Devices, systems, and methods for bone balance adjustment based on bone spur detection.

Abstract

A method for evaluating a joint may include identifying a primary osteophyte in an image of the joint, determining the cross-sectional area of ​​the primary osteophyte, running an algorithm to determine one or more adjustment parameters based on the identified cross-sectional area of ​​the primary osteophyte, and outputting one or more determined adjustment parameters to a display. The primary osteophyte may be located beneath soft tissue. The algorithm may apply an equation to receive the identified cross-sectional area and output one or more adjustment parameters. One or more adjustment parameters may include the expected change in soft tissue laxity after removal of the identified primary osteophyte, adjustments to the planned depth of osteotomy for one or more osteotomies, adjustments to the planned angle of osteotomy for one or more osteotomies, and / or adjustments to the planned thickness of the implant.

Description

Technical Field

[0001] Among other aspects, the present disclosure relates to systems, devices, and methods for optimizing medical procedures, and more particularly, to systems, devices, and methods for detecting bone spurs and optimizing outcomes after joint replacement procedures.

Background Art

[0002] In surgeries incorporating artificial joints and / or implants, such as joint replacement procedures, often various factors need to be carefully considered. For example, when cutting or excising bone to fit a prosthesis, it may be necessary to remove bone spurs, which can change the predicted outcome of the initially planned bone cut. Improved systems and methods for performing, collecting, and analyzing image data and / or bone spur data are desired to facilitate surgical planning and improve patient outcomes.

Summary of the Invention

[0003] In an aspect of the present disclosure, a method of evaluating a joint can include identifying a first bone spur in an image of the joint, identifying a cross-sectional area of the first bone spur, executing an algorithm to determine one or more adjustment parameters based on the identified cross-sectional area of the first bone spur, and outputting the one or more determined adjustment parameters to a display. The first bone spur can be disposed under soft tissue. The algorithm can apply a linear equation that receives the identified cross-sectional area as an input and outputs one or more adjustment parameters. The one or more adjustment parameters can include a predicted change in soft tissue relaxation after the identified first bone spur is removed, an adjustment to the planned bone resection depth of one or more bone cuts, an adjustment to the planned bone resection angle of one or more bone cuts, and / or an adjustment to the planned thickness of an implant.

[0004] Identifying the first bone spur and / or identifying the cross-sectional area of the first bone spur can include analyzing the image using one or more image processing techniques. Identifying the cross-sectional area of the first bone spine may include analyzing the first dimension of the first bone spine and the second dimension of the first bone spine. Identifying the cross-sectional area of the first bone spine may include ignoring the third dimension of the first bone spine.

[0005] The one or more adjustment parameters may include an adjustment to the planned bone resection depth of the one or more bone cuts and an adjustment to the planned bone resection angle of the one or more bone cuts. Identifying the cross-sectional area of the first bone spine may include determining that the image of the joint is an image in a set of images that shows the maximum extent of the first bone spine in the first dimension.

[0006] The linear relationship is a linear relationship between the identified cross-sectional area and the adjustment to the planned bone resection depth, such that as the identified cross-sectional area increases, the decrease in the planned bone resection depth increases, and / or a linear relationship between the identified cross-sectional area and the planned thickness of the implant, such that as the identified cross-sectional area increases, the increase in the planned thickness of the implant increases.

[0007] The method may further include identifying a second bone spine of an image of the joint located under the soft tissue, identifying the cross-sectional area of the second bone spine, identifying the position of the first bone spine, and identifying the position of the second bone spine. Executing an algorithm to determine the one or more adjustment parameters may be further based on the identified cross-sectional area of the second bone spine, the identified position of the first bone spine, and the identified position of the second bone spine.

[0008] The method may further include determining, based on the identified locations of the first and second osteophytes, that the first osteophyte is located on the first side of the joint and the second osteophyte is located on the second side of the joint opposite to the first side. The method may further include determining the difference between the identified cross-sectional area of ​​the first osteophyte and the identified cross-sectional area of ​​the second osteophyte. The linear relationship may include a linear relationship between the determined difference in identified cross-sectional areas and the adjustment to the planned angle of osteotomy, where a larger determined difference in identified cross-sectional areas results in a larger adjustment to the planned angle of osteotomy.

[0009] The method may further include determining whether the difference in the identified cross-sectional areas is greater than or equal to a predetermined difference threshold. If the determined difference in the identified cross-sectional areas is less than or equal to the predetermined difference threshold, the method may include determining that the first osteophyte and the second osteophyte are symmetrical. If the determined difference in the identified cross-sectional areas is greater than the predetermined difference threshold, the method may include determining that the first osteophyte and the second osteophyte are asymmetrical.

[0010] If the first and second osteophytes are determined to be symmetrical, the algorithm may include determining that the planned depth of the osteotomy should be reduced by a predetermined amount for each predetermined amount of specified cross-sectional area of ​​the first and / or second osteophytes, or that the planned thickness of the implant should be increased by a predetermined amount for each predetermined amount of specified cross-sectional area of ​​the first and / or second osteophytes. If the first and second osteophytes are determined to be asymmetrical, the algorithm may include determining whether the first osteophyte is larger than the second osteophyte based on the specified cross-sectional areas of the first and second osteophytes. If the first osteophyte is determined to be larger than the second osteophyte, the method may include determining that the planned angle of the osteotomy should be adjusted to a first direction or orientation by a predetermined amount of angle of osteotomy for each predetermined difference between the specified cross-sectional area of ​​the first and second osteophytes. If it is determined that the first osteophyte is not as large as the second osteophyte, the method may include determining that the second osteophyte is larger than the first osteophyte, and determining that the planned angle of osteotomy should be adjusted by a predetermined amount of osteotomy angle for each predetermined difference in the second direction or orientation opposite to the first direction or orientation.

[0011] The method may further include determining that the first and second osteophytes are asymmetrical; determining that both the specified cross-sectional area of ​​the first and second osteophytes are larger than a predetermined cross-sectional area; determining that the difference in the specified cross-sectional areas is larger than a predetermined difference; determining that the planned depth of the osteotomy should be reduced by a predetermined amount for each predetermined amount of specified cross-sectional area of ​​the first and / or second osteophytes; or determining that the planned thickness of the implant should be increased by a predetermined amount for each predetermined amount of specified cross-sectional area of ​​the first and / or second osteophytes.

[0012] The joint may be the knee joint. The first osteophyte may be a lateral osteophyte, and the second osteophyte may be a medial osteophyte. One or more osteotomies may include a tibial or femoral osteotomy. The first direction may be varus of the tibia or varus of the femur. The second direction may be valgus of the accessory navicular bone or valgus of the femur. The specified depth may be in the range of 0.4 mm to 0.6 mm. The specified cross-sectional area of ​​the first and / or second osteophytes may be in the range of 85 mm² to 100 mm². The angle of the specified osteotomy may be in the range of 0.4° to 0.6°. The specified difference between the specified cross-sectional area of ​​the first and second osteophytes may be in the range of 80 mm² to 100 mm².

[0013] The specified depth may be in the range of 0.75 mm to 0.125 mm. The specified cross-sectional area of ​​the first and / or second osteophyte is 15 mm². 2 ~25mm 2 The range may be as follows: The angle of the predetermined amount of bone resection may be in the range of 0.75° to 0.125°. The predetermined difference between the specified cross-sectional area of ​​the first osteophyte and the specified cross-sectional area of ​​the second osteophyte is 15 mm 2 ~25mm 2 It could be within the range of.

[0014] According to another aspect of the present disclosure, a method for evaluating a joint may include receiving an image of the joint including an osteophyte, and receiving a treatment plan that includes a plan to remove the osteophyte after one or more osteotomies have been performed. The image shows a view of the joint in a first and second dimension, with soft tissue extending above the osteophyte. One or more osteotomies may be configured for the placement of implants during the procedure. The method may include identifying the cross-sectional area of ​​the osteophyte in the first and second dimensions, and running an algorithm to determine one or more adjustment parameters. The algorithm may apply a linear equation that takes the identified cross-sectional area of ​​at least one osteophyte as input, and output one or more adjustment parameters. One or more adjustment parameters may include adjustments to one or more osteotomy parameters and / or adjustments to implant parameters of the received treatment plan. The method may include outputting one or more determined adjustment parameters to a display. One or more osteotomy parameters may include the planned depth of osteotomy for one or more osteotomies and / or the planned angle of osteotomy for one or more osteotomies.

[0015] According to yet another aspect of this disclosure, a system configured to evaluate a joint may include an image acquisition device configured to acquire at least one image of the joint, a memory configured to store information, a controller, and a display. The information may include imaging data associated with at least one acquired image. The imaging data may include the cross-sectional area and location of at least one identified portion of the bone of the joint. The controller may be configured to run an algorithm that determines one or more adjustment parameters based on at least one acquired image and / or stored imaging data. The algorithm may apply a linear equation that takes the cross-sectional area of ​​at least one identified portion of the bone as input and output one or more adjustment parameters. The one or more adjustment parameters may include adjustments for bone resection parameters and / or adjustments for implant parameters. The display may be configured to display the determined one or more adjustment parameters.

[0016] The image acquisition device may be a computed tomography (CT) acquisition device. At least one acquired image may be a CT scan. A CT scan can provide views of the joint in the first and second dimensions, where soft tissue extends. The cross-sectional area can be determined using the dimensions of the identified portion of bone in the first and second dimensions.

[0017] The display may be configured to show a graphical user interface that includes a notification based on at least one identified part of the bone identified in the acquired image.

[0018] One or more adjustment parameters may include coronal plane alignment, and bone resection parameters may be used to determine coronal plane alignment. In yet another aspect of the present disclosure, a method for evaluating a joint may include receiving an image of the joint, running an algorithm that determines one or more adjustment parameters based on the identification of bony prominences such as posteromedial flares, and outputting one or more determined adjustment parameters to a display. The one or more adjustment parameters include the expected change in soft tissue laxity after the removal of the identified bony prominences, adjustments to the planned depth of the osteotomy of one or more osteotomies, adjustments to the planned angle of the osteotomy of one or more osteotomies, and / or adjustments to the planned size of an implant.

[0019] A more complete understanding of the subject matter of this disclosure and its various advantages can be obtained by referring to the following detailed description, which includes references to the attached drawings. [Brief explanation of the drawing]

[0020] [Figure 1] Exemplary portions of the anatomical structure of a patient according to aspects of this disclosure are shown. [Figure 2] The diagram shows an example of soft tissue, such as ligaments, extending over bone growth, such as bone spurs, according to an aspect of this disclosure. [Figure 3]Figure 2 shows an example of soft tissue from which bone spurs have been removed, according to an aspect of this disclosure. [Figure 4] An exemplary knee joint in flexion and extension according to aspects of this disclosure is shown. [Figure 5] An exemplary cross-sectional view of a knee joint including one or more implant components according to an aspect of this disclosure is shown. [Figure 6] An exemplary electronic data processing system, including a bone balancing system, is shown according to an exemplary embodiment. [Figure 7] An exemplary process flow diagram, including data, inputs, and outputs of the bone balancing system shown in Figure 6, is provided in accordance with the embodiments of this disclosure. [Figure 8] An exemplary graphical user interface for the bone balancing system of Figure 6, according to an aspect of this disclosure, is shown. [Figure 9] Images of the anatomical structures of a patient, including the femur, tibia, and osteophytes, according to aspects of this disclosure are shown. [Figure 10] An example of detecting and calculating the surface area of ​​a bone spur shown in Figure 1, according to an aspect of this disclosure, is provided. [Figure 11] An example of detecting and calculating the surface area of ​​a bone spur shown in Figure 9, according to an aspect of this disclosure, is provided. [Figure 12] This disclosure illustrates exemplary preoperative methods for adjusting a preoperative plan or treatment plan based on detected osteophytes. [Figure 13] Exemplary preoperative and / or intraoperative methods 1300 for adjusting a treatment plan based on detected osteophytes are shown according to aspects of this disclosure. [Figure 14] This disclosure illustrates exemplary preoperative and / or intraoperative methods for adjusting a treatment plan for knee surgery based on detected osteophytes. [Figure 15] This disclosure illustrates exemplary preoperative methods for adjusting a surgical or treatment plan based on detected osteophytes. [Figure 16] This disclosure provides exemplary methods for adjusting a surgical or treatment plan based on relaxation. [Figure 17]This illustrates an exemplary relationship between the removal of posterior osteophytes and knee joint laxity. [Figures 18A-18B] Figure 18A shows a top view of the tibia and implant overlaid with exemplary guidance displays at different stages of the surgical procedure according to an aspect of this disclosure, and Figure 18B shows a top view of the tibia and implant overlaid with exemplary guidance displays at different stages of the surgical procedure according to an aspect of this disclosure. [Figures 18C-18D] Figure 18C shows a top view of the tibia and implant overlaid with exemplary guidance displays at different stages of the surgical procedure according to an aspect of this disclosure, and Figure 18D shows a top view of the tibia and implant overlaid with exemplary guidance displays at different stages of the surgical procedure according to an aspect of this disclosure. [Modes for carrying out the invention]

[0021] The following will be a detailed description of various embodiments of this disclosure shown in the accompanying drawings. Where possible, the same or similar reference numerals will be used throughout the drawings to indicate the same or similar features. Note that the drawings are in a simplified form and are not drawn to exact scale. Furthermore, as used herein, the term "a" means "at least one." The term includes the terms specifically mentioned above, their derivatives, and terms with similar meanings. Although at least two variations are described herein, other variations may include any suitable combination of all or some of the embodiments described herein.

[0022] Where used herein, the terms “implant trial” and “trial” are interchangeable, and therefore, unless otherwise stated, express use of either term includes the other term. In this disclosure, “user” is synonymous with “practitioner” and may be any person (e.g., surgeon, technician, nurse, etc.) who completes the described act.

[0023] An implant may be a device that is at least partially embedded in a patient and / or provided within the patient's body. For example, an implant may be a sensor, artificial bone, or other medical device that is bonded to, embedded in, or at least partially embedded in bone, skin, tissue, organ, etc. A prosthesis or artificial joint may be a device configured to assist or replace a limb, bone, skin, tissue, etc., or a part thereof. Many prostheses are implants, such as tibial joint components. Some prostheses may be exposed outside the body and / or partially implanted, such as an artificial forearm or leg. Some prostheses may not be considered implants and / or may be entirely outside the body, such as a knee brace in other conditions. The systems and methods disclosed herein may also be used in relation to implants, prostheses that are implants, and prostheses that may not be considered “implants” in the narrow sense. Where used herein, the terms “implant” and “prosthesis” are used interchangeably, and therefore, unless otherwise stated, express use of either term includes the other term. The term “implant” is used throughout this disclosure, but should include prostheses that are not necessarily “implants” in the narrow sense.

