Method and system for optimising orthopaedic treatment

Abstract

A method implemented on an electronic computing device for optimising orthopaedic treatment, the method comprising: obtaining patient information; determining a patient musculoskeletal model and patient native mechanics from the patient information; determining a treatment outcomes field based on surgical parameters and a treatment outcome measures field based on treatment outcome measures; determining a post-operative musculoskeletal model and post- operative mechanics across the treatment outcomes field; constraining the treatment outcome measures field to one or more safe zones of the treatment outcome measures field; mapping the one or more safe zones of the treatment outcome measures field to one or more safe zones of the treatment outcomes field; presenting the one or more safe zones of the treatment outcomes field, and determining an optimised orthopaedic treatment based on the one or more safe zones of the treatment outcomes field.

Description

METHOD AND SYSTEM FOR OPTIMISING ORTHOPAEDIC TREATMENTFIELD

[0001] This relates to a method and a system for optimising orthopaedic treatment.BACKGROUND

[0002] Orthopaedic surgery focuses on treating conditions related to the musculoskeletal system, which includes bones, joints, cartilage, muscles, ligaments, tendons, and nerves. Orthopaedic surgery can involve procedures such as joint replacements, arthroplasty, arthroscopy, spinal fusion, fracture repair, soft tissue repair, and corrective procedures for deformities.

[0003] Soft tissue refers to the non-bony structures in the body, including cartilage, muscles, tendons, ligaments, fascia, nerves, blood vessels, and synovial tissue. These tissues provide support, movement, and protection to the skeletal system and organs, and they play crucial roles in the body's biomechanics and physiological functions.

[0004] Soft tissue balancing is a critical aspect of orthopaedic surgery, particularly in joint replacement procedures such as total knee arthroplasty or total hip arthroplasty. It involves optimizing the tension and alignment of the surrounding soft tissues, including ligaments, tendons, cartilage and muscles, to achieve proper joint stability and function.SUMMARY

[0005] According to a first aspect, there is provided a method implemented on an electronic computing device for optimising orthopaedic treatment, the method comprising: obtaining patient information; determining a patient musculoskeletal model and patient native mechanics from the patient information; determining a treatment outcomes field based on surgical parameters and a treatment outcome measures field based on treatment outcome measures; determining a postoperative musculoskeletal model and post-operative mechanics across the treatment outcomes field; performing soft tissue balancing on the post-operative musculoskeletal model and updating the post-operative musculoskeletal model; constraining the treatment outcome measures field to one or more safe zones of the treatment outcome measures field; mapping the one or more safe zones of the treatment outcome measures field to one or more safe zones of the treatment outcomes field; presenting the one or more safe zones of the treatment outcomes field, anddetermining an optimised orthopaedic treatment based on the one or more safe zones of the treatment outcomes field.

[0006] In an example of the first aspect, the method further comprises intraoperatively updating the optimised orthopaedic treatment based on implant placement information or a morphological change.

[0007] In a further example of the first aspect, the constraining comprises comparing the patient musculoskeletal model with the post-operative musculoskeletal model and comparing the patient native mechanics with the post-operative mechanics.

[0008] In a further example of the first aspect, the method further comprises delivering the optimised orthopaedic treatment.

[0009] In a further example of the first aspect, soft tissues comprise: cartilage, muscles, tendons, ligaments, fascia, fibrous tissue, fat, blood vessels, nerves, and synovial membranes.

[0010] In a further example of the first aspect, performing soft-tissue balancing on the postoperative musculoskeletal model comprises using the patient native mechanics to create virtual markers for the post-operative musculoskeletal model and performing inverse kinematics.

[0011] In a further example of the first aspect, determining a patient musculoskeletal model and patient native mechanics from the patient information comprises estimating the patient native mechanics through shape model fiting.

[0012] In a further example of the first aspect, determining a patient musculoskeletal model and patient native mechanics from the patient information comprises fiting an articulated statistical shape model with embedded soft tissue to raw geometric meshes.

[0013] In a further example of the first aspect, the surgical parameters comprise two or more of changes in centres of rotation, a leg length, changes in leg length, an offset, changes in offset, implant properties, implant orientations, surgical approaches, capsule releases, capsule repairs, bony section locations, soft tissue reatachment locations, and reaming and broach paths.

[0014] In a further example of the first aspect, the changes in centres of rotation comprise a change in a femoral head centre or a change in a hip joint centre.

[0015] In a further example of the first aspect, determining a post-operative musculoskeletal model and post-operative mechanics across the treatment outcomes field comprises simulating day- to-day functions and lifestyle-specific function.

[0016] In a further example of the first aspect, the treatment outcomes measures comprise two or more of muscle weakening risk, muscle overstretching risk, dislocation risk, pain risk, hip-spine mobility score, range of motion, functional improvement, joint contact forces, osteoarthritis risk, implant stability or loosening risk, implant contact forces, implant contact area, risk of injury due to muscle imbalance, neutral align score, surgeon likeability of the plan, regulatory or consensus outcome measures and risks, patient reported outcome measures; and implant wear risk and / or rate.

[0017] In a further example of the first aspect, constraining the treatment outcome measures field to one or more safe zones of the treatment outcome measures field comprises constraining according to a constraint function, wherein the constraint function comprises an absolute constraint independent of the patient native mechanics and a relative constraint dependent on the patient native mechanics.

[0018] In a further example of the first aspect, the method further comprises determining an optimality figure of merit for the one or more safe zones of the treatment outcomes field and ranking the one or more safe zones of the treatment outcomes field by optimality.

[0019] In a further example of the first aspect, determining an optimality figure of merit for the one or more safe zones of the treatment outcomes field is based on one more of an orthopaedic service provider's preferences, a patient's preferences, characteristics of an orthopaedic surgical approach, and characteristics of an orthopaedic surgical delivery system.

[0020] In a further example of the first aspect, presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises recommending a safe zone of the treatment outcomes field based on zone size and optimality.

[0021] In a further example of the first aspect, presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises highlighting a vector of the treatment outcomes field having the largest 2-norm.

