be remeshed, for example, in 3-Matic software by Materialise NV, Belgium to obtain a smooth and uniform triangulation. Preferably, to create an SSM that captures shape variations of bones, all entities of the model set need to have corresponding points. For triangular meshes, this means that vertices on similar anatomic regions need to be corresponding and that the number of vertices in each mesh is equal (i.e., the corresponding point problem). After fulfilling these two conditions an SSM is calculated using principal component analysis ("PC A"). In some embodiments, the corresponding point problem is solved using the template based method. The method comprises registering a template (e.g., one data entry) to all the meshes of the dataset. The transformed template meshes are then used directly to build up the data matrix for PCA. The registration may be done using a combination of manually indicated anatomical features (e.g., landmarks and ridges) and an iterative nonlinear morphing algorithm based on a thin plate splines (TPS) kernel. In some embodiments, the SSM may be directly fitted to the healthy parts of the patient's bone. The parameters of the SSM may therefore be varied to minimize the distance from the patient's bone to the sample of the SSM. The idea behind this approach is that the remaining healthy parts of the patient's bone are predictors for the missing anatomical parts. In some embodiments, before fitting the SSM to the bone anatomy, a defect part of the bone may be manually cut out or automatically detected. Disregarding the defect part guarantees that the shape of the SSM may be fitted as close as possible to the remaining healthy parts of the bone. For example, in some embodiments, a rigid iterative closest point registration (ICP) with the mean SSM-shape is used to initialize the SSM-fit using the healthy parts of the patient's bone. Afterwards, in some embodiments, the fitting process is performed, which optimizes the distance from each point in the patient's bone to the sample of the SSM by varying the different modes of variation one by one. For each mode of variation, a bisection algorithm is used to find the minimum distance from the patient's bone to the SSM-sample. After calculating the minimum distance of each mode of variation, a rigid ICP registration optimizes the translation and rotation of the SSM-fit.

The plurality of generated fixation configurations are subsequently analysed to select from the plurality of fixation configurations the fixation configuration having the best characteristics in terms of fixing the implant to the bone. Hereto, a fixation score is calculated for each of the different fixation configurations, which allows efficient and reliable comparison of the different fixation configurations. The fixation score is representative for the measure of implant fixation on the bone for a fixation configuration. In other words, the score is a quantitative measure of a particular fixation configuration of its capability to fixate the implant to the bone and prevent separation of the implant from the bone, for instance in terms of pull-out strength or displacement under typical loading conditions. The step of analysing may comprise evaluating the fixation configurations in a numerical simulation that includes a complete biomechanical evaluation that is based on an FEA, patient-specific muscle forces (MSM), patient-specific geometry, and patient- specific material properties.

A preferred embodiment of the method according to the invention further comprises the step of providing bone density data of at least the part of the bone of the patient comprising the location on the bone and wherein the in step of analysing, the fixation score is at least partly based on the bone density in the location of the fixation means of the fixation configuration. The bone density data may for instance be obtained using a DEXA-scan or may be obtained from another medical imaging process. Preferably, during the process for generating the three-dimensional model of the bone, the local bone density values are also included in the model. Particularly reliable scoring is obtained if the fixation score is at least partly based on the sum of the values for the local bone densities along the lengths of each trajectory in a fixation configuration. The values for the bone density on the outer surface or mantle of a fixation means are hereto summed, such that the total density of the bone surrounding the fixation means in implanted situation is known.