[0024] When describing preferred embodiments of this disclosure, refer to the directional nomenclature used when describing the human body. Note that this nomenclature is used for convenience only and is not intended to limit the scope of the invention. For example, as used herein, the term “distal” means toward the human body and / or toward the operator, and the term “proximal” means toward the human body and / or toward the operator. As used herein, the terms “comprise,” “comprising,” or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus, including a list of elements, may include other elements that are not explicitly listed or are specific to such system, process, method, article, or apparatus, rather than including only those elements. The term “exemplary” is used in the sense of “example” and not “ideal.” Furthermore, relative terms such as “about,” “substantially,” and “approximately” are used to indicate a possible variation of ±10% in the stated numerical value or range.

[0025] This disclosure generally provides a system and method for detecting and removing osteophytes, and for balancing joint laxity in the knee joint. Osteophytes can develop around the knee in response to disease, and the number, size, volume, and growth of osteophytes may correlate with the progression of the disease. As osteophytes grow, the soft tissue extending over them may stretch. However, when the osteophytes are removed, the previously stretched soft tissue may loosen and may not contract completely back to its original state. This reduction in soft tissue laxity can lead to joint laxity. Therefore, to balance the joint space, surgeons may try to predict the effect of osteophyte removal on the resulting flexion and extension gaps before performing any bone cuts and before those osteophytes are removed.

[0026] In some cases, with conventional total knee arthroplasty (TKA), surgeons may be able to adjust soft tissue at the site of the bone cut. In some cases using robotic TKA, surgeons can adjust the bone cut to soft tissue. In some cases, debridement of osteophytes may require adjustment of the planned bone resection. To better plan TKA with targeted laxity, a predictive algorithm for determining the precise adjustments required in the bone resection based on the size and location of the osteophytes may be desired, which is described in more detail below.

[0027] Osteophytes can be found in many locations, medial, lateral, anterior, posterior, centrally within the intergranular notch, and around the patella. Most osteophytes are easily accessible and can be removed during a surgical approach to the knee. However, posterior osteophytes are relatively difficult to access, in that their removal can only be performed after a posterior osteotomy on the femur. Any laxity resulting from the removal of posterior osteophytes cannot be corrected by adjusting the osteotomy because the osteotomy has already been performed. This disclosure generally provides a predictive algorithm that anticipates the effects of osteophyte removal before it is performed, enabling adjustments in the osteotomy before any osteotomy is performed.

[0028] Figure 1 shows an exemplary portion of an anatomical structure 100 (e.g., a leg or knee joint). Referring to Figure 1, the portion of the anatomical structure 100 may include one or more bones 102 (e.g., a tibia or femur) and one or more soft tissues 104 (e.g., ligaments). One or more bones 102 may include one or more osteophytes 106 or other bony projections. The soft tissues 104 may extend over the osteophytes 106.

[0029] Osteophytes can be bone spurs that develop on bone. They may develop in response to a disease (e.g., osteoarthritis or OA), and the number, size, volume, and / or growth of osteophytes may correlate with the progression of the disease. As osteophytes grow, soft tissues extending over them may stretch. Referring to Figure 1, soft tissue 104 (e.g., ligaments) may stretch over osteophyte 102.

[0030] Removing osteophytes during medical procedures (e.g., total knee arthroplasty, i.e., TKA, or partial knee arthroplasty, i.e., PKA) may be desirable to treat the disease and reduce pain (e.g., from impact). However, once removed, previously stretched soft tissue may become lax and may not fully contract to its original state due to the removal of one or more osteophytes. For example, referring to Figure 2, bone 200 may include an osteophyte 202 and a ligament 204 extending over the osteophyte 202. Referring to Figure 3, when the osteophyte 202 is removed from bone 200, the ligament 204 may remain lax. If the ligament and osteophyte 202 occur near a joint (e.g., the knee joint), this reduction in soft tissue laxity may lead to joint laxity, potentially reducing joint function and increasing the patient's risk of complications.

[0031] As shown in Figure 2, ligament 204 may extend along the X and Z directions on the osteophyte 202, and therefore may stretch in the X and Z directions when the osteophyte 202 is removed (Figure 3). However, the thickness or width of the osteophyte 202 in the direction entering and / or leaving the page may not be relevant to how ligament 204 is stretched in Figures 2 and 3.

[0032] Figure 4 shows an exemplary knee joint 400 in flexion and extension. Referring to Figure 4, the knee joint 400 may include the femur 402, the tibia 404, and ligaments 406 (e.g., medial collateral ligament) connected to the femur 402 and tibia 404. The femur 402 may rotate relative to the tibia 404 about a flexion-extension axis 408 extending through the femur 402. A treatment plan (e.g., a surgical plan or a medical treatment plan) may include one or more osteotomies, such as distal and posterior femoral resections 410 via the femur 402, and / or a proximal tibial resection 412 via the tibia 404. When the knee joint 400 is extended, the extensible space 414 may extend between the femoral resection 410 and the tibial resection 412. When the knee joint 400 is flexed, the flexural space 416 may extend between the femoral resection portion 410 and the tibial resection portion 412. The femoral resection portion 410 and the tibial resection portion 412 may be configured to comprise a balanced knee joint 400, and, in some examples, one or more prosthesis components or liners, e.g., a femoral prosthesis component 502, a tibial prosthesis component 504, and a liner 506 as shown in Figure 5.

[0033] When planning a medical procedure at the knee joint 400, the practitioner (e.g., surgeon, physician, medical planner, etc.) may desire to make the extensor space 414 equal to the flexion space 416. In some cases, the practitioner may plan to create a rectangular flexion space 416 by adjusting the femoral cutting block so that it is parallel to the tibial surface to be resected at a 90° angle, with the ligaments (e.g., ligaments 406) under tension. These adjustments and the relaxation of ligaments 406 are brought about by the presence and subsequent removal of osteophytes, as described above. Therefore, the practitioner may remove any easily accessible osteophytes before planning (or fine-tuning of the plan) and performing the osteotomy, although certain osteophytes (e.g., posterior osteophytes) may not be accessible until the osteotomy is performed. Subsequent removal of posterior osteophytes can have a significant impact on the extensor space 414 and the flexion space 416. Therefore, in order to balance the gaps, the practitioner may attempt to predict the effect of removing the posterior osteophytes on the resulting extensor gap 414 and flexion gap 416 before performing any bone cutting and before removing those posterior osteophytes, much like how a golfer can adjust their aim to account for the wind. Removal of posterior osteophytes can affect both extension and flexion, regardless of whether they are located medially or laterally.

[0034] Embodiments disclosed herein can analyze the location and / or dimensions of these initially inaccessible osteophytes to determine one or more osteotomy parameters for the procedure (e.g., osteotomy location and / or inclination). Alternatively, or in addition, embodiments disclosed herein can analyze the location and / or dimensions of these initially inaccessible osteophytes to determine the dimensions and / or design of one or more implants or orthotics used in the procedure, e.g., the thickness, size, material, and / or shape of the femoral prosthesis component 502, the tibial prosthesis component 504, and / or liner 506 shown in Figure 5.

[0035] Referring to 6, the electronic data processing system 6 may include a bone balancing system 600 which includes one or more algorithms. The bone balancing system 600 may receive one or more medical images of a patient's anatomical structure acquired using one or more image acquisition devices 610, analyze the received medical images to determine and / or adjust a treatment plan 620, which may be output to a display 630 and / or a robot and / or automated data system or platform 640 (e.g., a robotic system such as a surgical robot and / or robotic tools). Results and / or outcome data 650 from the treatment may be supplied to the bone balancing system 600 to further improve one or more algorithms.

[0036] The bone balancing system 600 may be implemented as one or more computer systems or cloud-based electronic processing systems. The image acquisition device 610 may include, in addition to computed tomography (CT) scanners, magnetic resonance imaging (MRI) machines, X-ray machines, radiography systems, ultrasound systems, thermography systems, tactile imaging systems, elastography, nuclear medicine functional imaging systems, positron emission tomography (PET) systems, single-photon emission computed tomography (SPECT) systems, cameras, and the like. An improvised patient scheduled to undergo a procedure (e.g., surgery) may first be imaged using the image acquisition device 610 (e.g., a CT scanner). Images and / or information collected during imaging (e.g., CT or CAT scan) may be transmitted from or stored in the image acquisition device 610.

[0037] During imaging using the image acquisition device 610, the patient may go through one or more "poses" or positions in which specific soft tissues of the joint (e.g., medial and / or lateral soft tissues) are stressed at specific positions of the joint (e.g., flexion and extension) by applying one or more forces (e.g., varus and valgus forces). The bone balancing system 600 may determine and / or calculate the size and / or shape of one or more joint spaces (e.g., flexion space and extension space) to evaluate the joint's soft tissue envelope. Alternatively, or in addition to this, the practitioner may determine the size and / or shape of one or more spaces and input the determined size and / or shape into the bone balancing system 600 (e.g., via a user interface such as the display 630). The bone balancing system 600 may identify bone landmarks from the images and / or data (e.g., osteophytes and their dimensions and / or location) acquired during the poses. Alternatively, the practitioner can analyze the images and input imaging data, such as the location and dimensions of bone landmarks (e.g., osteophytes), into the bone balancing system 600.

[0038] The bone balancing system 600 may run one or more algorithms to determine a treatment plan 620 using imaging data including determined gaps (e.g., flexion and extension gaps) and / or identified bone landmarks (e.g., osteophytes). The treatment plan 620 may include a series of steps to be performed in the procedure, such as one or more osteoresection parameters (e.g., resection or cutting to be performed on the bone and / or one or more osteophytes to be removed) and / or the design of an implant (e.g., a prosthetic liner). The treatment plan 620 may also include predicted outcomes (e.g., the risk of complications during the procedure or the risk of infection after the procedure). For example, the treatment plan 620 may first be determined based on the size and / or shape of one or more joint gaps, and then modified based on identified osteophytes to be removed during the procedure.

[0039] In some cases, a treatment plan 620 including one or more bone resection parameters 762 and / or implant parameters 764 (Figure 7) may be transmitted to a display 630. One or more bone parameters 762 and one or more implant parameters 764 of the treatment plan 620 may be collectively referred to as one or more adjustment parameters. In some cases, a treatment plan 620 including one or more bone resection parameters 762 and / or implant parameters 764 may be transmitted to a robot and / or surgical robot or robot tool (e.g., an automated cutting bar) of an automated data system 640 that performs the determined bone resection parameters 762 by automatically cutting the bone, for example via a surgical robot holding a tool, a surgeon holding a robotic tool, etc., according to the bone resection parameters 762 of the treatment plan 620. As a series of treatments continue, the actual or observed outcomes and / or results 650 may also be used by the bone balancing system 600 to update its predictions (e.g., intraoperatively, based on data or outcomes collected during surgery) and / or to make future predictions for future patients (e.g., based on postoperative outcomes or results). Intraoperative data for further refinement may be similar to preoperative data 720. Details of the bone balancing system 600 and its evaluation will be explained in more detail with respect to Figure 7.

[0040] Figure 7 shows exemplary data, inputs, and outputs of the bone balancing system 600. Referring to Figure 7, the bone balancing system 600 can receive preoperative data 720 from one or more preoperative measurement systems 710, analyze the preoperative data 720, and generate one or more outputs 730, such as a treatment plan 620, to one or more output systems 770, such as a display 630. The preoperative measurement system 710 may include an image acquisition device 610, an electronic device for storing an electronic medical record (EMR) 714, a patient, practitioner, and / or user interface or application 750 (e.g., a tablet, computer, or other mobile device), and a robot and / or automated data system or platform 640 (e.g., MAKO robot system or platform, MakoSuite, etc.), which may include the aforementioned robotic device.

[0041] The bone balancing system 600 receives imaging data 722 via the image acquisition device 610 and can receive supplementary or additional information (e.g., patient data and medical history 724, planned procedure data 726, surgeon and / or staff data 728, and / or previous procedure data 740) via the EMR 712, interface 714, sensors and / or electronic medical devices, and / or robotic platform 640. Each device of the preoperative measurement system 710 (image acquisition device 610, EMR 712, user interface or application 714, sensors and / or electronic medical devices, and robotic platform 640) may include one or more communication modules (e.g., WiFi module, Bluetooth® module, etc.) configured to transmit preoperative data 720 to each other, to the bone balancing system 600, and / or to one or more output systems 770.

[0042] The image acquisition device 610 may be configured to collect or acquire one or more images, videos, or scans of anatomical structures within the patient's body, such as bones, ligaments, soft tissues, and brain tissue, to provide imaging data 722, which will be described in further detail later. As mentioned above, in addition to computed tomography (CT) scanners, the image acquisition device 610 may include magnetic resonance imaging (MRI) machines, X-ray machines, radiography systems, ultrasound systems, thermography systems, tactile imaging systems, elastography, nuclear medicine functional imaging systems, positron emission tomography (PET) systems, single-photon emission computed tomography (SPECT) systems, cameras, etc. The collected images, videos, or scans may be transmitted automatically or manually to the bone balancing system 600. In some examples, the user may select specific images from multiple images taken by the image acquisition device 610 for transmission to the bone balancing system 600.

[0043] The bone balancing system 600 may use data previously collected from EMR712, which may include patient data and medical history 724 in the form of past practitioner evaluations, medical records, past patient report data, past imaging procedures, and treatments. For example, EMR712 may include data on demographics, medical history, biometrics, past treatments, general observations about the patient (e.g., mental health), lifestyle information, and data from physical therapy.

[0044] The bone balancing system 600 may also use current or up-to-date (e.g., real-time) patient data via the patient, practitioner, and / or a user interface or application 714. These user interfaces 714 may be implemented as a mobile application and / or a patient management website or an interface such as OrthologIQ®. The user interface 714 may present questionnaires, surveys, or other prompts for the practitioner or patient to input data and / or responses (e.g., responses to various poses), psychosocial information and / or preparation for surgery, comments, etc., for additional patient data 724. The patient may also input psychosocial information such as perceived or assessed pain, stress levels, anxiety levels, emotions, and outcome measures (PROMS) reported by other patients into these user interfaces 714. The patient and / or practitioner may report lifestyle information via the user interface 714. The user interface 714 may also collect clinical data such as data on planned procedures 726 and data on planned surgeons and / or staff 728, which will be described in more detail later. These user interfaces 714 may be implemented on and / or combined with other devices disclosed herein (e.g., robotic platform 640).