[0022] In a further example of the first aspect, presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises informing a user or an orthopaedic system of surgical parameter tolerances of a safe zone of the treatment outcomes field.

[0023] In a further example of the first aspect, presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises presenting interactive 3D visualisations of a patient musculoskeletal model and patient mechanics in a virtual reality, augmented reality, or mixed reality environment.

[0024] In a further example of the first aspect, each of the treatment outcome measures is in the vector form [0, infinity), where the "0" value is representative of the best outcome, and infinity is representative of the process of tending towards a worse outcome.

[0025] In a further example of the first aspect, each of the treatment outcome measures is in the vector form [-1, 1], where the "-1" value is representative of the worst outcome, and the "1" value is representative of the best outcome.

[0026] In a further example of the first aspect, the patient information comprises patient motion data.

[0027] In a further example of the first aspect, the patient information comprises patient anatomical imaging data.

[0028] According to a second aspect, there is provided a method implemented on an electronic computing device for optimising orthopaedic treatment, the method comprising: receiving a musculoskeletal model and a treatment outcomes field of an orthopaedic treatment; intraoperatively triggering an update to the orthopaedic treatment based on implant placement information or a morphological change; updating a musculoskeletal model based on the implant placement information or the morphological change; performing soft tissue balancing on the updated musculoskeletal model and further updating the musculoskeletal model; updating the treatment outcomes field based on the updated musculoskeletal model; updating one or more safe zones of the updated treatment outcomes field; presenting the one or more safe zones of the updated treatment outcomes field; and determining an optimised orthopaedic treatment based on the one or more safe zones of the updated treatment outcomes field.

[0029] In an example of the second aspect, the method further comprises delivering the optimised orthopaedic treatment.

[0030] In a further example of the second aspect, wherein soft tissues comprise: cartilage, muscles, tendons, ligaments, fascia, fibrous tissue, fat, blood vessels, nerves, and synovial membranes.

[0031] In a further example of the second aspect, performing soft tissue balancing on the updated musculoskeletal model comprises using the patient native mechanics to create virtual markers for the updated musculoskeletal model and performing inverse kinematics.

[0032] In a further example of the second aspect, presenting the one or more safe zones of the updated treatment outcomes field comprises recommending a safe zone of the updated treatment outcomes field based on zone size and optimality.

[0033] In a further example of the second aspect, presenting the one or more safe zones of the updated treatment outcomes field comprises informing a user or an orthopaedic system of surgical parameter tolerances of a safe zone of the updated treatment outcomes field.

[0034] According to a third aspect, there is provided a system configured to optimise orthopaedic treatment according to the first aspect or the second aspect, the system comprising: an information storage subsystem; a pre-operative patient function assessment subsystem; an image processing subsystem; a simulation subsystem; a treatment fields subsystem; and a presentation subsystem.

[0035] In an example of the third aspect, the system comprises a surgical delivery subsystem.

[0036] According to a fourth aspect, there is provided a non-transitory computer readable medium comprising instructions which, when executed by one or more processors, cause the one or more processors to optimise orthopaedic treatment according to the first aspect or the second aspect.BRIEF DESCRIPTION

[0037] The description is framed by way of example with reference to the drawings which show certain embodiments. However, these drawings are provided for illustration only, and do not exhaustively set out all embodiments.

[0038] Figure 1 shows an example method of optimising orthopaedic treatment.

[0039] Figure 2 shows a further example method of optimising orthopaedic treatment.

[0040] Figure 3 shows an example system configured to optimise orthopaedic treatment.

[0041] Figure 4 shows an example of estimating and assessing soft tissue function.

[0042] Figure 5 shows a further example of estimating and assessing soft tissue function.

[0043] Figure 6 shows a further example of estimating and assessing soft tissue function.

[0044] Figure 7 shows a further example of estimating and assessing soft tissue function.

[0045] Figure 8 shows an example method of updating an orthopaedic treatment.DETAILED DESCRIPTION

[0046] Orthopaedic surgery including arthroplasty tends to focus on restoring the bone anatomy rather than the function of a joint, losing sight of the purpose of the orthopaedic surgery which is to restore joint functions by achieving one or more of reducing patient pain, improving range of motion, and restoring joint stability. Several challenges exist including:• defining optimal resections (both soft and bony);• identifying the optimal orientation and placement of components to maximise mechanics; and• predicting patient outcomes such as pain.

[0047] The above considerations are all influenced by the soft tissues, eg the body's non-bony tissues. Soft tissues comprise: cartilage, muscles, tendons, ligaments, fascia, fibrous tissue, fat, blood vessels, nerves, and synovial membranes. Soft tissues play a vital role in movement, support, and overall body function. Soft tissue analysis can analyse contributions of forces from the soft tissue and may be used to analyse the effect that resection of different soft tissues has on the mechanics of the joint. As a non-limiting example if certain muscles are resected, how much more load the remaining muscles would experience.

[0048] It is critical to navigate around the soft tissues during surgery. The passive and active forces involving the soft tissue help stabilise the joint and play an important role in the function and mechanics of a joint. If the soft tissues are overstretched or irritated, a patient will experience pain, which is considered an undesirable outcome.

[0049] Understanding the post-operative configuration of the joint and how the changes to the joint will affect the patient's soft tissue mechanics throughout motion is key for identifying the optimal resection, identifying optimal implant orientation and placement, optimising surgical outcomes, and optimising post-operative recovery through rehabilitation. Without knowledge and understanding of the soft tissues, the post-operative configuration of the joint can be difficult to predict due to different soft-tissue tension.

[0050] There is described herein a method of determining safe and optimal combinations of orthopaedic surgical parameters by constraining a treatment outcomes field based on estimated treatment outcomes measures. The estimation of the treatment outcomes measures uses a patient's musculoskeletal model, which may model the effects of soft-tissue balancing. The safe and optimal combinations of orthopaedic surgical parameters may be presented as safe zones to a user (e.g. a patient or a surgeon) or an orthopaedic system for optimising orthopaedic treatment or surgical delivery. A treatment outcomes field can be useful in the short or long-term planning of a patient's orthopaedic treatment. In particular, the treatment outcomes field can help guide decisionmaking through the patient's journey by identifying a preferred pathway between the patient's current state and a target state.