A further preferred embodiment of the method further comprises the steps of:

providing a numerical three-dimensional implant model of the implant;

generating a numerical three-dimensional combinational model of the implant and the patient on the basis of the patient model and the implant model,

wherein the fixation configurations are generated on the basis of the combinational model. The combinational model may be generated from for instance the separate models of the bone and the implant. For customized implants, such a model may be readily available and the design of this implant may for instance be based on the three-dimensional numerical model of the bone. For obtaining a reliable fixation score, analysing preferably comprises numerically analysing the combinational model, for instance using Finite Elements Analysis, wherein the fixation score is at least partly based on a calculated property in said numerical analysis, such as displacement on an applied load. Based on this model, the step of analysing may further comprise generating a spring model, wherein each of the fixation means in a fixation configuration is characterized by a spring having a stiffness based on the fixation characteristics of said fixation means and wherein the fixation score is at least partly based on the displacement of the implant on an applied load. The spring constants of the spring in the model may for instance be based on local FEA of the fixation means such as a screw in the bone, fixation means characteristics such as shape, material and possibly threading in case of a screw. It is also possible that the spring constants are at least partly based on the density of the bone along the trajectories of the different fixation means, as mentioned above.

After selection of the most suitable fixation configurations form the plurality of generated fixation configurations, the implant can be designed such that the implant is suitable to receive the fixation means in the manner as prescribed by the selected fixation configuration. To this end, for instance suitable holes for screws or pins having an orientation along the trajectories of the fixation means in the fixation configuration are designed and preferably subsequently manufactured. It should be noted that the invention is not limited to a particular type of implant. The invention can be applied to customized implants, which are customized to fit accurately on the location of the bone, or the implant may be of a generic type. It is even possible that the implant comprises a plurality of parts, for instance in the form of an implant and a guide for guiding the implant to the location, wherein the fixation configuration for the combination of the implant and the guide can efficiently be designed with the method according to the invention. Also the term implant can, according to a preferred embodiment, be interpreted broadly. It is for instance not necessary that the implant is arranged for direct contact with the bone. It is possible that the implant is arranged to be connected with the bone with suitable fixation means, without actually contacting said bone. It is further possible that the invention is applied to design devices to contact other parts than bone, for instance soft tissue. It is further contemplated to use the method according to the invention for designing external devices, i.e. devices which are not implanted as a whole of which the fixation means are arranged to contact the patient, in particular the bone. To be able to reliably and accurately manufacture the implant in accordance with the design thereof, the step of manufacturing preferably comprises using a three-dimensional printing technique, also referred to as rapid manufacturing technique, layered manufacturing technique, additive manufacturing technique or material deposition manufacturing technique. Rapid manufacturing includes all techniques whereby an object is built layer by layer or point per point by adding or hardening material (also called free-form manufacturing). The best known techniques of this type are stereolithography and related techniques, whereby for example a basin with liquid synthetic material is selectively cured layer by layer by means of a computer-controlled electromagnetic beam; selective laser sintering, whereby powder particles are sintered by means of an electromagnetic beam or are welded together according to a specific pattern; fused deposition modelling, whereby a synthetic material is fused and is stacked according to a line pattern;

laminated object manufacturing, whereby layers of adhesive -coated paper, plastic, or metal laminates are successively glued together and cut to shape with a knife or laser cutter; or electron beam melting, whereby metal powder is melted layer per layer with an electron beam in a high vacuum.

In particular embodiments, Rapid Prototyping and Manufacturing (RP&M) techniques are used for manufacturing the implant of the invention. Rapid Prototyping and Manufacturing (RP&M) can be defined as a group of techniques used to quickly fabricate a physical model of an object typically using three-dimensional (3-D) computer aided design (CAD) data of the object. Currently, a multitude of Rapid Prototyping techniques is available, including stereo lithography (SLA), Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), foil-based techniques, etc. A common feature of these techniques is that objects are typically built layer by layer. Stereo lithography (SLA), presently the most common RP&M technique, utilizes a vat of liquid photopolymer "resin" to build an object a layer at a time. On each layer, an electromagnetic ray, e.g. one or several laser beams which are computer-controlled, traces a specific pattern on the surface of the liquid resin that is defined by the two- dimensional cross-sections of the object to be formed. Exposure to the electromagnetic ray cures, or, solidifies the pattern traced on the resin and adheres it to the layer below. After a coat had been polymerized, the platform descends by a single layer thickness and a subsequent layer pattern is traced, adhering to the previous layer. A complete 3-D object is formed by this process.