[0045] The bone balancing system 600 may receive previous treatment data 750 and / or other real-time data or observations (e.g., observed patient data 724) from previous patients via the robot platform 640. The robot platform 640 may include one or more robotic devices (e.g., surgical robots), computers, databases, etc., used in previous treatments of different patients. The robot platform 640 may assist in previous treatments through automated movement, surgeon assistance, and / or sensing, and may be implemented as or include one or more automated surgical tools or robotic surgical tools, robotic surgical robots or computerized numerical control (CNC) robots, surgical tactile robots, surgical remote surgical robots, surgical handheld robots, or any other surgical robots.

[0046] Although the preoperative measurement system(s) 710 is described in relation to the image acquisition device 610, EMR 712, user interface 714, and robotic platform 640, other devices may be used preoperatively to collect preoperative data 720, such as joint alignment and / or identified bone landmarks or osteophytes or other data cited to create a treatment plan 620. For example, mobile devices such as cell phones and / or smartwatches may include various sensors (e.g., gyroscopes, accelerometers, temperature sensors, optical or light sensors, magnetometers, compasses, Global Positioning System (GPS), etc.) to collect patient data 724 such as location data, sleep patterns, motion data, heart rate data, lifestyle data, and activity data. As another example, wearable sensors with various sensors (e.g., cameras, optical sensors, barometers, GPS, accelerometers, temperature sensors, pressure sensors, magnetometers or compasses, MEM devices, inclinometers, acoustic distance meters, etc.), heart rate monitors, motion sensors, external cameras, etc., can be used during physical therapy or pre-rehabilitation programs to collect information on the patient's kinesiology, alignment, movement, fitness, heart rate, electrocardiogram data, respiratory rate, body temperature, oxygen supply, sleep patterns, activity frequency and intensity, sweat, perspiration, air circulation, stress, step pressure or push-off force, balance, heel strike, gait, fall risk, frailty, and overall function. Other types of systems or devices may include electromyography or EMG systems or devices, motion capture (mocap) systems, sensors using machine vision (MV) technology, virtual reality (VR) or augmented reality (AR) systems, etc.

[0047] Preoperative data 720 may be data collected, received, and / or stored prior to the start of a medical treatment plan or medical procedure. As indicated by the arrows in Figure 7, preoperative data 720 may be collected using the preoperative measurement system 710 from the memory system 752 (e.g., a cloud storage system) of the bone balancing system 600 and from the output system 770 (e.g., from previous procedures) for one or more continuous feedback loops. Some of the preoperative data 724 may be detected directly via one or more devices (e.g., an imaging device 610 and / or a wearable motion sensor or mobile device), or manually entered by a medical professional, patient, or other party. Other preoperative data 724 may be determined (e.g., by the bone balancing system 600) based on information detected directly from previous medical procedures, input information, and / or stored information.

[0048] As mentioned above, the preoperative data 724 may include imaging data 722, patient data and / or medical history 724, information regarding the planned procedure 726, surgeon data 728, and previous procedure data 740.

[0049] The imaging data 722 may include one or more images (e.g., raw images), videos, or scans of the patient's anatomical structure collected and / or acquired by the imaging device 610. The bone balancing system 600 may receive and analyze one or more of these images to determine further imaging data 722, which may be used as further input preoperative data 724. In some examples, the imaging device 610 may analyze and / or process one or more images and transmit any analyzed and / or processed imaging data to the bone balancing system 600 for further analysis.

[0050] One or more images of the imaging data 722 can be illustrated or displayed, and the bone balancing system 600 may be configured to identify and / or recognize in the images the location or alignment, composition and / or density, fracture and / or tear, bone landmarks (e.g., condylar surface, femoral head or epiphysis, femoral neck or bone spur, body or diaphysis, articular surface, epicondyle, lateral epicondyle, medial epicondyle, process, prominence, tubercle and nodal point, tibial tubercle, trochanter, spine, ridge or line, small face, crest and prominence, foramen and fissure, canal, fovea and central fovea, notch and groove, and sinus), geometry (e.g., diameter, inclination, angle), and / or other anatomical geometric data such as deformation or flare (e.g., coronal deformation, sagittal deformation, lateral femoral spur flare, or medial femoral spur flare). Such geometry is not limited to the overall geometry but may include relative dimensions (e.g., the length or thickness of the tibia or femur).

[0051] The imaging data 722 may include information about osteophytes identified or detected in or around joints, such as the knee joint. The imaging data 722 may include indications of the dimensions and / or positioning of the osteophytes, the compartment or location of the osteophytes, and / or whether the osteophytes are easily reachable or can only be removed after osteotomy. The imaging data 722 can be used to indicate or determine the size, volume, or location of the osteophytes, bone loss, joint cavity, B score, bone quality / density, skin-to-bone ratio, bone mass loss, hardware detection, anterior-posterior (AP) and medial-lateral (ML) distal femur size, and / or joint angles. The analyses and / or calculations that can be derived from the images or scans will be described in more detail later when describing the bone balancing system 600. For example, autonomous segmentation and / or calculation of the size and location of osteophytes may be applied to one or more images and / or imaging data 722.

[0052] Furthermore, the imaging data 722 may include morphological and / or anthorometric data (e.g., physical dimensions such as viscera and bone), fracture, tilt or angle data, tibia tilt, posterior tibia tilt, i.e., PTS, and bone mineral density (e.g., bone mineral or bone marrow density, bone softness or hardness, or bone impact). The imaging data 722 is not limited to bone data and may include other internal imaging data such as cartilage, soft tissue, blood flow, or ligaments.

[0053] In addition to the raw image, the imaging data 722 may include intermediate and / or related imaging data 722 used by the bone balancing system 600 to calculate the output 730. Such intermediate imaging data 722 may include processed image data showing specifically detected attributes, such as density or composition charts or graphs, quantified data showing relative position, dimensions, etc., and / or the probability of a particular patient condition. One or more algorithms 760 of the bone balancing system 600 may determine or calculate this intermediate imaging data 722 when determining the output 730, or alternatively, or further, the image acquisition device 610 may include one or more processors configured to calculate or quantify at least a portion of the intermediate imaging data 722 based on the raw image, video, or scan. The intermediate imaging data 722 may include information that relates to, shows, and / or quantifies aspects of the raw image, chart, etc.

[0054] Patient data and medical history 724 include the following information about the patient: identity (e.g., name or date of birth), demographic attributes (e.g., patient's age, sex, height, weight, race, body mass index (BMI), etc.), lifestyle (e.g., smoking habits, exercise habits, drinking habits, eating habits, health, activity level, frequency of upward activities such as climbing stairs, frequency of sitting-to-standing or bending movements such as getting in and out of a car, number of steps per day, activities of daily living or ADL performed, etc.), medical history (e.g., allergies, disease progression, addiction, previous drug use, previous infections, frailty, comorbidities, previous surgery or treatment, previous (e.g., injury, previous pregnancy, orthodontic appliance, braces, artificial joint, or use of other medical devices), assessment and / or evaluation (e.g., clinical tests and / or blood tests, American Society of Anesthesiologists or ASA score, and / or suitability for surgery or anesthesia), electromyography data (muscle response or electrical activity in response to nerve stimulation), psychosocial information (e.g., perceived pain, stress level, anxiety level, mental health status), PROMS (e.g., outcome score for knee injury and osteoarthritis or KOOS, outcome score for hip injury and osteoarthritis or HOOS, virtual analog pain scale or VAS, PROMIS) Information may include information on past biometrics (e.g., heart rate or heart rate variability, electrocardiogram data, respiratory rate, temperature (e.g., body temperature or skin temperature), fingerprints, DNA, etc.), past kinesiology or alignment data, past imaging data, and data from preventive programs or physiotherapy (e.g., mean weight loading). Medical history 724 may include information on previous clinical or hospital visits, such as type of visit, date of admission, comorbidity data such as hospital-reported Elixhauser or Charlson scores, or selected comorbidities (e.g., ICD-10 POA), and previous anesthesia and / or responses. However, this list is not exhaustive, and preoperative data 720 may include other patient-specific, clinical, and / or surgeon- or practitioner-specific information (e.g., experience level).

[0055] Patient data 724 can be obtained based on the EMR 712, user interface 714, memory system 752, and / or robot platform 640, but the embodiments disclosed herein are not limited to the collection of patient data 724. For example, other types of patient data 724 or additional data may include data relating to activity level, kinematics, muscle function or ability, range of motion data, intensity and / or force measurements, kicking force, force, or acceleration, toe force, strength, or acceleration during walking, angular range or axis of joint movement or range of motion, step count data (e.g., measured by a pedometer), flexion or extension data including gait data or assessment, fall risk data, balance data, joint stiffness or laxity data, postural sway data, data from examinations performed in a clinic or remotely, etc.

[0056] Information regarding the planned procedure 726 may include the initial procedure plan 620 and / or logistical information and substantial information regarding the procedure. Substantially planned information regarding the procedure 726 may include the surgeon's surgical procedure or other procedure or treatment plan, including the planned procedure or instructions for the incision, the surgical side of the patient's body (e.g., left or right) and / or lateral information, the depth of the bone cut or excision, the design, type, and / or size of the implant, the alignment of the implant, information on fixation or tools (e.g., implants, rods, plates, screws, wires, nails, bearings used), cement vs. cementless technique or implant, final or desired alignment, posture or orientation information (e.g., flexion or extension capture space values, space or width between two or more bones, joint alignment), planning time, space balance time, and use of the expanded tactile boundary. Information regarding the logistics of the planned procedure 726 may include information about the location of the planned procedure, such as the hospital, the type of procedure or surgery to be performed (e.g., total or partial knee arthroplasty or total or partial knee replacement, total or partial gluteustomy or total or partial gluteustomy, spinal surgery, patellar surface reconstruction, etc.), schedule or appointment information, and necessary equipment or tools. This initially planned procedure 726 information may be prepared or entered manually by the surgeon and / or pre-prepared or determined using one or more algorithms. Surgeon data 728 may include information about the surgeon or other staff who will perform the procedure plan 620, such as identity (e.g., name), experience level, health level, height and / or weight.

[0057] Previous treatment data 740 may include information about previous treatments performed on the same or previous patient. Such information may include the same types of information as planned treatment data 728 (e.g., treatment instructions or steps, osteotomy site, implant design, implant alignment, etc.) in addition to outcome and / or result information (e.g., outcome 650 as described with reference to Figure 6), and may include both immediate and long-term results, postoperative complications, length of hospital stay, reoperation data, rehabilitation data, patient movement and / or motion data, etc. Previous treatment data 740 may include information about previous treatments of previous patients who share at least one of the same or similar characteristics as the current patient (e.g., demographic characteristics, biometric characteristics, medical condition, etc.).

[0058] The preoperative data 720 may include any other additional or supplementary information stored in the memory system 752, which may include known data and / or data from third parties, such as data from the Knee Society Clinical Rating System (KSS) or data from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Furthermore, in some cases, during surgery, data may be collected and / or received by the bone balancing system 600 to further generate and / or refine the output 730 (e.g., the treatment plan 620).

[0059] Bone Balancing System 600 The bone balancing system 600 may include a memory system 752 containing stored data 754, and a processing circuit 756 including a processor 758 and one or more algorithms. The one or more algorithms may include a bone balance adjustment algorithm 760. The bone balance adjustment algorithm 760 can analyze imaging data 722 using one or more linear relationships, nonlinear relationships, or both to generate and / or modify a treatment plan 620. In some examples, the bone balancing system 600 may include a “trained” artificial intelligence (AI) and / or machine learning system, which can learn and improve the relationships, patterns, etc. of preoperative data 720, outputs 730, actual results 650 (Figure 6), and determine and / or improve the bone balance adjustment algorithm 760 in some examples. The bone balancing system 600 and bone balance adjustment algorithm 760 may also be referred to as the bone balancing system 600 and bone balance adjustment algorithm 760, and / or the gap or soft tissue balancing system 600 or the gap or soft tissue balance adjustment algorithm 760.

[0060] The bone balancing system 600 may be implemented using one or more computing platforms, such as a platform including one or more computer systems and / or electronic cloud processing systems. Examples of one or more computing platforms include, but are not limited to, smartphones, wearable devices, tablets, laptop computers, desktop computers, Internet of Things (IoT) devices, remote server / cloud-based computing devices, or other mobile or fixed computers. The bone balancing system 600 may also include one or more hosts or servers connected to a network environment via wireless or wired connections. The remote platform may be implemented in or function as a base station (which may also be called a node B or evolved node B (eNB)). The remote platform may also include web servers, email servers, application servers, etc.

[0061] The bone balancing system 600 may include one or more communication modules (e.g., WiFi or Bluetooth modules) configured to communicate with the preoperative measurement system 722, the output system 770, and / or other third-party devices. For example, such communication modules may include an Ethernet card and / or port for sending and receiving data over an Ethernet®-based communication link or network, or a Wi-Fi transceiver for communication over a wireless communication network. Such communication modules may include wired or wireless interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for data communication with external sources via direct connection or network connection (e.g., Internet connection, LAN, WAN, or WLAN connection, LTE, 4G, 5G, Bluetooth, Near Field Communication (NFC), Radio Frequency Identifier (RFID), Ultra Wideband (UWB), etc.). Such communication modules may include a radio interface that generates symbols and transmits them over one or more downlinks, and receives the symbols (e.g., over an uplink), including filters, converters (e.g., digital-to-analog converters), mappers, fast Fourier transform (FFT) modules, and the like.

[0062] The memory system 752 may have one or more memories or storage configured to store or maintain preoperative data 720, output 730, and memory data 754 from previous patients and / or previous procedures. The preoperative data 720 and output 730 of the current procedure may also become memory data 754. Although the memory system 752 is shown near the processing circuit 756, the memory system 752 may be implemented on a separate circuit, housing, device, and / or computing platform and may include memories or storage that communicate with the bone balancing system 600, such as cloud storage systems and other remote electronic storage systems.

[0063] The memory system 752 may include one or more external or internal devices (such as random access memory or RAM, read-only memory or ROM, flash memory, hard disk storage or HDD, solid-state devices or SSDs, static storage such as magnetic or optical devices, or other types of non-temporary mechanical or computer-readable media) configured to store data and / or computer-readable code and / or instructions that complete, execute, or facilitate the various processes or instructions described herein. The memory system 20 may include volatile or non-volatile memory (e.g., semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory, or removable memory). The memory system 752 may include database components, object code components, script components, or any other type of information structure to support the various activities described herein. In some embodiments, the memory system 752 may be communicably connected to a processing circuit 756 and may include computer code for executing one or more processes described herein. The memory system 752 may include various modules, each of which may store data and / or computer code related to a specific type of function.