[0051] The methods disclosed herein can be implemented by instructions on a computing device, such as an electronic computing device. These can constitute a computer program that is embodied in various media, including tangible or non-tangible media, transitory or non-transitory media, and can run on any suitable device.Method

[0052] Figure 1 shows an example method 100 of optimising orthopaedic treatment.

[0053] At step 101, patient information is obtained.

[0054] Step 101 can comprise accessing the medical history of a patient. This can be achieved by identifying a source of medical history information, which may be a database and / or the patient themselves. Once the source of medical history information has been identified, the information is accessed and analysed with respect to factors that are relevant to orthopaedic treatment. A factor may be considered relevant if it facilitates an understanding of the patient's overall health status and any implications as far as the potential range of orthopaedic treatment options. Non-limitingly, thiscan include past medical conditions, surgeries, allergies, medications, and relevant family medical history.

[0055] Step 101 can comprise obtaining patient-specific functional data, which is impaired functional data since the patient by definition has an impairment. This may be achieved by conducting a physical examination to assess the patient's range of motion, strength, stability, and any signs of injury or abnormalities in the musculoskeletal (soft tissue) system. This can comprise obtaining patient marker data and utilising motion capture technology. This can also comprise obtaining, processing, and analysing video footage of the patient undergoing a functional movement assessment.

[0056] Step 101 can comprise obtaining patient-specific imaging data, which may be 2D or 3D. This can comprise imaging via one or more of imaging modalities such as X-rays, MRI scans, CT scans, ultrasound, or bone scans.

[0057] Step 101 may be at least partially performed by a trained Al. For example, the trained Al may have undergone targeted training to be able to analyse a patient's medical history and extract the relevant factors. In one example embodiment, it may be a trained Al that preliminarily obtains patient information and presents the obtained patient information to an orthopaedic medical service provider for review.

[0058] At step 103, a patient-specific 3D anatomy is generated based on the obtained patient information. This comprises the generation of a patient-specific musculoskeletal model of the patient's native anatomy, which may be a healthy (unimpaired) native anatomy or a pre-operative native anatomy.

[0059] This model comprises anatomical structures including without limitation bones, joints, muscles and other features having geometric constraints adapted to the patient's anatomy that model the function of the patient's musculoskeletal system.

[0060] Step 103 may be achieved via a combination of image processing and 3D modelling processes and techniques. Step 103 may be implemented using a deep neural network and a set of image filters that are configured to generate 3D models of anatomical structures such as bones, joints, muscles, and other relevant anatomical structures from medical imaging data of the patient. The deep neural network is trained to associate image texture with output 3D voxel volumes of the various anatomical structures. A series of image filters including thresholding, region-growing,Gaussian smoothing, and Marching-cubes may then convert the 3D voxel volumes into 3D triangulated meshes.

[0061] An articulated statistical shape model with embedded soft tissue may be fitted to the raw geometric models or meshes. The articulated statistical shape model morphs a canonical triangulation of each anatomical object to the raw mesh. In this way, anatomical regions and landmarks may be mapped onto the musculoskeletal model, and morphometric measurements such as lengths, angles, areas, and volumes may be taken from the musculoskeletal model.

[0062] Depending on the degree of impairment suffered by the patient, shape model fiting may be required to estimate the patient's native anatomy, or the patient's native anatomy may be inferred from the unimpaired contralateral limb.

[0063] At step 105, simulations of motion are performed using the generated musculoskeletal model to estimate the patient's native (unimpaired) mechanics and function. Non-limitingly, the simulations can relate to gait analysis, muscle activation paterns, and joint contact forces. The simulations can also relate to activities of daily living such as moving from a siting position to a standing position, bending over while seated, geting out of bed, side-stepping, performing a golf swing, or cycling. Simulations of motion may in an example be done using subject specific data, such as motion capture via MOCAP systems that use infrared cameras with markers or even markerless systems (e.g. with gait analysis), or collected from muscle activation paterns (e.g. collected with EMG). Alternatively, simulations could be done with generic motion data collected from a population and averaged. The reference posture of the bones maters here as the motions describe change in the posture of the bones.

[0064] At step 107, a treatment outcomes field is determined. The treatment outcomes field is a multi-dimensional space (or vector field) of all possible or desired combinations of surgical parameters that may be used in simulation. The dimension of the treatment outcomes field is the number of surgical parameters in consideration. The surgical parameters may be selected from a surgical parameter domain. While the surgical parameter domain may comprise a predetermined set of surgical parameters, additional surgical parameters may be added over the course of orthopaedic treatment optimisation, and existing surgical parameters may be removed and / or altered.

[0065] No n-l i mitingly, examples of surgical parameters include:• changes in a centre of rotation e.g. femoral head centre or hip joint centre;• leg length;• changes in leg length;• offset;• changes in offset;• implant properties comprising sizes, types, shapes (e.g. dual mobility or conventional);• implant orientations (stem, cup, etc.);• contracture releases;• surgical approaches, (e.g. anterior, posterior, or lateral);• capsule releases;• capsule repairs;• bony section locations;• soft tissue (e.g. muscle, tendon, or ligament) reattachment locations; and• reaming and broach paths, including the amount of reaming and broaching.

[0066] At step 109, a multi-dimensional treatment outcomes vector (in other words, a multidimensional surgical parameters vector) in the treatment outcomes field is selected for subsequent simulation analysis. This may in a non-limiting example be selected programmatically, e.g. via an iterative algorithm, or if a particular combination of surgical parameters is of interest, then that specific vector can be chosen. A vector could in an example be selected based on certain expected conditions that are selected via filters or assumptions.

[0067] At step 111, the musculoskeletal model reflecting the patient's anatomy is updated.