Selective laser sintering (SLS) uses a high power laser or another focused heat source to sinter or weld small particles of plastic, metal, or ceramic powders into a mass representing the 3- dimensional object to be formed.

Fused deposition modeling (FDM) and related techniques make use of a temporary transition from a solid material to a liquid state, usually due to heating. The material is driven through an extrusion nozzle in a controlled way and deposited in the required place as described among others in U.S. Pat. No. 5.141.680.

Foil-based techniques fix coats to one another by means of gluing or photo polymerization or other techniques and cut the object from these coats or polymerize the object. Such a technique is described in U.S. Pat. No. 5.192.539.

Typically RP&M techniques start from a digital representation of the 3-D object to be formed, in this case the design of the implant. Generally, the digital representation is sliced into a series of cross-sectional layers which can be overlaid to form the object as a whole. The RP&M apparatus uses this data for building the object on a layer-by-layer basis. The cross-sectional data representing the layer data of the 3-D object may be generated using a computer system and computer aided design and manufacturing (CAD/CAM) software.

The implant of the invention may be manufactured in different materials. Typically, only materials that are biocompatible (e.g. USP class VI compatible) with the human body are taken into account. Preferably the implant is formed from a heat-tolerable material allowing it to tolerate high- temperature sterilization. In the case SLS is used as a RP&M technique, the implant may be fabricated from a polyamide such as PA 2200 as supplied by EOS, Munich, Germany or any other material known by those skilled in the art may also be used.

A further preferred embodiment of the method further comprises the step of designing and manufacturing a guide arranged to be connected to the implant, wherein the guide is arranged to interact with the implant for guiding at least one of the fixation means along the trajectory of said selected fixation configuration. The guide preferably has a unique fit on the implant, such that the trajectories, for instance for drilling or inserting the fixation means, are clear to the user during surgery.

The invention further relates to an implant manufactured by a method according to the invention. The invention further relates to a kit of parts comprising an implant according to the invention and a plurality of screws having lengths as prescribed by the fixation configuration having the highest fixation score.

The present invention is further illustrated by the following Figures, which show a preferred embodiment of the method according to the invention, and are not intended to limit the scope of the invention in any way, wherein:

Figure 1 is a schematic overview of the screw optimization method;

Figure 2 shows a SSM reconstruction of a patient's bone; - Figure 3a and b schematically show the screw regions in accordance with the invention;

Figure 4a and 4b illustrate the screw trajectories for the ilium cup region (4a) and for the pubis cup region (4b); - Figures 5a and 5b illustration the danger zones;

Figures 6a and 6b show two examples of generated screw configurations for the ilium group; Figure 7 shows an example of determining the fixation score by quantification of the bone density around the screw mantles of the screws; and

Figure 8 shows an example of determining the fixation score by using a spring model.

A schematic view of the automatic screw optimization method is shown in Figure 1. The input for the method is the virtual anatomical reconstruction using an SSM (Statistical Shape Model), the geometry of the patient's bone and the CT-data of the patient. In the generation step, possible screw trajectories are calculated for each screw region and several screw configurations are generated. A screw configuration is defined as a complete configuration of screws in a certain position and direction and of a certain length. In the evaluation step, the different screw trajectories are evaluated with parameters that influence screw fixation, to end up with one score per screw configuration. This score will make it possible to compare different screw configurations and to optimize for total fixation of the implant. The optimization is actually an interaction between generation of screw configurations and their evaluations. The output of the optimization is one or multiple optimal screw configurations.