[0064] The processing circuit 756 may include a processor 758 configured to perform or execute one or more algorithms, including a bone balance adjustment algorithm 760, based on the received data, the received data may include preoperative data 720 and / or any data from the memory system 752 for determining the output 730. The preoperative data 720 may be received via manual input, retrieved from the memory system 752, and / or receive instructions from the preoperative measurement system 722. The processor 758 may be configured to determine a pattern based on the received data.

[0065] The processor 758 may be implemented as a general-purpose processor or computer, a dedicated computer or processor, a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), one or more field-programmable gate arrays (FPGAs), a group of processing components, a processor based on a multicore processor architecture, or other suitable electronic processing component. The processor 758 may be configured to execute machine-readable instructions, which may include one or more modules implemented as one or more functional logic, hardware logic, electronic circuits, software modules, etc. In some cases, the processor 758 may be separate from one or more computing platforms, including the bone balancing system 600. The processor 758 may be configured to perform one or more functions associated with the bone balancing system 600, such as overall control of one or more computing platforms, including the bone balancing system 600, including processes related to precoding antenna gain / phase parameters, encoding and decoding individual bits that form communication messages, formatting information, and managing communication resources and / or communication modules.

[0066] In some embodiments, the processing circuit 756 and / or the memory system 752 may include several modules related to the medical procedure, such as an input module, an analysis module, and an output module. The bone balancing system 600 does not need to be housed in a single housing. Rather, the components of the bone balancing system 600 may be located in various different locations, or even in separate locations. The components of the bone balancing system 600, including the components of the processing circuit 756 and the memory system 752, may be located, for example, within components of different computers, robotic systems, devices, etc., used in surgical procedures.

[0067] The bone balancing system 600 may make intermediate decisions and determine one or more outputs 730 using one or more algorithms, including a bone balance adjustment algorithm 760. One or more algorithms may be configured to make decisions or collect data from preoperative data 720, including imaging data 722. For example, one or more algorithms may be configured to make decisions for bone recognition, soft tissue recognition, and / or related to the aforementioned intermediate imaging data 722. One or more algorithms may operate simultaneously and / or separately to determine and / or display one or more outputs 730.

[0068] One or more algorithms include a bone balance adjustment algorithm 760. Furthermore, one or more algorithms may include one or more machine learning algorithms that are trained using, for example, linear regression, nonlinear regression, random forest regression, CatBoost regression, statistical shape modeling, or SSM. One or more algorithms may be continuously modified and / or improved based on actual outcomes and / or results 650 (Figure 6). One or more algorithms may be configured to determine one or more outputs 2722 using segmentation and / or thresholding techniques on received images, videos, and / or scans of imaging data 722. For example, one or more algorithms may be configured to segment images (e.g., CT scans, ultrasound images) and / or threshold soft tissue or identified bone, bone landmarks, osteophytes, etc. One or more algorithms may be configured to automate data extraction and / or collection when images are received from the image acquisition device 610. The bone balance adjustment algorithm 760 is described in more detail below.

[0069] One or more outputs 730 may, but are not limited to, a treatment plan 620. For example, one or more outputs 730 may include associated graphics, text, or a graphical user interface (GUI) configured to display the treatment plan 620, an anatomical representation of the patient, determined and / or enhanced images, etc., on a display 630 or other output system 770. Each of these outputs 730 may be used as input preoperative data 722 to determine the other outputs 730.

[0070] Output 730 may be output to any one or more of the output systems 770 via wireless, electronic, and / or wired connections. The output systems 770 may include a display 630, a mobile device 772, or a physical medium 774 such as paper, canvas, film, or other material medium.

[0071] Bone balance adjustment algorithm 760 To determine one or more bone resection parameters 762 and / or implant parameters 764, the bone balancing system 600 can execute a bone balance adjustment algorithm 760. The bone balance adjustment algorithm 760 may be a single algorithm and / or may include multiple algorithms that perform similar calculations (e.g., one algorithm for each type such as bone resection and soft tissue envelope) and / or related functions (e.g., bone recognition, surface area calculation, and bone parameter determination). For convenience of explanation, the bone balance adjustment algorithm 760 will be described as a single algorithm. Although the bone balancing system 600 is described as executing the bone balance adjustment algorithm 760, the embodiments disclosed herein are not limited to devices or systems that execute the bone balance adjustment algorithm 760.

[0072] As described above, the bone balance adjustment algorithm 760 may be configured to determine one or more osteotomy parameters 762 and / or one or more implant parameters 764 of the joint based on identified osteophytes, the location and / or dimensions of identified osteophytes, and their planned removal. The osteotomy parameters 762 may include relaxation parameters, osteotomy adjustments, osteotomy depth, osteotomy angle or inclination, or other adjustments that the surgeon may make during the procedure in which the osteotomy is performed. The implant parameters 764 may include one or more dimensions of the implant configured to balance with the joint and / or interarticular space, such as the thickness of the implant.

[0073] For example, the bone balance adjustment algorithm 760 may be configured to predict the amount of soft tissue relaxation that would result from removing identified osteophytes based on their size and / or location before removal. For example, if posterior osteophytes are present on the femur or tibia, the practitioner may plan to remove them. Because they are in a posterior location, it may only be possible to access the posterior osteophytes after performing tibial and femoral resections. When osteophytes are removed, the soft tissue envelope changes, substantially altering both the size and shape of the flexural and extensor spaces, and the pre-resection soft tissue "pose" may become inaccurate. Therefore, the bone balance adjustment algorithm 760 may predict the changes in soft tissue envelope relaxation and adjust (e.g., reduce) the bone resection parameters 762, implant parameters 764, and / or other parameters in the treatment plan 620.

[0074] Soft tissue laxity of a joint resulting from the removal of osteophytes can occur in both flexion and extension of the joint. For example, the soft tissue envelope may be lengthened by removing the osteophyte in a flexed and / or extended position, instead of being "tent-shaped" by the osteophyte due to having to move around it. If one or more posterior osteophytes are removed, this removal may result in laxity of the soft tissue envelope and / or ligaments extending around and / or above the removed osteophyte. In some cases, posterior osteophytes affect the extension and flexion of one or more ligaments. In other cases, posterior osteophytes may only result in the extension of one or more ligaments. In some cases, posterior osteophytes may result in part of the flexion space and part of the extension space.

[0075] In some examples, ligament length can be measured when wrapping around an osteophyte, and the joint can then be modeled without the osteophyte to predict the original ligament path and / or laxity. The amount of laxity that occurs may correlate with the dimensions of the osteophyte from which the soft tissue extends, such as the length, depth, height, or a combination thereof. In some other examples, the cross-section of the osteophyte can be calculated to determine the amount of laxity after the osteophyte is removed from the model. In another example, the length and / or volume of ligament displacement required to cross the osteophyte can be measured before removal in the generated model. This dimension (e.g., depth) is the surface area (e.g., square millimeters, i.e., mm²) shown in a given view (e.g., sagittal plane) of an image such as a CT image. 2 ) can be approximated by. In some cases, the ligamentary pathway can be measured around the osteophyte. Once the osteophyte is removed in the model, the ligamentary pathway distance before and after removal can be determined by comparing the ligamentary depth, height, and pathway distance to calculate the soft tissue laxity. In some cases, the change in laxity can be determined using the attachment points and pathways of one or more ligaments in the model without the osteophyte. Ligamentary attachment points may be junctions such as insertion sites, osteotendinous junctions, osteoligamentous junctions, musculotendinous junctions, and / or other junctions between the ligament and another anatomical part of the patient.

[0076] In some cases, to develop a treatment plan 620 based on the bone balancing system 600, the bone balancing system 600 may determine a change in ligament laxity by comparing a predicted ligamentary path (above) including one or more osteophytes present in the target bone with a second ligamentary path based on the removal of one or more osteophytes. The cross-sectional area of ​​the osteophytes may be used (further described below) to determine the change in laxity based on a comparison of the ligamentary path including the osteophytes with the proposed ligamentary path after the removal of the osteophytes.

[0077] The bone balance adjustment algorithm 760 may be configured to predict the laxity resulting from osteophyte removal and may determine changes in the osteotomy parameters 762 and / or implant parameters 764 (e.g., tibial liner thickness, implant recommendation, etc.) to accommodate the increased laxity. The bone balance adjustment algorithm 760 may analyze a given (e.g., sagittal) view of a joint (e.g., knee joint) and, based on imaging data and / or images acquired from the sagittal view (or other views) using the image acquisition device 610, identify one or more osteophytes that may not be readily removed before osteotomy (e.g., posterior osteophyte), determine the surface area of ​​each identified osteophyte, and determine the amount of laxity based on the determined surface area.

[0078] The determination may be based on learned relationships and / or linear equations. As an example of a linear equation, the bone balance adjustment algorithm 760 can perform an equation of the simple form y=mx+b, where "x" is mm 2is the surface area at, "y" is the amount of change in the bone resection depth in millimeters (mm), the amount of change in the angle or inclination of the bone resection in degrees, or the amount of change in the thickness of the implant in mm, "m" is a multiplier (e.g., an input by the operator learned and / or refined based on the results), and "b" is a constant. For example, in the context of the knee joint, "b" can be 0 and / or can be adjusted based on preoperative data 720 (e.g., patient data 724, treatment plan 620), and "m" can be in the range of 0.004 to 0.006 (e.g., 0.005, 0.0055, or 0.0056), provided that the embodiments disclosed herein are not limited. For example, "m" and "b" can have different values for different joints and / or can be adjusted based on patient data. "M" and "b" can also be further adjusted based on learned relationships (e.g., gender) or other preoperative data 720 (e.g., the condition of the cartilage or the progression of the disease), predictions (e.g., cartilage loss or predicted disease progression), intraoperative data, etc. The sign of "m" can depend on the symmetry of the bone spurs and / or the type of parameter being adjusted. For example, when "y" indicates the depth of the bone resection, "m" can be a negative value, indicating a decrease in the bone resection relative to the initial treatment plan 620. When "y" indicates the thickness of the implant, "m" can be positive, indicating an increase in the thickness of the implant relative to the thickness of the initial treatment plan 620. When "y" indicates the angle or inclination of the bone resection, the sign of "m" can depend on whether the bone spurs on one side of the joint are more or less than the bone spurs on the opposite side of the joint, as will be explained in more detail below.

[0079] For example, the bone balance adjustment algorithm 760 2 the removal of bone spurs having a related surface area of 90 mm 2 results in a relaxation of 0.5 mm of soft tissue based on a linear relationship of 0.5 mm per 90 mm and / or 0.1 mm per 18 mm 2 (e.g., at 180 mm 2 a relaxation of 1.0 mm of soft tissue occurs, at 270 mm 2 a relaxation of 1.5 mm of soft tissue occurs, at 18 mm 2This can be determined to result in soft tissue relaxation of 0.1 mm, for example. In some cases, the linear relationship is simplified to 100 mm for and / or for a specific increment. 2 0.5 mm and / or 20 mm each 2 Each can be rounded up to the nearest 0.1 mm. In other examples, nonlinear relationships may be used, such as an exponential correlation between osteophyte surface area and soft tissue relaxation.

[0080] In some other examples, nonlinear equations may be used to determine soft tissue laxity using one or more parameters of detected bone spurs. Nonlinear regression can relate two variables that are in a nonlinear (e.g., curvature) relationship. Nonlinear regression can be constructed through a series of approximations. These approximations can be determined using methods such as the Gauss-Newton method, the Levenberg-Marckert method, or a combination thereof. Machine learning can be used to generate multiple models to assist in the approximations and generate nonlinear regression data, thereby determining estimated soft tissue laxity or other parameters associated with processed medical images.

[0081] The bone balance adjustment algorithm 760 may be configured to determine and / or refine an equation based on the outcome 650. For example, while using a linear equation, the bone balance adjustment algorithm 760 may determine the predicted relaxation and / or change in the osteotomy parameter 762 and / or change in the implant parameter 764 by multiplying the surface area by 0.0055 or 0.0056, but the embodiments disclosed herein are not limited to this multiplier, and this multiplier may be adjusted and refined. In some other examples, the bone balance adjustment algorithm 760 may be configured to determine and / or refine a nonlinear equation based on the outcome 650. The bone balance adjustment algorithm 760 may also store an index (e.g., a table or database) of surface area and adjustments, which may be updated and / or refined based on the outcome 650.

[0082] Since soft tissue laxity may correlate with the length obtained by soft tissue that does not need to move around the osteophyte, certain dimensions of the osteophyte, such as width, may not be important as an indicator of the depth of soft tissue laxity. For ease of explanation, the distance traveled by a hiker crossing a mountain can depend primarily on two dimensions: (1) the height of the mountain, and (2) the scale of the forward movement of the hiker. However, the width of the mountain may be irrelevant. Similarly, in this case, only two dimensions may be relevant to the “distance” covered by the soft tissue, and width may not be relevant. For example, the ligament 204 in Figures 2 and 3 may be stretched primarily in the X and Z directions, but not in the direction of entering or leaving the page. Therefore, the bone balance adjustment algorithm 760 does not need to approximate all dimensions or volumes of the identified osteophyte, and may make its determination based primarily on two dimensions or the determined area. Thus, a given view may be a two-dimensional image facing the relevant dimensions to depict the extension of soft tissue and osteophytes in the relevant dimensions. In some other examples, a given view could be a three-dimensional model that can be manipulated by the user. In the context of the posterior osteophyte of the knee joint, this given view could be a sagittal view.

[0083] The bone balance adjustment algorithm 760 may also take into account the location of the osteophytes to be removed in order to predict the resulting soft tissue laxity and / or direction of laxity, and / or other bone resection parameters 762. For example, if the osteophytes are removed from the posterolateral femur or posterolateral tibia, the soft tissue laxity may be lateral. If the osteophytes are removed from the posteromedial femur or posteromedial tibia, the soft tissue laxity may be medial. If symmetrical posterolateral and posteromedial femoral or tibia osteophytes are present, the soft tissue laxity may be global. If asymmetrical posterolateral and posteromedial femoral or tibia osteophytes are present, the soft tissue laxity may be asymmetrical.