[0068] Step 111 may comprise adjusting the musculoskeletal model based on the selected multidimensional vector. For example, changes in a centre of rotation, leg length, or offset may be imposed on the musculoskeletal model.

[0069] Step 111 may comprise performing soft tissue balancing on the musculoskeletal model. This may be achieved by using the native mechanics of the patient to create virtual markers for the musculoskeletal model and then performing inverse kinematics with the selected treatment outcomes vector. In one example embodiment, this may also be achieved by performing an inverse rotation of a femur to any change in anteversion.

[0070] In an example embodiment, the selected treatment outcomes vector comprises at least a change in a centre of rotation.

[0071] In an example embodiment, the selected treatment outcomes vector comprises at least a change in a femoral head centre and a change in a hip joint centre.

[0072] In an example embodiment, the selected treatment outcomes vector comprises a change in the amount of femoral offset.

[0073] At step 113, a post-operative reference anatomy is estimated based on the updated musculoskeletal model. This comprises estimating soft tissue properties. In one example embodiment, estimating soft tissue properties comprises estimating a muscle length. In a further example embodiment, estimating soft tissue properties comprises estimating a muscle moment. The muscle length may influence the soft tissue properties (on a force length curve), and similarly the muscle moment may provide an indication of amount of expected force due to the lever arm.

[0074] At step 115, the post-operative reference anatomy is used to simulate motions of the patient post-operation. This allows information about the patient's post-operative mechanics to be estimated. The range of motions simulated may comprise basic, day-to-day functions such as the patient moving from a siting position to a standing position or geting out of bed. The range of motions simulated may comprise lifestyle-specific motions tailored to the particular patient e.g. performing a golf swing. As a part of the simulation of a motion of interest, one or more soft tissue properties e.g. muscle moment generating capacity or muscle length of one or more soft tissue elements may be analysed.

[0075] Steps 109 to 115 may be iterated to form a candidate zone in the treatment outcomes field based on a number of treatment outcomes vectors of interest. This comprises selecting multiple treatment outcomes vectors, updating the musculoskeletal model of the patient accordingly for each of the selected treatment outcomes vector, estimating a post-operative reference anatomy for each of the selected treatment outcomes vector, and simulating motions of the patient's postoperation to obtain an estimation of the patient's post-operative mechanics for each of the selected treatment outcomes vector.

[0076] At step 117, a set of treatment outcome measures specific to the particular patient may be determined from the patient's post-operative anatomy and the patient's post-operative mechanics by way of prediction and / or inference. Treatment outcome measures may be determined across apart or the whole of the candidate zone. Non-limitingly, an example set of treatment outcome measures may comprise:• muscle weakening risk;• muscle overstretching risk;• dislocation risk;• pain risk;• hip-spine mobility score;• range of motion;• functional improvement;• joint contact forces;• osteoarthritis risk;• implant stability / loosening risk;• implant contact forces;• implant contact area;• risk of injury due to muscle imbalance;• neutral align score;• surgeon likeability of the plan;• regulatory / consensus outcome measures and risks;• patient reported outcome measures; and• implant wear risk and / or rate.

[0077] The treatment outcome measures may each be inherently quantitative or may be put in a quantitative form.

[0078] One or more of the treatment outcome measures may be interdependent on another one or more of the treatment outcome measures such that some treatment outcome measures may be derived from others.

[0079] Each of the treatment outcome measures can be normalised, weighted, or otherwise transformed such that all treatment outcome measures in the treatment outcome measures set span the same range to allow for mathematical (statistical) operations to be performed.

[0080] For example, each of the treatment outcome measures may be put in the form [-1, 1], where the "-1" value is representative of the worst outcome and the "1" value is representative ofthe best outcome. For some of the outcome measures, there may be a bell curve or a band of optimal values.

[0081] In a further example, each of the treatment outcome measures may be put in the form [0, infinity), where the "0" value is representative of the best outcome, and infinity is representative of the process of tending towards a worse outcome. This may be a linear or logarithmic scale.

[0082] Step 117 may comprise determining a treatment outcomes measures field such that a set of treatment outcome measures may be in the form of a treatment outcome measures vector existing in the treatment outcomes measures field. The treatment outcomes measures field is a multi-dimensional space of all possible combinations of treatment outcome measures. The dimension of the treatment outcomes measures field is the number of treatment outcome measures in consideration.

[0083] In this way, step 117 may comprise mapping the candidate zone of the treatment outcomes field to a corresponding candidate zone in the treatment outcome measures field. This may be achieved by way of a mapping function comprising the patient's post-operative anatomy and the patient's post-operative mechanics. In a non-limiting example mapping the candidate zone of the treatment outcomes field to a corresponding candidate zone in the treatment outcome measures field could be via a look up table where input parameters (treatment outcomes), following the simulation, produce specific output parameters (treatment outcome measures). It could be mapped using a more complex method e.g. machine learning, neural network and the like.

[0084] At step 119, the candidate zone of the treatment outcome measures field may be constrained according to a constraint function, which may comprise multiple functions. The constraint function may comprise an absolute component and a relative component.

[0085] The absolute constraint comprises limiting the candidate zone according to a prescribed allowable range or threshold for one or more of the treatment outcome measures. As an example, a threshold may be imposed in relation to the muscle overstretching risk of a replaced joint. A part of the candidate zone may fail to meet the threshold requirement, indicating that the corresponding surgical combinations may result in an unacceptably high muscle overstretching risk e.g. due to the post-operative anatomy having one or more muscles having a muscle length or moment that falls outside a safe range.

[0086] In one example embodiment, constraining the candidate zone of the treatment outcome measures field comprises analysing for at least one relevant muscle to determine whether the muscle meets the threshold or range requirement for muscle moment and muscle strength.

[0087] The relative constraint comprises comparing the estimated post-operative mechanics of the patient to the estimated native mechanics of the patient to determine the degree of restored function. It may further be determined whether the degree of restored function meets a threshold e.g. the post-operative function needs to have at least 95% of the native range of motion. This is the relative constraint in the sense that the constraint is dependent on (relative to) the estimated native mechanics of the patient. Native mechanics may be healthy (unimpaired) native mechanics or preoperative native mechanics.