The first step of the screw placement method is to define possible regions where screws can enter/leave the bone, in the case of the pelvis, cup regions (ilium, pubis, ischium) and flange regions (ilium, pubis, ischium) exist. These regions can be easily transferred to the patient's bone to identify the screw regions are shown in figure 3. For an efficient and automated process, these regions need to be automatically transferable from a model to the patient's defect bone. An SSM is in this example used as a template model, but any model that has the ability to transfer regions from a template bone to the patient's bone is possible. Figure 2 illustrates the virtual reconstruction of a patient's defect bone using an SSM. From left to right: the patient's defect bone, the non- defect parts of the patient's defect bone, the SSM fitted onto the bone and a post-processing operation to improve the fit. The screw regions where screws can possibly enter/leave the bone annotated on the SSM are shown in figure 3. From the different screw regions, all possible screw trajectories can be generated. While generating the trajectories, several constraints can be set to filter out unwanted trajectories. For cup screw trajectories for example, a constraint is defined on the minimum and maximum screw length as well as on the distance from the screw trajectory to the outer bone surface (to prevent screws to damage the cortex). For flange screws, a constraint is defined on the minimum screw length and bi- cortical fixation is imposed. Figure 5 shows all possible screw trajectories for the ilium cup region (5a) and the pubis cup region (5b), wherein each trajectory is represented by a line, wherein colour represents the maximum length of a screw in that direction. The next step of the method is to filter out screw trajectories that intersect or come close to danger zones. These danger zones can be blood vessels, nerves or other anatomical structures where screw placement is dangerous or impossible. Also these danger zones may be included in the SSM. Figure 5 shows that screw trajectories which are too close to danger zones are removed. In figure 5a, all trajectories are still available. When defining a danger region (around the ischial nerve, on the left), the algorithm will remove all trajectories that are too close to this danger region, which results in the trajectories as shown in figure 5b.

Based on the screw trajectories of different regions, screw configurations are generated. Screw regions that can influence each other, will be grouped for the generation step. In this example, this means that the cup ilium and the flange ilium screw region will be grouped (same for ischium and pubis). In the grouped regions, the algorithm will select a random screw trajectory to start from. Choosing one trajectory is free of constraints. The choice of the next trajectory is also random, but it excludes trajectories that cross within a threshold distance from the previously defined screw(s). The end result of this step is a number of screw trajectories that stay within a safe distance from each other. Figure 6 shows two generated configurations for the ilium group.

In a next step, the screw configurations are evaluated. The goal of the evaluation step is to quantify the total 'amount' of fixation of a certain screw configuration, for instance by determined a fixation score. This quantification is necessary to compare the quality of different screw configurations and to choose one configuration over the other.

A possible evaluation may be based on the bone density in the area surrounding the screws in the screw configuration. Figure 7 shows a visualization of the bone density measurement along each screw mantle for a screw configuration. This data may for instance be obtained from a DEXA- scan. Summing the bone density measurement for each of the screws in a screw configuration may be used as fixation score.

As an alternative, different parameters may be assigned to the different screws which together determine the score. With each parameter calculated for each screw, a fixation characterization can be made for each screw using a weighting function that takes into account the importance of each parameter. The weighting function is dependent on the bone type and should be defined using representative experiments (real or via numerical simulations (e.g. FEA)).

A next step may be to combine the different fixation characterizations of each screw into a general fixation quality measure, i.e. the fixation score. The calculation for the combination of the different screw contributions can have different forms, from very simple to very complex. A simple form would be just to sum the contributions of each screw to become the final fixation measure.

Consequently, more screws and screws that have a better individual fixation would provide a higher overall fixation. A more complex form would be to take into account the directions of the individual screws together with the individual fixation characterizations.

An example of this would be a spring model of the screw configuration. The displacement of the implant design can be estimated by defining a spring model based on the simulated fit of the implant design to the bone of the patient. In this model, each screw is characterized by a spring located on the screw head position with a stiffness value k. This stiffness may be based on parameters influencing the pull-out strength of screws such as density of the bone, bi-cortical or uni-cortical configuration, screw diameter, screw length and thread design. This individual stiffness will be higher when the individual fixation characterization of the screw is higher. If a force is applied on the spring model, the micro-displacement can be calculated that the force induces. This micro-displacement will then represent the total fixation quality of the screw configuration. A higher micro-displacement means a lower fixation quality.