[0084] The bone resection parameter 762 may include adjustments and / or tilts of the angle determined based on the determined surface area of ​​the osteophyte and / or the determined laxity. For example, if the bone balance adjustment algorithm 760 determines that the osteophyte of the joint is medial, the bone balance adjustment algorithm 760 may determine that removal of the medial osteophyte will result in laxity of the medial joint space during flexion and extension. In some cases, the bone balance adjustment algorithm 760 may be adjusted and / or take into account the practitioner's preference, as this may not always match the degree of laxity that occurs during flexion of a particular joint, and it may also determine that removal of the medial osteophyte will result in laxity of the medial joint space during extension but less laxity (or no laxity at all) during flexion. For example, the bone balance adjustment algorithm 760 may change the type of bone for which less resection is recommended (e.g., from the tibia to the femur).

[0085] In relation to the knee joint, the bone balance adjustment algorithm 760 may determine the amount of change in the tibia to be resected in the treatment plan 620, anticipating the determined laxity of the medial space, based on the determined surface area of ​​the medial osteophyte. Angle adjustments may be made to accommodate the predicted laxity. In some cases, angle adjustments to the treatment plan 620 may include modifying the values ​​of the resection and / or resection angle related to the planned surgical resection or other surgical parameters. By changing the values ​​of the resection plane and / or angle, the joint may be adjusted to compensate for the removal of the osteophyte while maintaining the target laxity. In some cases, the treatment plan 620 may include modifications to the resection depth and / or resection angle, modifications to the joint positioning, or both, to adjust the placement of the implant relative to the bone and / or the implant relative to the implant. For example, if the bone balance adjustment algorithm 760 determines that the surface area of ​​the medial osteophyte is 90 mm 2 If this is determined, the bone balancing system will be 90mm 2 0.5° and / or 20mm per unit 2Based on a linear relationship of 0.1° for each, the osteoctomy parameter 762 for tibial valgus 0.5° (or 0.5° of tibial valgus per 0.5 mm of predicted flank, or 0.1° of tibial valgus per 0.1 mm of predicted flank) is determined (e.g., 180 mm). 2 Then, tibial eversion 1.0°, 270mm 2 Then, tibial eversion 1.5°, 20mm 2 For example, the tibial valgus may be 0.1°. For instance, the bone balancing system 600 may determine the predicted angle or inclination (e.g., tibial valgus) by multiplying the surface area by 0.0055 or 0.0056, but the embodiments disclosed herein are not limited to this multiplier and may be adjusted and refined. The bone balance adjustment algorithm 760 may also store an index of surface area and adjustment, which may be updated and / or refined based on the outcome 650.

[0086] In some embodiments, the bone balance adjustment algorithm 760 can calculate the amount of bone that needs to be removed and the appropriate resection angle to adjust for the laxity caused by the removal of bony prominences such as medial osteophytes or posteromedial bone flares. In some examples, a reduction osteotomy is performed by removing bony prominences by using a trial on the tibia as a reference. In some embodiments, the bone balance adjustment algorithm 760 can calculate the amount of bone that needs to be removed after removing both posteromedial bone flares and medial osteophytes, thereby extending the laxity of the medial soft tissue sleeve beyond what could be achieved by removing only the medial osteophyte. In some examples, the correction of varus deformity by medial reduction osteotomy results in a 1° correction for every 2 mm of bone removed. By identifying the correlation between bone removal and ligament deformity correction, the risk of excessive soft tissue release can be reduced.

[0087] Similarly, if the bone balance adjustment algorithm 760 determines that the joint bone spur is located laterally, it may determine that removal of the lateral bone spur will result in laxity of the lateral joint space during flexion and extension. In some cases, this may not align with the degree of laxity that occurs during flexion, and the bone balance adjustment algorithm 760 may be adjusted and / or take into account the practitioner's preference, and it may determine that removal of a medial bone spur primarily results in laxity of the medial joint space during extension, but less laxity (or no laxity) during flexion. In some cases, if a surgeon believes that the laxity resulting from bone spur removal will have a similar effect on the extensor and flexion spaces, they may compensate for the expected laxity by removing less bone from the tibia. For example, if removal of a posterior medial bone spur results in medial laxity during flexion and extension, the surgeon may reduce the amount of medial tibia resection. Similarly, removing posterior lateral osteophytes results in lateral laxity during flexion and extension, prompting surgeons to resect less of the lateral tibia. Removal of symmetrical medial and lateral osteophytes results in global laxity, necessitating proximal tibia resection by the surgeon. For surgeons who believe that posterior osteophyte removal only results in extension laxity, the strategy would be to resect less of the distal femur on the affected side, distally shift the femoral incision in the case of symmetrical posterior osteophytes, or asymmetrically distally shift the distal femur in the case of asymmetrical medial and lateral posterior femoral osteophytes.

[0088] In relation to the knee joint, the bone balance adjustment algorithm 760 can determine the amount of change in the tibia to be amputated in the treatment plan 620, based on the determined surface area of ​​the lateral osteophyte and anticipating the determined laxity of the lateral space. Angle adjustments can be made to accommodate the predicted laxity. For example, if the bone balance adjustment algorithm 760 determines that the surface area of ​​the lateral osteophyte is 90 mm 2 If this is determined, the bone balancing system will be 90mm 2 0.5° and / or 20mm per unit 2Based on a linear relationship of 0.1° for each, the osteoctomy parameter 762 for tibial varus of 0.5° (or 0.5° of tibial varus for every 0.5 mm of predicted flaccidity) is determined (e.g., 180 mm). 2 Then, tibia varus 1.0°, 270mm 2 Then, 1.5° varus of the tibia, 20mm 2 For example, tibial varus (0.1°) may be possible. The bone balance adjustment algorithm 760 may determine the predicted angle or inclination (e.g., tibial varus) by multiplying the surface area by 0.0055 or 0.0056, but the embodiments disclosed herein are not limited to this multiplier and may be adjusted and refined. The bone balance adjustment algorithm 760 may also store an index of surface area and adjustment, which may be updated and / or refined based on the outcome 650.

[0089] If the bone balance adjustment algorithm 760 identifies symmetrical osteophytes on the medial and lateral sides of a joint, the algorithm 760 may determine that removing these symmetrical osteophytes will result in global laxity in extension and / or flexion of the joint. In relation to the knee joint, the bone balance adjustment algorithm 760 may determine the amount of change in the tibia in anticipation of the determined laxity, based on the determined surface area of ​​the medial osteophyte. For example, if the bone balance adjustment algorithm 760 determines that the surface areas of the medial and lateral osteophytes are 90 mm² 2 If this is determined, the bone balance adjustment algorithm 760 will perform a tibial resection in the treatment plan 620 by 90 mm. 2 Each reduction reduces the tibial resection by 0.5 mm and / or 20 mm. 2 Based on a linear relationship where the tibial resection is reduced by 0.1 mm for each subsequent reduction, the tibial resection is reduced by 0.5 mm (for example, 180 mm). 2 In this case, the tibial resection is reduced by 1.0 mm, resulting in a 270 mm reduction. 2 In this case, the tibial resection is reduced by 1.5 mm, resulting in a 20 mm reduction. 2In this case, it may be determined that the tibial resection will be 0.1 mm less. Alternatively, the bone balance adjustment algorithm 760 may determine that the thickness of the liner used in the treatment plan 620 should be 0.5 mm or more (note: linear thickness available in 1 mm increments) and / or should be an increased thickness based on a linear relationship as the implant parameter 764.

[0090] If the bone balancing system 600 identifies medial and lateral osteophytes of different sizes, the bone balancing adjustment algorithm 760 may determine greater relaxation and / or adjustment of the treatment plan 620 (e.g., tibial resection) on the side with the larger osteophyte. For example, the bone balancing system 600 may determine the difference between the surface area of ​​the medial osteophyte and the surface area of ​​the lateral osteophyte. If the medial osteophyte is larger than the lateral osteophyte and the difference in surface area is 90 mm 2 In that case, the bone balance adjustment algorithm 760 is 90mm 2 0.5° and / or 20mm per unit 2 Based on a linear relationship of 0.1° for each, it was determined that the treatment plan 620 should include a 0.5° osteotomy parameter 762 for tibial valgus (e.g., 180 mm). 2 Then, tibial eversion 1.0°, 270mm 2 Then, tibial eversion 1.5°, 20mm 2 This can be determined to result in tibial eversion of 0.1°, etc. Similarly, if the lateral osteophyte is larger than the medial osteophyte and the difference in surface area is 90 mm 2 In that case, the bone balance adjustment algorithm 760 is 90mm 2 0.5° and / or 20mm per unit 2 Based on a linear relationship of 0.1° for each, it was determined that the treatment plan 620 should include a 0.5° osteoctomy parameter 762 for tibial varus (e.g., 180 mm). 2 Then, tibia varus 1.0°, 270mm 2 Then, 1.5° varus of the tibia, 20mm 2 This can be determined to result in conditions such as 0.1° of tibial eversion.

[0091] Figure 8 shows an exemplary graphical user interface of the bone balancing system 600 as displayed on the display 630. Referring to Figures 6-8, a practitioner (e.g., a surgeon) may use a computer or other system of the bone balancing system 600 to analyze one or more images of the patient's anatomical structure 802 (e.g., knee joint) and / or an initially determined treatment plan 620. One or more images of the patient's anatomical structure 802 and / or treatment plans 620 may be contained in a case file, patient folder, etc., selectable by the practitioner. One or more images of the patient's anatomical structure 802 may be acquired using an image acquisition device 610 and / or for the patient in various poses. The bone balancing system 600 may analyze one or more images of the patient's anatomical structure 802 to detect and / or scan for osteophytes. If one or more osteophytes are detected, the bone balancing system 600 may issue a notification or alert 804 (e.g., a pop-up or interactive box) indicating that osteophytes have been detected. In some cases, the bone balancing system 600 may determine that the treatment plan 620 is and / or should include the removal of osteophytes, but this removal is performed after the bone has been cut and / or affects the relaxation of soft tissue. In such cases, notification 804 may include text 806 (e.g., "Do you want to make adjustments to the treatment plan?") suggesting that adjustments be made to the treatment plan 620 based on the detected osteophytes, and may also present one or more selectable user inputs 808 (e.g., "Yes" or "No" buttons) which the practitioner can select. If the practitioner uses the selectable user inputs 808 to instruct and / or indicate the adjustments to be made, the bone balancing system 600 may execute the bone balancing adjustment algorithm 760 to determine the adjustments to the treatment plan 620 and / or determine a new treatment plan 620.

[0092] Figure 9 shows an image 900 of the patient's anatomical structure, including the femur 902, tibia 904, and osteophyte 906. Referring to Figures 6-9, the practitioner may select an image 900 from among several images to display (for example, using a computer with a display 630). The bone balancing system 600 may detect the osteophyte 906 and determine that the osteophyte 906 is likely to affect soft tissue laxity after removal. The bone balancing system 600 may determine that the osteophyte 906 is inaccessible until one or more osteotomies are performed on the femur 902 and / or tibia 904. Figures 10-11 show two examples of surface area detection and calculation by the bone balancing system 600. Figure 10 may show an example based on the osteophyte 106 shown in Figure 1, and Figure 11 may show an example based on the osteophyte 906 shown in Figure 9.

[0093] Referring to Figures 6-7 and 10, the bone balancing system 600 may identify an image 1000 that displays the maximum extent of the detected osteophyte 1002 in a predetermined dimension. In Figure 10, the image 1000 may show a sagittal view of the knee joint and display the maximum protrusion of the osteophyte 1002. The bone balancing system 600 may analyze the image 1000 to display the boundary or contour 1004 of the osteophyte 1002. For example, the bone balancing system 600 may perform segmentation or thresholding techniques, but the embodiments disclosed herein are not limited. Alternatively, or in addition, the practitioner may select an area of ​​the osteophyte 1002 and / or draw the boundary or contour 1004 of the bone balancing system 600 for analysis (e.g., using a mouse). The bone balancing system 600 may calculate and / or display the surface area 1006 of the osteophyte 1002, which may be based on the boundary or contour 1004. Using the surface area 1006, soft tissue laxity of soft tissue (e.g., ligaments) extending over the osteophyte 1002 can be determined or predicted. Similarly, referring to Figures 6-7 and 11, the bone balancing system 600 may identify an image 1100 that displays the maximum extent of the detected osteophyte 1102 in a predetermined dimension. In Figure 11, the image 1100 may show a sagittal view of the knee joint and display the maximum protrusion of the osteophyte 1102. The bone balancing system 600 may analyze the image 1100 to display the boundary or contour 1104 of the osteophyte 1102. For example, the bone balancing system 600 may perform segmentation or thresholding techniques, but the embodiments disclosed herein are not limited. Alternatively, or in addition, the practitioner may select an area of ​​the osteophyte 1102 and / or draw the boundary or contour 1104 of the bone balancing system 600 for analysis (e.g., using a mouse). The bone balancing system 600 can calculate and / or display the surface area 1106 of the osteophyte 1102, which may be based on the boundary or contour 1104. Using the surface area 1106, soft tissue laxity of soft tissues (e.g., ligaments) extending over the osteophyte 1102 can be determined or predicted.

[0094] Figure 12 shows an exemplary preoperative method 1200 for adjusting the preoperative or treatment plan based on detected osteophytes. Referring to Figure 12, method 1200 may be performed by a bone balancing system 600 (e.g., using a bone balancing adjustment algorithm 760) and / or by the practitioner. Method 1200 may include step 1202 of receiving one or more images of the patient's anatomical structure, such as one or more CT scans, ultrasound images, or X-rays. Step 1202 may include, for example, receiving one or more images from an image acquisition device or other system and placing them into electronic storage. Method 1200 may include step 1204 of receiving one or more treatment plans, including one or more osteotomies. The treatment plans may also include implants configured to be received in the bone in one or more osteotomies. Step 1204 may include receiving one or more treatment plans from memory, by manual input by the practitioner, from a remote system that has determined one or more treatment plans, and / or determining one or more treatment plans using preoperative data 720, for example, as described with reference to Figure 7.

[0095] Method 1200 may include step 1206 of detecting one or more osteophytes in one or more received images. Step 1206 may include using image processing techniques to identify osteophytes and / or using a machine learning system trained to detect osteophytes (e.g., autonomous segmentation and / or calculation of the size and location of osteophytes). Alternatively, or in addition, step 1206 may include receiving input from a practitioner (e.g., via a computer) and / or receiving annotations indicating one or more osteophytes in one or more received images. Step 1206 may include detecting osteophytes and determining whether each detected osteophyte is reachable and / or not reachable for removal before bone cutting. Step 1206 may include determining whether each detected osteophyte is still not reachable after bone cutting. In step 1206, if it is determined that the detected osteophyte is inaccessible (or extremely difficult to access) even after osteotomy, the treatment plan and / or anticipated soft tissue treatment may be adjusted to take into account that such osteophyte will not be removed (which may result in less change to soft tissue laxity).