[0088] By applying the constraint function to the candidate zone of the treatment outcome measures field, one or more safe zones of the treatment outcome measures field may be determined at step 119. The one or more safe zones may comprise disjointed safe zones of different sizes.

[0089] In an example embodiment where each of the treatment outcome measures vector is the form [0, infinity), where the "0" value is representative of the best outcome, and infinity is representative of the process of tending towards a worse outcome, a constraint may be achieved according to one or more of the following:• each of the vector values is below a corresponding threshold;• the vector norm is below a threshold;• the maximum value in the vector is below a threshold; and• a weighted function comprising some or all the values is below a threshold.

[0090] Additionally, an optimality figure of merit may be determined for the one or more safe zones and / or the treatment outcome measures vectors making up the one or more safe zones. The figure of merit may be determined by a weighted function comprising some or all the values of one or more treatment outcome measures vectors. A greater optimality value indicates higher optimality than a lower optimality value.

[0091] The figure of merit may take into account one or more of an orthopaedic service provider's preferences (e.g. emphasis on certain treatment outcome measures), a patient's preferences (e.g. risk and functional performance trade-off), characteristics of an orthopaedic surgical approach, andcharacteristics of an orthopaedic surgical delivery system (e.g. the degree of automation in a robotics system)

[0092] The one or more safe zones and the constituent treatment outcome measures vectors may then be ranked by optimality.

[0093] At step 121, one or more safe zones of the treatment outcomes field may be determined by mapping the one or more safe zones of the treatment outcomes measures field to the treatment outcomes field. This may be achieved by way of a mapping function comprising the patient's postoperative anatomy and the patient's post-operative mechanics. This mapping function may be an inverse function of the mapping function of step 117.

[0094] The one or more safe zones of the treatment outcomes field may be ranked by optimality in a similar manner to step 119.

[0095] At step 123, the one or more safe zones of the treatment outcomes field are presented for optimising orthopaedic treatment.

[0096] The one or more safe zones of the treatment outcomes field may be presented to a user e.g. the patient, the orthopaedic service provider, or another interested party at a user interface. The interface may comprise 2D and / or 3D visualisations of the patient's anatomy and mechanics. The interface may comprise visualisations and interactive elements in a virtual reality, augmented reality, or mixed reality environment.

[0097] Figure 4 shows an example of estimating and assessing soft tissue function. In this example, abductor muscles and adductor muscles are analysed for their moment generation capacity and muscle length based on head offset size and cup position.

[0098] Figure 5 shows an example of estimating and assessing soft tissue function. In this example, abductor muscles adductor muscles are analysed for their sensitivity to medialisation and offset. In particular, the change in moment arms of adductors relative to the change in moment arms of abductors is mapped to a region comprising a weak zone, a strong zone, and trade-off zones.

[0099] Figure 6 shows an example of estimating and assessing soft tissue function. In this example, abductor muscles and adductor muscles are analysed for their sensitivity to medialisation and offset. A matrix is shown which summarises how different combinations of cup positions and head offset sizes affect the moment arms and muscle lengths of the abductor and adductor muscles. Thehighlighted grid is the current plan. Each grid has four numbers that represent the change in moment arms and maximum change in muscle lengths of the abductor and adductor muscles throughout motion.

[0100] Figure 7 shows an example of estimating and assessing soft tissue function. In this example, abductor muscles and adductor muscles are analysed for their sensitivity to medialisation and offset. A matrix is shown which summarises how different combinations of cup positions and head offset sizes affect the moment generation capacity (MGC) and muscle length (ML) of the abductor (Ab) and adductor (Ad) muscles. The highlighted cell is the current plan. Each cell has four bars the represent the change in MGC and maximum change in ML and the Ab and the Ad muscles throughout motion.

[0101] The interface may show a graphical representation of optimized muscle function at the ideal hip joint centre, leg length, and offset. The interface may compare percentage changes in functional capacity of six primary hip muscle groups— adductors, abductors, flexors, extensors, internal rotators, and external rotators— relative to the native condition.

[0102] The interface may present a visual representation of an ideal hip joint centre, annotated with offsets along inferior, medial, and posterior axes, each specified as 0 mm deviation. The interface may also present a visual representation of the ideal femoral target configuration, comprising measurements for leg length, lateral offset, and posterior offset.

[0103] The interface may be configured to allow a user to identify the different safe zones including their boundaries and interactively navigate and select a safe zone.

[0104] The interface may comprise an Al guide configured to receive requirements from a user and recommend a best safe zone for treatment. In an example embodiment, the recommendation may be determined heuristically, preferring a large zone size and high optimality values.

[0105] The interface may be configured to allow a user to clearly and timely find the most optimal combination of surgical parameters by highlighting the largest norm, which may be the 2-norm.

[0106] The one or more safe zones of the treatment outcomes field may be presented as a target for surgical delivery systems. The one or more safe zones of the treatment outcomes field may be combined with hip-cup-orientation planning to arrive at a target implant orientation and placement that will optimise patient function. These targets may be represented in a coordinate system suitable for implementation in surgical navigation systems and robotics.

[0107] For a safe zone, a user or an orthopaedic system may be informed of the surgical parameter tolerances of the safe zone i.e. the multi-dimensional boundary of the safe zone and the associated margin of error for delivery of treatment.

[0108] At step 125, an optimised orthopaedic treatment may be determined based on the one or more safe zones of the treatment outcomes field. In an example embodiment, this comprises collaborative decision-making involving the patient, the surgeon, and Al assistance.

[0109] At step 127, the optimised orthopaedic treatment may be delivered to the patient via a surgeon, a robotics surgical delivery subsystem, or a combination of both.

[0110] The method 100 may be integrated into a long-term orthopaedic treatment plan for a patient. In particular, the treatment outcomes field can help guide decision-making through the patient's journey. Figure 2 shows an example method 200 for optimising orthopaedic treatment.

[0111] At step 201, patient information is obtained. This may be substantially similar to step 101 of method 100.