A schematic of an example of such a spring model is shown in Figure 8. As shown, the spring model 300 includes parameters for the shape and position of the implant 310 with respect to the bone 320 of the patient, the shape of the bone 320 of the patient, and the screw(s) 330 (or other fixation elements) that couple (e.g., connect) the implant 310 to the bone 320 of the patient.

Further, contact points 340 are shown where the implant 310 comes into contact with the bone 320. Each contact point 340 can be characterized as a spring that will resist compression (i.e., a force that moves the implant 310 and bone 320 together), but has no resistance to tensile forces (i.e., a force that moves the implant 310 and the bone 320 apart) as there is no force applied by the bone 320 on the implant 310 at the contact point 340 when forces move them apart. In some embodiments, there may be bone ingrowth near the contact point 340 that provides some resistance to tensile forces and the contact point 340 may be characterized accordingly. Further, each screw 330 can be characterized as a spring that will resist tensile forces (i.e., a force that moves the implant 310 and the bone 320 apart), but has no resistance to compression (i.e., a force that moves the implant 310 and bone 320 together) as the screw 330 would push out a screw hole in the implant 310 so no force is applied by the bone 320 on the implant 310 at the screw 330 when forces move them together. The stiffness of each spring (e.g., contact point 340 and/or screw 330) can be characterized by an individual spring constant k, which may be unique to each spring. In particular, the stiffness of the spring is a characterization of the local resistance to indentation for contact points 340, and to pull-out for screws 330. Some springs may have the same k value. Based on these k values and the other characteristics of the spring model 300 described herein, the displacement of the implant 310 with respect to the bone 320 can be estimated/calculated for any given force vector G 360 applied to the implant 310 as described herein.

The characterization of the spring constant k may be performed using experimental data of biomechanical bone tests. In some embodiments, the spring constant k may be calculated based on measured patient-specific properties (e.g., bone thickness, cortical thickness, etc.) local to the area of implantation of the implant 310 and/or parameter values of parameters of the implant 310. In some embodiments, the spring constant k of a given screw 330 is calculated based at least in part on one or more of the parameter values of the following parameters of the implant 310: cortical thickness, trabecular thickness, trabecular young's modulus, cortical young's modulus, screw length, screw diameter, screw position, and screw orientation. In some embodiments, the spring constant k of a given contact point 340 is calculated based at least in part on one or more of the parameter values of the following parameters of the implant 310: cortical thickness, trabecular thickness, trabecular young's modulus, and cortical young's modulus.

The calculation of the spring constant k based on the patient-specific properties and implant parameters may in some embodiments be performed using finite element analysis (FEA) or be based on the bone density along a screw trajectory as described above.

The displacement of the implant 310 in the spring model 300 can be calculated using the following equations and the below described iterative process: = K^~1 - G

W

wherein:

u is the displacement of the implant 310;

K is the stiffness matrix of the spring model 300;

kj is the spring constant of the spring i of the spring model 300;

Wj is the moment and force direction vector of the spring i of the spring model 300;

G is an applied force on the spring model 300;

Pi is the position of the spring i (e.g., in an xyz axis); and

n_; is the force direction vector of the spring i.

According to the above equations, the total displacement u of the implant 310 is calculated through a matrix multiplication of the inverse stiffness-matrix K ¹ and the applied force G 360. For each applied G 360 (e.g., each force the implant 310 is expected to have applied during normal operation after implantation in the patient), the displacement u can be calculated, but this induces spring tension/compression in the model that cannot happen in reality as screws 330 cannot have compression force and contact points 340 cannot have tensile force as discussed herein. Therefore, the displacement u for a given G 360 is iteratively calculated using the above equations until all springs i have allowed/possible tension/compression. To ensure that all springs i have