[0096] Method 1200 may include step 1208 of detecting the location or position of one or more detected osteophytes. Step 1208 may include detecting the side or compartment (e.g., medial or lateral) of the one or more detected osteophytes relative to the bone, and / or, if one or more detected osteophytes are detected, determining the coordinates or other positional parameters of the one or more detected osteophytes relative to the bone and / or to each other. Step 1208 may include detecting the position using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. Alternatively, or in addition, step 1208 may include receiving input from a practitioner (e.g., via computer) and / or annotations indicating the location of one or more osteophytes in one or more received images.

[0097] Method 1200 may include step 1210 of determining the cross-sectional area or surface area of ​​one or more detected osteophytes. Step 1210 may include detecting the surface area using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. For example, step 1210 may include determining or detecting the boundaries of bone (e.g., tibia, femur), osteophytes, or both in the images and calculating the area within the boundaries. Once the boundaries of bone, osteophytes, or both in the images are identified, a model of the joint may be created using the image data. In some examples, the joint model may include one or more osteophytes, or may be created without one or more osteophytes. In one example, a model without osteophytes may be created by subtracting the respective boundaries and / or volumes of one or more osteophytes from the image data. Alternatively, or in addition, step 1210 may include receiving input from a practitioner (e.g., via a computer) and / or annotations indicating the surface area of ​​one or more osteophytes in one or more received images. The determined cross-sectional area may be output to a display or other output system.

[0098] In some examples, step 1210 may include identifying from among several received images the image that best reflects the surface area of ​​the osteophyte. For example, step 1210 may include identifying from among several received images one or more images that show the patient's anatomical structure (e.g., joints) across a first predetermined dimension and a second predetermined dimension in which soft tissue may extend. In the context of a knee joint, one or more identified images may be sagittal views of the knee joint. Step 1210 may further include identifying, among the identified images that reflect the first and second predetermined dimensions (e.g., sagittal views), the image that shows the maximum extent of the osteophyte in one of the first and second predetermined dimensions. In some examples, step 1210 may include determining the detected surface area of ​​the osteophyte based on multiple images of the same view (e.g., sagittal view) that include the osteophyte, and identifying calculated values ​​for the maximum surface area, the average surface area, and / or the median surface area. In some examples, step 1210 may include determining the surface area of ​​the detected osteophyte and / or the volume of the detected osteophyte based on multiple images, but the algorithm may be simplified by considering the surface area of ​​the two most important dimensions and ignoring the dimensions of the osteophyte that do not affect soft tissue relaxation. In other examples, step 1210 may include determining the height or depth of the osteophyte in one of the given dimensions instead of, or in addition to, the two-dimensional surface area of ​​the detected osteophyte, if, for example, the surface area cannot be calculated (e.g., due to poor image quality). In such situations, the bone balance adjustment algorithm 760 may be adjusted to consider a single dimension in order to approximate the effect on soft tissue relaxation.

[0099] Method 1200 may include step 1212 of determining the symmetry of the detected osteophytes based on their detected location and / or determined cross-sectional area. For example, if multiple osteophytes are detected in step 1202, step 1212 may include determining that the osteophytes are symmetrical and / or asymmetrical based on their detected location and size. Step 1212 may include determining that two osteophytes are symmetrical if they are the same or similar in size (e.g., the difference in determined cross-sectional area is less than a predetermined threshold) and are located on opposite sides of the joint. Step 1212 may include determining that two osteophytes are asymmetrical if they are different in size (e.g., the difference in determined cross-sectional area exceeds a predetermined threshold) and / or occur on the same side of the joint (and / or occur on the side that is not opposite the joint). Asymmetry in step 1212 may also be determined based on the detected osteophytes being located at different distances or apart from a given axis.

[0100] Step 1212 may include using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. Alternatively, or in addition, Step 1212 may include receiving input from a practitioner (e.g., via a computer) and / or receiving annotations indicating the symmetry of bone spurs in one or more of the received images.

[0101] Method 1200 may include step 1214 of determining one or more osteoctomy parameters or implant parameters based on the determined cross-sectional area and / or determined symmetry. Step 1214 of determining one or more osteoctomy parameters and / or implant parameters may include determining changes to the osteoctomy parameters and / or implant parameters included in the initially received treatment plan, such as adjusting the angle of resection to adjust the joint and / or adjusting the value of the resection angle. Step 1214 may also include identifying changes and / or amounts of soft tissue laxity after the removal of detected osteophytes. The determination made in step 1214 may be output to a display or other output system, for example, as instruction text and / or a list of steps, as a chart, or visually on a virtual bone model.

[0102] Step 1214 may involve executing the bone balance adjustment algorithm 760, which may implement one or more linear relationships as previously described with respect to Figure 6. The linear relationships may be input and / or refined by the practitioner. Alternatively, or in addition to that, the linear relationships may be learned or refined through the evaluation of multiple patients. In some examples, the linear relationships may be stored and / or implemented using an index database, using known surface area quantities, known symmetry, and stepwise relationships of corresponding changes in one or more osteotomy parameters or implant parameters. In some examples, the linear relationships may include equations with multipliers or slopes that are multiplied by the surface area to result in one or more osteotomy parameters or implant parameters.

[0103] In some other examples, the bone balance adjustment algorithm 760 can implement one or more nonlinear relationships. These nonlinear relationships can be input and / or refined by the practitioner. Alternatively, or in addition, nonlinear relationships can be learned or refined through the evaluation of multiple patients. In some examples, nonlinear relationships can be stored and / or implemented using an index database, using known surface area quantities, known symmetries, and stepwise relationships of corresponding changes to one or more osteosurgery parameters or implant parameters. In some examples, the nonlinear relationships may include equations with multipliers or slopes that are multiplied by the surface area of ​​the target region (e.g., one or more osteophytes, ligaments, bone, etc.) to result in one or more osteosurgery parameters or implant parameters.

[0104] Method 1200 may include step 1216, which adjusts one or more treatment plans based on the determined osteotomy parameters and / or implant parameters derived from step 1214. Step 1216 may be performed by the bone balance adjustment algorithm 760. Alternatively, or in addition, the practitioner may manually adjust the treatment plans based on the determined osteotomy parameters and / or implant parameters derived from step 1214.

[0105] For example, in steps 1214 and 1216, the bone balance adjustment algorithm 760 determines the surface area (e.g., 90 mm) 2 or 20mm 2It can be determined that the removal of an osteophyte having ) will result in a determined amount of soft tissue relaxation (e.g., 0.5 mm or 0.1 mm), that the bone resection parameter should be reduced by a predetermined amount (e.g., 0.5 mm or 0.1 mm), or that the implant parameter (e.g., thickness) should be increased by a predetermined amount (e.g., 0.5 mm or 0.1 mm), and / or, based on symmetry, that the angle of bone resection should change by a predetermined amount (e.g., 0.5° or 0.1°) in a predetermined direction or orientation (e.g., varus or eversion). Examples of these adjustments are described in more detail with respect to Figures 6 and 13-14. After steps 1214 and 1216, the treatment plan may be updated by adjustments so that the surgeon can execute the treatment plan during surgery. The entire method 1200 may be performed before the procedure and before any bone resection is performed. Alternatively, or in addition, steps 1214 and / or 1216 may be repeated based on new intraoperative information received regarding bone spurs (e.g., those identified during surgery but not captured by received images), their location, and / or their size. The decisions made during method 1200 may be output to a display as, for example, a virtual model, a list of procedures, or a final treatment plan. In some examples, the decisions and / or the finalized treatment plan may be output as a set of programmed instructions configured to be executed by a robotic platform 640 (e.g., CNC instructions) that can assist in bone cutting or other steps in the medical procedure.

[0106] Figure 13 shows exemplary preoperative and / or intraoperative methods 1300 for adjusting the treatment plan based on detected osteophytes. Some steps of method 1300 are similar to steps of method 1200, and for ease of explanation, repeating or similar parts of the description of certain steps in method 1300 may be omitted.

[0107] Method 1300 may include the steps of: receiving an initial treatment plan (step 1302); identifying or detecting one or more osteophytes in one or more received images of the joint (step 1304); and locating the one or more identified osteophytes (step 1306). Furthermore, Method 1300 may include the step of identifying an image among multiple images having multiple views that shows the maximum dimension of the one or more identified osteophytes (e.g., a sagittal view). Steps 1302-1308 may include using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. Alternatively, or in addition, steps 1302-1308 may include receiving input from a practitioner (e.g., via a computer) and / or receiving annotations (e.g., on one or more received images).

[0108] Method 1300 uses the images identified in step 1308, which have a view showing the maximum dimension, to determine the surface area and / or cross-sectional area of ​​one or more identified osteophytes in mm². 2 Step 1310 may include identifying the surface area and / or cross-sectional area using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. Alternatively, or in addition, Step 1310 may include receiving input from a practitioner (e.g., via a computer) and / or annotations indicating the surface area and / or receiving the surface area from the input.

[0109] Method 1300 may include step 1312 of determining the size and symmetry of one or more identified osteophytes. Determining the size in step 1312 may include using the surface area determined in step 1310 and / or determining the size of another parameter of the osteophyte (e.g., volume) using, for example, image processing techniques (e.g., autonomous segmentation and / or calculation of osteophyte size and location). In the case of multiple osteophytes, determining the size in step 1312 may include determining the size difference between the osteophytes. Determining symmetry in step 1312 may be based on the location determined in step 1306 and the determined size difference of the osteophytes.

[0110] Method 1300 may include step 1314 of performing one or more poses. Step 1314 may include applying stress to the soft tissues of the joint in both flexion and extension by applying predetermined forces (e.g., varus and eversion forces). Images and / or other preoperative data 720 may be acquired for each pose. In step 1314, poses may be performed according to the practitioner's preference. Step 1314 may also include determining the size and shape of the flexion and extension spaces. Step 1314 may include adjusting the initial treatment plan received in step 1302 based on the determined size and shape of the flexion and extension spaces. Alternatively, or in addition, step 1314 may be performed before step 1302, and the treatment plan received in step 1302 may include parameters and / or adjustments determined based on one or more poses performed. Alternatively, or in addition, step 1314 may be performed immediately before the procedure and / or during the surgery, prior to the bone cutting.

[0111] Method 1300 may include making further adjustments, as outlined in steps 1316–1322. In step 1316, if it was determined in step 1312 that symmetrical osteophytes are present (e.g., by the bone balancing system 600 and / or the practitioner), the bone balancing adjustment algorithm 760 may determine whether the osteotomy in the treatment plan (e.g., tibial and / or femoral osteotomy) should be reduced by a predetermined amount, or whether the implant thickness should be increased by a predetermined amount. In some examples, the adjustment algorithm 760 may determine which type of osteotomy (e.g., tibial and / or femoral osteotomy) should be adjusted and / or receive practitioner input on the type of osteotomy. Alternatively, or in addition, the adjustment algorithm 760 may receive practitioner input related to the expected effects of laxity (e.g., indicating whether the practitioner expects laxity to be affected in both extension and flexion, or primarily in extension) or other practitioner preferences, and the adjustment algorithm 760 may determine the type of osteotomy based on the practitioner input.

[0112] In step 1318, if step 1312 determines that asymmetrical osteophytes exist and that the first (e.g., lateral) osteophyte is larger than the second (e.g., medial) osteophyte, the bone balance adjustment algorithm 760 may determine that adjustments are necessary, such as increasing the first alignment parameter (e.g., the angle of osteotomy in the first direction, or varus) by a predetermined amount. In step 1320, if step 1312 determines that asymmetrical osteophytes exist and that the second (e.g., medial) osteophyte is larger than the first (e.g., lateral) osteophyte, the bone balance adjustment algorithm 760 may determine that adjustments are necessary, such as increasing the second alignment parameter (e.g., the angle of osteotomy in the second direction, or valgus) by a predetermined amount.

[0113] In step 1322, if it is determined in step 1312 that asymmetrical osteophytes exist and each osteophyte is larger than a predetermined size, and / or the difference in the size of the osteophytes is greater than a predetermined difference, the bone balance adjustment algorithm 760 can adjust the first alignment parameter and / or the second alignment parameter by a predetermined amount by determining a combination of adjustments, such as reducing the amount of bone resection by a predetermined amount or increasing the thickness of the implant by a predetermined amount. The determination made in steps 1312 to 1322 can be output to a display or other output system, for example, as a chart, visually output to a virtual bone model, as command text and / or a list of steps. In some examples, the determination and / or the finalized treatment plan can be output as a set of programmed commands configured to be executed by a robotic platform 640 (e.g., CNC commands) that can assist in bone resection or other steps in a medical procedure.

[0114] Method 1300 may include step 1324, which involves positioning a trial implant and evaluating its stability and range of motion. Step 1324 may be performed intraoperatively after the bone cut has been made, and further adjustments may be made intraoperatively.

[0115] Figure 14 shows an exemplary method 1400 for adjusting the treatment plan for knee surgery (e.g., total or partial knee arthroplasty) based on detected osteophytes. Some of the steps in method 1400 are similar to the steps in method 1200 and / or method 1300, and for ease of explanation, repeating or similar portions of the description of certain steps in method 1400 may be omitted.

[0116] Method 1400 may include step 1402 of receiving an initial treatment plan. Method 1400 may include step 1404 of identifying or detecting one or more posterior osteophytes in one or more images of the knee joint, for example, by analyzing one or more images. Step 1404 may include receiving one or more images. One or more received images may include one or more preoperative lateral or sagittal view images that may have been acquired using an X-ray machine, an ultrasound machine, and / or a CT scan machine.

[0117] Method 1400 may include step 1406 of identifying the location and / or approximate compartment (e.g., lateral or medial) of the identified posterior osteophyte. Step 1406 may include identifying the location and / or position of the identified posterior osteophyte in one or more sagittal views of the images.

[0118] Method 1400 may include step 1408 of identifying a sagittal plane image or view from among multiple images showing the maximum dimension (e.g., depth or height) of one or more identified posterior osteophytes. Steps 1402–1408 may include using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. Alternatively, or in addition thereto, steps 1402–1408 may include receiving input from a practitioner (e.g., via computer) and / or receiving annotations (e.g., on one or more received images).

[0119] Method 1400 uses the images identified in step 1408, which have a view showing the maximum dimension, to determine the surface area and / or cross-sectional area of ​​one or more identified posterior osteophytes in mm². 2Method 1400 may include step 1410, which identifies the bone spurs. Method 1400 may include step 1412, which determines the size and symmetry of one or more identified posterior bone spurs. Determining the size in step 1412 may include using the surface area determined in step 1410 and / or determining the size of another parameter of the bone spur (e.g., volume) using, for example, image processing techniques (e.g., autonomous segmentation and / or calculation of bone spur size and location). Determining the size in step 1412 may include determining the difference in size between a detected outer bone spur and a detected inner bone spur. Determining the symmetry in step 1412 may be based on the location determined in step 1406 and the difference in the determined size of the bone spurs.