[0112] Steps 203 and 205 determine a treatment outcomes field. These steps may be substantially similar to steps 103, 105, and 107 of method 100. Step 203 may comprise updating the treatment outcomes field with the latest information about the patient.

[0113] The treatment outcomes field 206 is passed as input to a state locator 208 and a treatment outcomes field explorer 210.

[0114] At step 209, the state locator 208 determines a location in the treatment outcomes field corresponding to the patient's current state.

[0115] At step 211, the state locator 208 determines a target state and a location in the treatment outcomes field corresponding to the target state. The surgeon and the patient 212 can influence the determination of the target state via the treatment outcomes explorer 210.

[0116] At step 213, a treatment decision is determined for reaching the target state from the current state. For example, there may be specific cuts and implant decisions such as placements that need to be performed in order to achieve a specific soft tissue outcome that is being targeted. However, the surgeon may not be exact with this delivery. In an example exact cuts and placement may be measured intraoperatively and used to update the current state. Once the treatmentoutcomes field is somewhat determined, the target state may change since the exact targeted outcome is no longer possible. The surgeon may receive an updated outcomes field during the operation that helps them to maximise the possible outcome. The decision leads to the delivery of treatment 217 via the treatment path 215. The outcome of the delivery 217 may then be fed back into a further iteration of the method 200 e.g. for updating patient information at 210 or for providing treatment history 219 to the state locator 208.

[0117] Figure 8 shows an example method 400 for updating an orthopaedic treatment. The example method 400 may occur as a part of the method 100 or the method 200 for optimising orthopaedic treatment.

[0118] At step 800, information relating to an existing orthopaedic treatment is received. This information may relate to the plan and / or delivery of the treatment and may comprise one or more of a musculoskeletal model, simulation results, a treatment outcomes field with safe zones, and a treatment outcome measures field with safe zones.

[0119] At step 801, an update to an orthopaedic treatment is triggered. The update may be triggered preoperatively (during planning of the orthopaedic treatment), intraoperatively (during delivery of the orthopaedic treatment), and / or postoperatively (during assessment and rehabilitation).

[0120] As an example, a preoperative update may be triggered by determining information that can be used to assist the planning of placement of an implant. Broadly speaking, the information may comprise implant placement information and / or information relating to one or more morphological changes.

[0121] As an example, an intraoperative update may be triggered by determining, substantially in real-time, the position and the orientation of an implant or a part of an implant that has been placed.

[0122] This placement information may be provided substantially in real-time by a robotics surgical delivery subsystem. Alternatively or additionally, an optical camera may be used to obtain the placement information. The optical camera may be assisted by reflective markers. Further, a functional test may be performed intra-operatively e.g. a range of motion test, a shuck test, or another test that measures motion or forces relating to the joint operated on to obtain placement information that can trigger an intraoperative update. Further, a medical scan such as an X-ray scan may be performed intraoperatively to obtain implant placement information.

[0123] As an example, a postoperative update may be triggered by determining placement information of an implant. This placement information may be obtained from CT and / or X-ray scans and / or functional tests.

[0124] Generally speaking, the update may be triggered by determining updated information relating to morphology e.g. as a result of bone cuts, reaming, or soft-tissue cuts and / or releases.

[0125] At step 802, a musculoskeletal model is updated based on inputs including but limited to:• positional and orientational information relating to one or more implants, whether partially or fully placed;• any coordinate systems used in the planning, delivery, or assessment of the orthopaedic treatment;• positional, depth, and orientational information relating to one or more of cuts, reaming, and releases;• the amount of soft-tissue release; and• functional information including measured forces and motion.

[0126] As an example, an existing musculoskeletal model may be updated with acetabular cup or femoral stem placement (hip), femoral condyle or tibial tray placement (knee), or humeral and glenoid component placement information (which may have been obtained intraoperatively).

[0127] As a further example, information relating to bone cuts and reaming are used to update the musculoskeletal model's bone geometry so that simulations can take into account the changed bony anatomy.

[0128] As a further example, soft-tissue release data, and measured forces are used to update the musculoskeletal model's muscle components' geometry and material properties so that simulations take into account the changed soft-tissue properties.

[0129] At step 802, updating a musculoskeletal model may comprise validating the updated model using functional data such as measured motion data. Relatedly, updating the musculoskeletal model may then comprise fiting parameters of the model to the measured motion data.

[0130] At step 803, a treatment outcomes field is updated. For an intraoperative update or a postoperative update, updating the treatment outcomes field may comprise reducing the number ofdimensions of an existing treatment outcomes field. This is because partially or fully placing an implant limits how certain surgical parameters may be altered i.e. certain dimensions of the treatment outcomes field become constants. For example, once an implant has been selected and at least partially placed, the type and the size of implant are deemed to be fixed for treatment optimisation.

[0131] At step 804, a multi-dimensional treatment outcomes vector (in other words, a multidimensional surgical parameters vector) in the updated treatment outcomes field is selected for subsequent simulation analysis.

[0132] At step 805, a post-operative reference anatomy is estimated based on the updated musculoskeletal model. Step 804 may be substantially similar to step 113 of the method 100.

[0133] At step 806, the post-operative reference anatomy is used to simulate motions of the patient post-operation. Step 806 may be substantially similar to step 115 of the method 100.

[0134] At step 807, a treatment outcomes measures field is updated. Step 807 may be substantially similar to step 117 of the method 100.

[0135] At step 808, one or more safe zones of the treatment outcome measures field are updated. Step 808 may be substantially similar to step 119 of the method 100.

[0136] At step 809, one or more safe zones of the treatment outcomes field are updated. Step 809 may be substantially similar to step 121 of the method 100.

[0137] At step 810, the one or more safe zones are presented for optimising orthopaedic treatment. Step 810 may be substantially similarto step 123 of the method 100. In a hip replacement example, this can comprise displaying cup and stem zones to help determine optimal targets based on information obtained intraoperatively.

[0138] At step 811, an optimised orthopaedic treatment is updated based on the updated one or more safe zones. In an example embodiment, this comprises collaborative decision-making involving the patient, the surgeon, and Al assistance.