allowed/possible tension/compression, the elongation/shortening of each spring i is calculated and assessed to verify if the elongation/shortening is allowed for the type of spring (i.e., contact points 340 cannot be elongated and screws 330 cannot be shortened). For each impossible spring i, the spring constant k_; is set to zero. This produces a new K matrix and therefore new calculations for elongation/shortening of each spring i. This process is iterated, as long as there is any calculated elongation/shortening of any spring that is not allowed. When the elongation/shortening calculations for each spring is found to be allowed, the process ends and the resulting value for displacement u of the implant 310 for the given G 360 according to the values found through the iterative process is considered the estimated displacement of the screw configuration. As discussed above, the estimated displacement can be used as a fixation score for selecting the most suitable screw configuration for an implant.

The present invention is not limited to the embodiment shown, but extends also to other embodiments falling within the scope of the appended claims.

Claims

Method for manufacturing a bone implant to be connected to a location on a bone of a patient, preferably to at least partially fit on the location on a bone of a the patient, wherein the method comprises the steps of:

- providing a numerical three-dimensional patient model of at least a part of the patient including the location on the bone to which the implant is to be connected;

- manufacturing the implant.

Method according to claim 1, further comprising the steps of:

- fitting the representative model on the patient model for identifying the corresponding fixation regions in the patient model, wherein the step of generating the fixation configurations comprises defining at least the trajectories of the fixation means on the basis of the fixation regions.

Method according to claim 2, wherein the representative model further defines a number of fixation means per fixation region, preferably for each fixation region, wherein the step of generating the fixation configurations includes generating a number of trajectories in each fixation region in accordance with the predefined number of fixation means per fixation region.

Method according to any of the preceding claims, wherein the step of generating the trajectories includes a filtering step of filtering out trajectories intersecting predefined excluded regions.

Method according to claim 4, further comprising the steps of:

- fitting the representative model on the patient model for identifying the corresponding excluded regions in the patient model,

Method according to at least claims 2 or 5, wherein the representative model comprises a Statistical Shape Model.

Method according to any of the preceding claims, wherein the step of generating the fixation configurations includes defining an origin of a trajectory and generating a plurality of trajectories originating from said origin, each having a different trajectory and length.

Method according to any of the preceding claims, wherein the fixation means comprise at least one screw, at least one pin or a combination thereof.

Method according to any of the preceding claims, further comprising the step of providing bone density data of at least the part of the bone of the patient including the location to which the device is to be connected and wherein in the step of analysing, the fixation score is at least partly based on the sum of the values for the local bone densities along the lengths of each trajectory in a fixation configuration.

Method according to any of the preceding claims, further comprising the steps of:

- providing a numerical three-dimensional implant model of the implant;

- generating a numerical three-dimensional combinational model of the implant and the patient on the basis of the patient model and the implant model, wherein the fixation configurations are generated on the basis of the combinational model.

11. Method according to claim 10, wherein the step of analysing comprises numerically analysing the combinational model, for instance using Finite Elements Analysis, wherein the fixation score is at least partly based on a calculated property in said numerical analysis, such as displacement on an applied load.

12. Method according to any of the preceding claims, wherein the step of analysing comprises generating a spring model, wherein each of the fixation means in a fixation configuration is characterized by a spring having a stiffness based on the fixation characteristics of said fixation means and wherein the fixation score is at least partly based on the displacement of the implant on an applied load.

13. Method according to any of the preceding claims, wherein the step of manufacturing

comprises using a three-dimensional printing technique.

14. Method according to any of the preceding claims, further comprising the step of designing and manufacturing a guide arranged to be connected to the implant, wherein the guide is arranged to interact with the implant for guiding at least one of the fixation means along the trajectory of said selected fixation configuration .

15. Implant or guide manufactured by a method according to any of the preceding claims.

16. Kit of parts comprising an implant according to any of the preceding claims and a plurality of fixation means having lengths as prescribed by the fixation configuration having the highest fixation score.