[0120] Method 1400 may include step 1414 of performing one or more poses. Step 1414 may include applying stress to the medial and lateral soft tissues of the joint in both flexion and extension by applying varus and valgus forces. Images and / or other preoperative data 720 may be acquired for each pose. Step 1414 may also include determining the size and shape of the flexion and extensor spaces. Step 1414 may include adjusting the initial treatment plan received in step 1402 based on the determined size and shape of the flexion and extensor spaces. Alternatively, or in addition, step 1414 may be performed before step 1402, and the treatment plan received in step 1402 may include parameters and / or adjustments determined based on one or more poses performed. Alternatively, or in addition, step 1414 may be performed immediately before the procedure and / or during the surgery, before the osteotomy.

[0121] Method 1400 may include making further adjustments, as outlined in steps 1416-1422. In step 1416, if it is determined in step 1412 that symmetrical osteophytes are present (e.g., by the bone balance system 600 and / or the practitioner), the bone balance adjustment algorithm 760 determines that tibial resection in the treatment plan is based on the determined surface area of ​​the determined osteophyte, which is 90 mm². 2The thickness should be reduced by 0.5 mm per unit area, or the implant thickness should be such that the determined surface area is 180 mm². 2 It may be determined that the area should be increased by 1 mm for each step. However, the embodiments disclosed herein are not limited to these specific linear relationships. For example, based on other preoperative data 720, the bone balance system 600 may determine that the tibia should be resected to a determined surface area of ​​95 mm 2 0.5 mm or 90 mm per unit 2 It can be determined that the reduction should be around 0.4 mm per unit area.

[0122] In step 1418, if asymmetrical bone spurs were found in step 1412, and the lateral bone spur was determined to be larger than the medial bone spur, the bone balance adjustment algorithm 760 determines a surface area asymmetry of 90 mm. 2 For each, or the difference of 90 mm between the surface area of ​​the lateral osteophyte and the surface area of ​​the medial osteophyte. 2 For each case, it may be determined that the resection angle should be adjusted by 0.5° inversion of the tibia. Resection of the varus tibia may also be performed by fixing the pivot axis medially and removing little bone from the lateral side of the tibia.

[0123] In step 1420, if asymmetrical bone spurs were found in step 1412, and the medial bone spur was determined to be larger than the lateral bone spur, the bone balance adjustment algorithm 760 determines a surface area asymmetry of 90 mm. 2 For each, or the difference of 90 mm between the surface area of ​​the lateral osteophyte and the surface area of ​​the medial osteophyte. 2 For each case, it may be determined that the resection angle should be adjusted by 0.5° of tibial valgus. Resection of tibial valgus may also be performed by fixing the pivot axis laterally and removing little medial bone from the tibia.

[0124] In step 1422, asymmetrical osteophytes were present in step 1412, with each of the medial and lateral osteophytes measuring 90 mm. 2 It has a surface area exceeding [a certain value], and the difference in surface area between the medial and lateral osteophytes is 90 mm. 2If it is determined that the value exceeds a certain threshold, the bone balance adjustment algorithm 760 can determine a combination of adjustments, such as reducing the resection, increasing the implant thickness, and / or adjusting the varus and / or valgus of the tibia. The determination made in steps 1412-1422 may be output to a display or other output system, such as a chart, a chart, or a visual output to a virtual bone model, for example, as a list of instruction text and / or steps. In some examples, the determination and / or finalized treatment plan may be output as a set of programmed instructions configured to be executed by a robotic platform 640 (e.g., CNC instructions) that can assist in bone resection or other steps in the medical procedure, such as ligament release (e.g., ligament cutting or decoupling). In some examples, ligament release may be used in conjunction with additional bone resection(s). In other examples, only ligament release may be included in the treatment plan, and bone resection may not be included.

[0125] Method 1400 may include step 1424, which is to position a trial implant and evaluate its stability and range of motion. Step 1424 may be performed in surgery after the osteotomy has been performed, and further adjustments may be made in surgery. Although Method 1400 describes knee surgery, embodiments disclosed herein may be used to make adjustments to other joints (e.g., elbow, hand, spine, ankle, foot, neck, etc.), in particular to other joints where osteophytes or other growths may form and / or need to be removed (e.g., to increase mobility and / or to reduce pain or impact).

[0126] Referring to Figure 15, some practitioners may disagree with the degree of laxity that occurs during flexion of certain joints, such as in relation to the knee joint, and the bone balance adjustment algorithm 760 may adjust and / or take into account the practitioner's preference, for example, determining that removal of medial osteophytes will result in laxity in the medial joint space, mainly during extension, but less (or no) laxity during flexion. Depending on the practitioner's preference, Method 1500 may be performed on the knee joint in place of or in addition to the earlier methods described herein. Some of the steps of Method 1500 are similar to the steps of Methods 1200, 1300, and / or Method 1400, and for convenience of explanation, repeating or similar parts of the description of certain steps in Method 1500 may be omitted. Method 1500 may differ from Method 1400 by predicting adjustments for femoral resection, femoral varus, and / or femoral valgus, rather than tibial resection, tibial varus, and / or tibial valgus.

[0127] Method 1500 may include step 1502 of receiving an initial treatment plan. Method 1500 may include step 1504 of identifying or detecting one or more posterior osteophytes on one or more images of the knee joint, for example, by analyzing one or more images. Step 1504 may include receiving one or more images. One or more received images may include one or more preoperative lateral or sagittal view images that may have been acquired using an X-ray machine, an ultrasound machine, and / or a CT scan machine.

[0128] Method 1500 may include step 1506 of identifying the location and / or approximate compartment (e.g., lateral or medial) of the identified posterior osteophyte. Step 1506 may include identifying the location and / or position of the identified posterior osteophyte in one or more sagittal views of the images.

[0129] Method 1500 may include step 1508 of identifying a sagittal plane image or view from among multiple images showing the maximum dimension (e.g., depth or height) of one or more identified posterior osteophytes. Steps 1502–1508 may include using image recognition and / or processing techniques and / or analyzing multiple images of the same joint. Alternatively, or in addition thereto, steps 1502–1508 may include receiving input from a practitioner (e.g., via a computer) and / or receiving annotations (e.g., on one or more received images).

[0130] Method 1500 uses the images identified in step 1508, which have a view showing the maximum dimension, to determine the surface area and / or cross-sectional area of ​​one or more identified posterior osteophytes in mm². 2 Method 1500 may include step 1510, which identifies the bone spurs. Method 1500 may include step 1512, which determines the size and symmetry of one or more identified posterior bone spurs. Determining the size in step 1512 may include using the surface area determined in step 1510 and / or determining the size of another parameter of the bone spur (e.g., volume) using, for example, image processing techniques (e.g., autonomous segmentation and / or calculation of bone spur size and location). Determining the size in step 1512 may include determining the difference in size between a detected outer bone spur and a detected inner bone spur. Determining the symmetry in step 1512 may be based on the location determined in step 1506 and the difference in the determined size of the bone spurs.

[0131] Method 1500 may include step 1514 of performing one or more poses. Step 1515 may include applying stress to the medial and lateral soft tissues of the joint in both flexion and extension by applying varus and valgus forces. Images and / or other preoperative data 720 may be obtained for each pose. Step 1514 may also include determining the size and shape of the flexion space and / or extensor space. Step 1514 may include adjusting the initial treatment plan received in step 1502 based on the determined size and shape of the flexion space and / or extensor space. Alternatively, or in addition, step 1514 may be performed before step 1502, and the treatment plan received in step 1502 may include parameters and / or adjustments determined based on one or more poses performed. Alternatively, or in addition, step 1514 may be performed immediately before and / or during the procedure, before the osteotomy.

[0132] Method 1500 may include making further adjustments, as outlined in steps 1516-1522. In step 1516, if it is determined in step 1512 that symmetrical osteophytes are present (e.g., by the bone balance system 600 and / or the practitioner), the bone balance adjustment algorithm 760 determines that femoral resection (e.g., distal femoral resection) in the treatment plan is based on the determined surface area of ​​the determined osteophyte, which is 90 mm². 2 The thickness should be reduced by 0.5 mm per unit area, or the implant thickness should be such that the determined surface area is 90 mm. 2 0.5 mm per unit area or 180 mm² of determined surface area. 2 It may be determined that the area should be increased by 1 mm for each step. However, the embodiments disclosed herein are not limited to these specific linear relationships. For example, based on other preoperative data 720, the bone balance system 600 may determine that the femoral resection should be 95 mm in the determined surface area. 2 0.5 mm or 90 mm per unit 2 It can be determined that the reduction should be around 0.4 mm per unit area.

[0133] In step 1518, if asymmetrical bone spurs were found in step 1512, and the lateral bone spur was determined to be larger than the medial bone spur, the bone balance adjustment algorithm 760 determines a surface area asymmetry of 90 mm. 2 For each, or the difference of 90 mm between the surface area of ​​the lateral osteophyte and the surface area of ​​the medial osteophyte. 2 For each case, it may be determined that the resection angle should be adjusted by 5° of femoral varus. Resection of a varus femur may also be performed by fixing the pivot axis medially and removing little of the lateral bone from the femur.

[0134] In step 1520, if asymmetrical bone spurs were found in step 1512, and the medial bone spur was determined to be larger than the lateral bone spur, the bone balance adjustment algorithm 760 determines a surface area asymmetry of 90 mm. 2 For each, or the difference of 90 mm between the surface area of ​​the lateral osteophyte and the surface area of ​​the medial osteophyte. 2 For each case, it may be determined that the resection angle should be adjusted by 5° of femoral valgus. Resection of the valgus femur may also be performed by fixing the pivot axis laterally and removing little of the medial bone from the distal femur.

[0135] In step 1522, asymmetrical bone spurs were present in step 1512, with each of the medial and lateral bone spurs measuring 90 mm. 2 It has a surface area exceeding [a certain value], and the difference in surface area between the medial and lateral osteophytes is 90 mm. 2 If it is determined that the value exceeds a certain threshold, the bone balance adjustment algorithm 760 may determine a combination of adjustments, such as reducing the amount of resection (e.g., distal femoral resection), increasing the thickness of the implant, and / or adjusting the varus and / or valgus of the femur. The determination made in steps 1512-1522 may be output to a display or other output system, such as a chart, a visual output to a virtual bone model, for example, as a list of instruction text and / or steps. In some examples, the determination and / or finalized treatment plan may be output as a set of programmed instructions configured to be executed by a robotic platform 640 (e.g., CNC instructions) that can assist in bone cutting or other steps in the medical procedure.

[0136] Method 1500 may include step 1524, which is to position a trial implant and evaluate its stability and range of motion. Step 1524 may be performed in surgery after the osteotomy has been performed, and further adjustments may be made in surgery. Although Method 1500 describes knee surgery, embodiments disclosed herein may be used to make adjustments to other joints (e.g., elbow, hand, spine, ankle, foot, neck, etc.), in particular to other joints where osteophytes or other growths may form and / or need to be removed (e.g., to increase mobility and / or to reduce pain or impact).

[0137] Referring to Figure 16, the bone balance adjustment algorithm 760 may be adjusted based on whether the practitioner believes or anticipates that the removal of bone spurs will affect relaxation in both extension and flexion, or primarily in extension. Alternatively, or in addition, the bone balance adjustment algorithm 760 may include a first algorithm and a second algorithm, and the bone balancing system 600 may execute the first algorithm if the practitioner believes or anticipates that the removal of bone spurs will affect relaxation in both extension and flexion, and the second algorithm if the practitioner believes or anticipates that the removal of bone spurs will affect relaxation primarily in extension. Method 1600 may include a step 1602 of determining whether adjustment should be made based on the prediction that relaxation will occur in both extension and flexion due to the removal of bone spurs. Step 1602 may include receiving input from the practitioner by bringing a notification and / or graphical user interface element (e.g., a pop-up notification) to the practitioner for input (e.g., via the display 630) and receiving the input. If, in step 1602, it is determined that the adjustment should be based on relaxation occurring in both extension and flexion (yes after step 1602), then method 1400, as described in Figure 14, may be performed. If, in step 1602, it is determined that the adjustment should not be based on relaxation occurring in both extension and flexion, and / or that the adjustment should be based primarily on relaxation occurring in extension (no after step 1602), then method 1500, as described in Figure 15, may be performed. In some examples, step 1602 may be performed after certain steps of method 1400 and / or method 1500. For example, steps 1402-1414 may be performed, and step 1602 may be performed to determine the way to proceed (e.g., by continuing with steps 1416-1424, or by performing steps 1516-1524 instead). In another example, steps 1502–1516 may be performed, and step 1602 may be performed to determine the course of action (for example, by continuing with steps 1516–1524 or by performing steps 1416–1424 instead).In some cases, the bone balancing system 600 may output recommendations based on a determination made in any of methods 1300, 1400, 1500, and / or 1600. In some cases, the recommendations may simply refer to a “bone” resection, and the surgeon may decide which bone to resection. For example, in relation to the knee joint, a surgeon may decide whether to make the recommended modification to the tibia if the surgeon believes that the soft tissue laxity resulting from the removal of a posterior osteophyte affects flexion and extension equally, or whether to make the recommended modification to the distal femur if the surgeon believes that the soft tissue laxity resulting from the removal of a posterior osteophyte affects extension only.

[0138] As mentioned above, removing medial osteophytes during medical procedures can cause previously stretched soft tissue to become lax, which may not fully retract to its original state. However, other conditions, such as posteromedial flaring of the bone, can also contribute to soft tissue stretching and joint laxity. While this can sometimes be corrected by releasing the soft tissue, such techniques require very high surgical skill, and this procedure can frequently lead to overcorrection of deformities, such as varus deformity. As will be discussed later, using the tibia as a reference, a reduced osteotomy may be performed to remove bony protrusions such as posteromedial flaring of the bone. A reduced osteotomy may be performed before or after removal of medial osteophytes and / or additional osteotomy, or as a separate procedure.

[0139] Secondary or complementary surgical steps are outlined in the following sections. The following surgical steps involve the use of robot-assisted planning and execution of pre-excision medial reduction osteotomy during medical procedures (e.g., TKA or PKA) to improve coronal alignment. In at least one embodiment, the surgical method can assist in correcting varus deformity.