[0139] At step 812, the updated optimised orthopaedic treatment is delivered to a patient via a surgeon, a robotics surgical delivery subsystem, or a combination of both.System

[0140] Figure 3 shows an example system 300 for optimising orthopaedic treatment e.g. by performing the example method 100 or the example method 200 for optimising orthopaedic treatment or the example method 400 for updating an orthopaedic treatment.

[0141] The system 300 comprises an information storage subsystem 301 configured to store patient information, information about orthopaedic treatment methods and systems, and any other information relevant to the optimisation of orthopaedic treatment according to the methods described herein.

[0142] The system 300 comprises a pre-operative patient function assessment subsystem 302 configured to obtain patient functional data.

[0143] The system 300 comprises an image processing subsystem 303 that is configured to generate and / or update patient anatomy including a musculoskeletal model.

[0144] The system 300 comprises a simulation subsystem 304 configured to simulate a patient's motions and estimate the patient's mechanics and function.

[0145] The system 300 comprises a treatment fields subsystem 305 configured to determine and store a treatment outcomes field and a treatment outcomes measures field. The treatment fields subsystem 306 is also configured to handle operations relating to the treatment outcomes field and the treatment outcomes measures field.

[0146] The system 300 comprises a presentation subsystem 306 configured to present information to a user and / or an orthopaedic system.

[0147] The system 300 comprises a surgical delivery subsystem 307 configured to deliver an orthopaedic treatment to a patient. The surgical delivery subsystem 307 may comprise artificial intelligence and / or robotics elements.

[0148] The surgical delivery subsystem may be an automated surgical delivery system comprising robotic arms equipped with surgical instruments controlled by a computer system. The subsystem may comprise advanced imaging technologies e.g. 3D visualisation and real-time feedback, allowing a surgeon to have a clear view of the surgical site and make accurate decisions during the procedure. Artificial intelligence algorithms may be used to enhance surgical accuracy and efficiency.

[0149] The processes and systems described herein may be performed on or encompass various types of hardware, such as computer systems. In some embodiments, computer, display, and / or input device, may each be separate computer systems, applications, or processes or may run as part of the same computer systems, applications, or processes - or one of more may be combined to run as part of one application or process - and / or each or one or more may be part of or run on a computer system.

[0150] A computer system may include a bus or other communication mechanism for communicating information, and a processor coupled with the bus for processing information. The computer systems may have a main memory, such as a random access memory or other dynamic storage device, coupled to the bus. The main memory may be used to store instructions and temporary variables. The computer systems may also include a read-only memory or other static storage device coupled to the bus for storing static information and instructions. The computer systems may also be coupled to a display, such as a CRT or LCD monitor. Input devices may also be coupled to the computer system. These input devices may include a mouse, a trackball, or cursor direction keys. Each computer system may be implemented using one or more physical computers or computer systems or portions thereof. The instructions executed by the computer system may also be read in from a computer-readable medium. The computer-readable medium may be a CD, DVD, optical or magnetic disk, laserdisc, carrier wave, or any other medium that is readable by the computer system. In some embodiments, hardwired circuitry may be used in place of or in combination with software instructions executed by the processor. Communication among modules, systems, devices, and elements may be over a direct or switched connection, and wired or wireless networks or connections, via directly connected wires, or any other appropriate communication mechanism. The communication among modules, systems, devices, and elements may include handshaking, notifications, coordination, encapsulation, encryption, headers, such as routing or error detecting headers, or any other appropriate communication protocol or attribute. Communication may also messages related to HTTP, HTTPS, FTP, TCP, IP, ebMS OASIS / ebXML, secure sockets, VPN, encrypted or unencrypted pipes, MIME, SMTP, MIME Multipart / Related Content-type, SQL, etc.Interpretation

[0151] A number of methods have been described above. Any of these methods may be embodied in a series of instructions, which may form a computer program. These instructions, or this computerprogram, may be stored on a computer readable medium, which may be non -transitory. When executed, these instructions or this program cause a processor to perform the described methods.

[0152] Where an approach has been described as being implemented by a processor, this may comprise a plurality of processors. That is, at least in the case of processors, the singular should be interpreted as including the plural. Where methods comprise multiple steps, different steps or different parts of a step may be performed by different processors.

[0153] The steps of the methods have been described in a particular order for ease of understanding. However, the steps can be performed in a different order from that specified, or with steps being performed in parallel. This is the case in all methods except where one step is dependent on another having been performed.

[0154] The term "comprises" and other grammatical forms is intended to have an inclusive meaning unless otherwise noted. That is, they should be taken to mean an inclusion of the listed components, and possibly of other non-specified components or elements.

[0155] While the present invention has been explained by the description of certain embodiments, the invention is not restricted to these embodiments. It is possible to modify these embodiments without departing from the spirit or scope of the invention.

Claims

CLAIMS1. A method implemented on an electronic computing device for optimising orthopaedic treatment, the method comprising: obtaining patient information; determining a patient musculoskeletal model and patient native mechanics from the patient information; determining a treatment outcomes field based on surgical parameters and a treatment outcome measures field based on treatment outcome measures; determining a post-operative musculoskeletal model and post-operative mechanics across the treatment outcomes field; performing soft tissue balancing on the post-operative musculoskeletal model and updating the post-operative musculoskeletal model; constraining the treatment outcome measures field to one or more safe zones of the treatment outcome measures field; mapping the one or more safe zones of the treatment outcome measures field to one or more safe zones of the treatment outcomes field; presenting the one or more safe zones of the treatment outcomes field, and determining an optimised orthopaedic treatment based on the one or more safe zones of the treatment outcomes field.

2. The method of claim 1, further comprising intraoperatively updating the optimised orthopaedic treatment based on implant placement information or a morphological change.

3. The method of claim 1 or claim 2, wherein the constraining comprises comparing the patient musculoskeletal model with the post-operative musculoskeletal model and comparing the patient native mechanics with the post-operative mechanics.

4. The method of any one of claims 1 to 3, further comprising delivering the optimised orthopaedic treatment.

5. The method of any one of claims 1 to 4, wherein soft tissues comprise: cartilage, muscles, tendons, ligaments, fascia, fibrous tissue, fat, blood vessels, nerves, and synovial membranes.

6. The method of claim 5, wherein performing soft tissue balancing on the post-operative musculoskeletal model comprises using the patient native mechanics to create virtual markers for the post-operative musculoskeletal model and performing inverse kinematics.

7. The method of any one of claims 1 to 6, wherein determining a patient musculoskeletal model and patient native mechanics from the patient information comprises estimating the patient native mechanics through shape model fiting.

8. The method of any one of claims 1 to 7, wherein determining a patient musculoskeletal model and patient native mechanics from the patient information comprises fiting an articulated statistical shape model with embedded soft tissue to raw geometric meshes.

9. The method of any one of claims 1 to 8, wherein the surgical parameters comprise two or more of changes in centres of rotation, a leg length, changes in leg length, an offset, changes in offset, implant properties, implant orientations, surgical approaches, capsule releases, capsule repairs, bony section locations, soft tissue reatachment locations, and reaming and broach paths.

10. The method of claim 9, wherein the changes in centres of rotation comprise a change in a femoral head centre or a change in a hip joint centre.

11. The method of any one of claims 1 to 10, wherein determining a post-operative musculoskeletal model and post-operative mechanics across the treatment outcomes field comprises simulating day-to-day functions and lifestyle-specific function.

12. The method of any one of claims 1 to 11, wherein the treatment outcomes measures comprise two or more of muscle weakening risk, muscle overstretching risk, dislocation risk, pain risk, hipspine mobility score, range of motion, functional improvement, joint contact forces, osteoarthritis risk, implant stability or loosening risk, implant contact forces, implant contact area, risk of injury due to muscle imbalance, neutral align score, surgeon likeability of the plan, regulatory or consensus outcome measures and risks, patient reported outcome measures; and implant wear risk and / or rate.

13. The method of any one of claims 1 to 12, wherein constraining the treatment outcome measures field to one or more safe zones of the treatment outcome measures field comprises constraining according to a constraint function, wherein the constraint function comprises an absolute constraint independent of the patient native mechanics and a relative constraint dependent on the patient native mechanics.

14. The method of any one of claims 1 to 13, further comprising determining an optimality figure of merit for the one or more safe zones of the treatment outcomes field and ranking the one or more safe zones of the treatment outcomes field by optimality.

15. The method of claim 14, wherein determining an optimality figure of merit for the one or more safe zones of the treatment outcomes field is based on one more of an orthopaedic service provider's preferences, a patient's preferences, characteristics of an orthopaedic surgical approach, and characteristics of an orthopaedic surgical delivery system.

16. The method of any one of claims 1 to 15, wherein presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises recommending a safe zone of the treatment outcomes field based on zone size and optimality.

17. The method of any one of claims 1 to 16, wherein presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises highlighting a vector of the treatment outcomes field having the largest 2-norm.

18. The method of any one of claims 1 to 17, wherein presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises informing a user or an orthopaedic system of surgical parameter tolerances of a safe zone of the treatment outcomes field.

19. The method of any one of claims 1 to 18, wherein presenting the one or more safe zones of the treatment outcomes field to optimise orthopaedic treatment comprises presenting interactive 3D visualisations of a patient musculoskeletal model and patient mechanics in a virtual reality, augmented reality, or mixed reality environment.

20. The method of any one of claims 1 to 19, wherein each of the treatment outcome measures is in the vector form [0, infinity), where the "0" value is representative of the best outcome, and infinity is representative of the process of tending towards a worse outcome.

21. The method of any one of claims 1 to 20, wherein each of the treatment outcome measures is in the vector form [-1, 1], where the "-1" value is representative of the worst outcome, and the "1" value is representative of the best outcome.

22. The method of any one of claims 1 to 21, wherein the patient information comprises patient motion data.

23. The method of any one of claims 1 to 22, wherein the patient information comprises patient anatomical imaging data.

24. A method implemented on an electronic computing device for updating an orthopaedic treatment, the method comprising: receiving a musculoskeletal model and a treatment outcomes field of an orthopaedic treatment; intraoperatively triggering an update to the orthopaedic treatment based on implant placement information or a morphological change; updating a musculoskeletal model based on the implant placement information or the morphological change; performing soft tissue balancing on the updated musculoskeletal model; updating the treatment outcomes field based on the updated musculoskeletal model; updating one or more safe zones of the updated treatment outcomes field; presenting the one or more safe zones of the updated treatment outcomes field; and determining an optimised orthopaedic treatment based on the one or more safe zones of the updated treatment outcomes field.

25. The method of claim 24, further comprising delivering the optimised orthopaedic treatment.

26. The method of claim 24 or claim 25, wherein soft tissues comprise: cartilage, muscles, tendons, ligaments, fascia, fibrous tissue, fat, blood vessels, nerves, and synovial membranes.

27. The method of claim 26, wherein performing soft tissue balancing on the updated musculoskeletal model comprises using the patient native mechanics to create virtual markers for the updated musculoskeletal model and performing inverse kinematics.

28. The method of any one of claims 24 to 27, wherein presenting the one or more safe zones of the updated treatment outcomes field comprises recommending a safe zone of the updated treatment outcomes field based on zone size and optimality.

29. The method of any one of claims 24 to 28, wherein presenting the one or more safe zones of the updated treatment outcomes field comprises informing a user or an orthopaedic system of surgical parameter tolerances of a safe zone of the updated treatment outcomes field.

30. A system configured to perform the method of any one of claims 1 to 29, the system comprising: an information storage subsystem; a pre-operative patient function assessment subsystem; an image processing subsystem; a simulation subsystem; a treatment fields subsystem; and a presentation subsystem.

31. The system of claim 30, further comprising a surgical delivery subsystem.

32. A non-transitory computer readable medium comprising instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 1 to 29.

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