[0140] The method may be performed by a bone balancing system 600 (e.g., using a bone balance adjustment algorithm 760) and / or by a practitioner. The method may include the step of receiving one or more images of the patient's anatomical structure, such as one or more CT scans, ultrasound images, or X-rays. The method may include receiving one or more images and placing them into electronic storage, and receiving one or more treatment plans, including one or more osteotomies. The treatment plans may also include implants configured to be received in the bone in one or more osteotomies. The method may also include determining one or more treatment plans using preoperative data 720, as described with reference to Figure 7, for example.

[0141] Based on the received images, the method may include image processing techniques to identify bony prominences such as posteromedial bone flares and / or other attributes of the patient's bone. However, it will be understood that identification may be performed by receiving input from the practitioner (e.g., via a computer). The method may then include determining various features of the identified bone flares, such as surface area or shape. Following this, the parameters for bone resection and implantation are established. Once these initial determinations are made, the method may generally involve executing the bone balance adjustment algorithm 760, which utilizes the linear relationships described in Figure 6. The practitioner can make inputs or refine the linear relationships. Furthermore, the relationships may be learned or improved through evaluation of multiple patients.

[0142] After performing the bone balance adjustment algorithm 760, adjustments to the bone resection and implant parameters may be necessary. For example, the bone balance adjustment algorithm 760 may recommend additional bone resection and / or miniaturization of the tibial component of the implant. In some embodiments, the bone balance adjustment algorithm may be configured to determine one or more bone resection parameters and / or one or more implant parameters of a joint based on the amount of soft tissue laxity resulting from undergoing a medial reduction osteotomy. In other embodiments, the bone balance adjustment algorithm may be configured to determine one or more bone resection parameters and / or one or more implant parameters relating to a joint based on the amount of soft tissue laxity resulting from both the removal of identified osteophytes (detected by conventional methods) and the subsequent medial reduction osteotomy.

[0143] As shown in Figures 18A–18D, the bone balance adjustment algorithm 760 can provide visual guidance to the surgeon through a visual display. As shown in Figure 18A, a trial or component 2001 in the tibia is measured relative to the tibial plateau 2004. If the surgeon wishes to perform a medial reduction osteotomy, the tibial component is first made lateral and then reduced as shown in Figure 18B. Next, as shown in Figure 18C, the method can provide guidelines 2002 indicating the required length and angle of the bone to be cut to achieve the desired shape of the bone. Guidelines 2002 can be virtually determined using parameters of a smaller-sized implant projected onto a CT-based image of the original pre-excised tibial plateau. The result following guidelines 2002 is shown in Figure 18D, which represents the postoperative bone shape. In some embodiments, the bone balance adjustment algorithm 760 can instruct a robotic device to perform osteotomy on behalf of the surgeon. The timing of osteotomy can be determined during surgery, after osteophyte removal, or as part of the preoperative planning process. In some embodiments, the osteotomy is performed “pre-excision,” that is, before any bone cutting is performed.

[0144] The bone balance adjustment algorithm 760 can then determine the amount of bone that needs to be resected to adjust for the loosening caused by the removal of the posteromedial bone flare (or the removal of other bone in the surgical plan). This step can be performed after or before the removal of the medial osteophyte. In some embodiments, the osteotomy is performed before other bone cuts so as not to interfere with the digital tensor function. As recognized, the degree of bone resection, for example, the amount of tibia removed during a medial reduction osteotomy, directly affects the degree of correction of coronal plane alignment. There is a linear correlation between the degree of bone resection and the degree of coronal plane correction achieved. In particular, for every approximately 1 mm to 2 mm of bone resected, there is a correction of approximately 1° in coronal plane alignment. For example, performing a medial reduction osteotomy for varus deformity may result in a correction of approximately 1° in coronal plane alignment after 1 mm of tibia resection. In some cases, the determined coronal plane correction resulting from planned tibia removal (i.e., medial reduction osteotomy) may be displayed preoperatively or intraoperatively and adjusted based on the results of real-time osteotomy (e.g., osteotomy detected by one or more imaging systems or operator input), and the adjustment of the surgical plan may be calculated via one or more algorithms based on the real-time osteotomy results and displayed to the operator.

[0145] It should be understood that in a particular embodiment, one or more steps from the various methods described herein may be combined. Furthermore, in a particular embodiment, less than a portion of the steps of the methods described herein may be performed, and / or additional steps not described herein may be performed. Furthermore, the steps described herein do not necessarily have to be performed in the order presented. Furthermore, it should be noted that although surgical steps are presented as separate processes, they can be seamlessly incorporated into other steps detailed in this disclosure. For example, adjustment of the preoperative plan or treatment plan to take into account identified bony prominences, such as posteromedial bone flares, may be performed concurrently with method 1200 for adjusting the preoperative plan or treatment plan based on detected osteophytes. [Examples]

[0146] Some or all of the above systems and methods are implemented to collect results for osteophyte resection and ligament balancing. In the following descriptive example, 310 CAT scan-based robotic total knee arthroplasty (TKA) procedures were performed and results collected over one year. Preoperative CT scans were analyzed for the location and dimensions of unreachable posterior osteophytes. Of the 310 patients undergoing TKA, 74 patients (24%) were found to have posterior femoral osteophytes. Of these 74 patients, 70 knees received cementless knee implants to secure the implants (e.g., cementless (95%)).

[0147] Of the 74 patients with osteophytes, 95% (e.g., 70 out of 74) were associated with varus deformity. Of the 70 knees, all had medial posterior osteophytes, and 32 had smaller lateral posterior osteophytes. In approximately 5% of the 74 patients, osteophytes were detected in the valgus knee, including even more lateral posterior osteophytes, and half had smaller medial posterior osteophytes.

[0148] The size of each osteophyte was determined. To determine the size of the osteophytes, the cross-sectional area of ​​the posterior femoral osteophytes in the sagittal plane was measured using a technique consistent with this disclosure. The medial posterior femoral osteophytes had an average size of 111 mm², with a size range of 32 mm² to 321 mm², of which 50% were classified as small (less than 100 mm²), 39% as medium (100 to 200 mm²), and 10% as extremely large (greater than 200 mm²). In this exemplary dataset, the average size of the included lateral posterior femoral osteophytes was 123 mm², of which 46% were classified as small, 40% as medium, and 14% as extremely large.

[0149] After removing accessible osteophytes, the soft tissue pose was captured, and bone balancing was performed according to the techniques described throughout this disclosure. Next, the sagittal plane alignment was evaluated, with further adjustments to the distal femoral resection (distal femoral resection of + / - 1 mm per 6° of deformity) based on the presence of flexion contracture or hyperextension. Subsequently, final adjustments to the bone balancing were made based on the size and location of the posterior femoral osteophytes. In this embodiment, since the laxity resulting from osteophyte removal may affect both extension and flexion, the laxity was adjusted based on the correction of the osteophytes, and the tibial resection was modified based on the size and shape of the posterior osteophytes.

[0150] When asymmetrical medial or lateral posterior osteophytes were detected, 60% (44 / 74) of the knees underwent a standalone angle adjustment for tibial resection, of which 57% (42 / 74) were varus and 3% (2 / 74) were valgus. The mean change in alignment was 0.5° (range 0.2–2.0°). When symmetrical medial and lateral posterior osteophytes were detected, 18% (13 / 74) of the knees underwent a standalone adjustment of the tibial resection level (proximal reduction), resulting in a mean reduction of tibial resection volume of 0.85 mm (range 0.5 mm–2 mm). When asymmetrical medial and lateral posterior osteophytes were detected, 20% (15 / 74) of the knees underwent a combined correction of angle and resection level for the tibia, of which 15% (11 / 74) were varus and 5% (4 / 74) were valgus (see Figure 17).

[0151] In all embodiments in which osteophytes were detected and corrected using a bone balancing algorithm (which may be any of the algorithms discussed herein), less bone was removed from the tibia in 100% of cases (74 / 74), and angular correction to fine-tune alignment toward the machine axis was achieved in 82% (61 / 74) of knees. In some embodiments, 99% of knees (73 / 74) used 9mm or 10mm liners (66 patients accommodated 9mm, and 7 patients accommodated 10mm), and one patient used an 11mm liner.

[0152] Embodiments disclosed herein may result in one or more algorithms for determining one or more osteotomy parameters and / or implant parameters and predicting the effects of soft tissue (and / or gap or balance effects) after the removal of one or more osteophytes and / or release of posterior ligaments, enabling surgeons to automate preoperative and / or intraoperative decision-making. These algorithms may help streamline the procedure workflow to anticipate the effect of removing one or more osteophytes (e.g., posterior osteophytes) on soft tissue balancing and to actively adjust the osteotomy accordingly.

[0153] Embodiments disclosed herein may provide algorithms for calculating one or more osteosurgery parameters and / or one or more implant parameters based on the cross-sectional area or surface area of ​​an osteophyte. The relationship may include adjusting a predetermined amount of parameters for a predetermined amount of cross-sectional area. For example, a linear relationship may calculate the osteosurgery parameters and / or implant parameters based on the cross-sectional area of ​​an osteophyte (90 mm²). 2 0.5° and / or 0.5 mm per spur, or 20 mm² of bone spur cross-sectional area. 2 This may include, but is not limited to, adjustments of 0.1° and / or 0.1 mm for each. For example, a predetermined amount in the parameter may be in the range of 0.4° to 0.6° and / or 0.4 mm to 0.6 mm, and a predetermined amount of the cross-sectional area may be 80 mm 2 or 85mm 2 From 100mm 2 It may be within the range of, for example, 0.075° to 0.125° and / or 0.075 mm to 0.125 mm, and the predetermined amount of the cross-sectional area may be 15 mm 2 ~25mm 2 These may be within the ranges mentioned above. A linear relationship may correspond to y=mx+b or y=mx, where "m" may be 0.0055 or 0.0056, or within the range of 0.005 to 0.006 or 0.004 to 0.006 (for example, in relation to the knee joint). For other joints, "m" and / or the linear relationship may differ based on the learned relationship.

[0154] The embodiments disclosed herein may be adapted to the practitioner's preferences and / or methods. For example, some practitioners prefer to create a rectangular gap and / or osteotomy, while others prefer to create a trapezoidal gap and / or osteotomy, for example, by making the outer sides looser and / or using a greater bone inclination on the outer sides. The embodiments disclosed herein may be used to adjust an initial treatment plan generated according to the practitioner's preferences, and the adjustment itself may be fine-tuned based on the practitioner's preferences.

[0155] Embodiments disclosed herein can assist surgeons in planning osteotomies in patients undergoing joint replacement surgery to correct varus or valgus deformities, as removal of posterior osteophytes can significantly affect laxity and / or surgical outcomes. Embodiments disclosed herein allow the degree of deformity to be taken into consideration when determining modifications to the osteotomy parameters and / or implant thickness in order to consider the removal of osteophytes.

[0156] Embodiments disclosed herein may provide algorithms that predict the increase in extensible and flexible space (or alternatively, extensible space) after the removal of one or more posterior osteophytes, based on the cross-sectional area of ​​one or more posterior osteophytes. Embodiments disclosed herein may provide recommendations based on the correlation between the size and / or location of osteophytes and the influence of soft tissue.

[0157] Embodiments disclosed herein may simplify the prediction of soft tissue effects caused by osteophyte removal by considering the cross-sectional area at the widest part of the osteophyte (e.g., using a slice of a CT scan) and by not requiring more dimensions and / or calculations (e.g., the volume of the osteophyte). Embodiments disclosed herein may not require consideration of the width and / or total volume of the osteophyte.

[0158] Embodiments disclosed herein can provide recommendations regardless of whether the deformation is fixed or / or flexible, and may not require input on whether the deformation is fixed or flexible. Embodiments disclosed herein make predictions regarding the impact on soft tissue laxity, independent of the rigidity of the deformation. Embodiments disclosed herein may provide a linear correlation between the size of the removed osteophyte (e.g., surface area and / or cross-sectional area) and the resulting soft tissue laxity. Furthermore, and / or, embodiments disclosed herein may provide a nonlinear correlation between the size of the removed osteophyte and the resulting soft tissue laxity.

[0159] Embodiments disclosed herein may provide recommendations regardless of patient-reported outcomes and may not require input of patient-reported outcomes. Embodiments disclosed herein may correlate outcomes with osteophyte size based on the correlation between osteophyte volume and disease severity or progression. Embodiments disclosed herein may predict better outcomes based on the removal of larger osteophytes.

[0160] Embodiments disclosed herein can identify the direction and magnitude of deformation, as well as the expected results of osteophyte removal. Embodiments disclosed herein can provide predictive algorithms for identifying the direction and magnitude of deformation, and / or provide detailed information, in light of the removal of posterior osteophytes and the subsequent increase in extensor and / or flexion gaps. Embodiments disclosed herein can provide algorithms configured to characterize the extent of deformation in both direction and magnitude.

[0161] The embodiments disclosed herein may be used to detect or collect preoperative, intraoperative, and / or postoperative information relating to patients and / or procedures. The embodiments disclosed herein intend to be implants or artificial joints and are not limited to the context described herein. For example, implants disclosed herein may be implemented as another implant system for another joint or other part of the musculoskeletal system (e.g., hip, knee, spine, bone, ankle, wrist, finger, hand, toe, or elbow) and / or as sensors configured to be directly implanted in the patient's tissues, bones, muscles, ligaments, etc. Each implant or implant system may include sensors such as inertial measuring units, strain gauges, accelerometers, ultrasonic or acoustic sensors configured to measure position, velocity, acceleration, direction, range of motion, and / or sensors configured to measure changes in synovial fluid, blood glucose levels, body temperature, and other biological information (such as changes in color, pH), and / or electrodes that detect electrical information, ultrasonic or infrared sensors that detect other nearby structures, etc., in order to detect infection, invasion, nearby tumors, etc. For example, an implant may be a sensor or other measuring device configured to be implanted in the patient's body by drilling into bone, another implant, or otherwise.

[0162] The embodiments and systems disclosed herein may perform determinations based on images or imaging data (e.g., from CT scans, ultrasound). The embodiments disclosed herein may predict soft tissue relaxation, resulting gaps, bone resection and / or implant parameters, etc., based on a single slice or view of a CT scan, and may not require calculation of bone spur volume, etc. The images disclosed herein may display or represent bone, tissue, or other anatomical structures, and the systems and embodiments disclosed herein may recognize, identify, classify, and / or determine parts of the anatomical structure of bone, cartilage, tissue, and bone landmarks (e.g., each specific vertebra within the spine). The embodiments and systems disclosed herein may determine the relative position, orientation, and / or angle between recognized bones, such as the Cobb angle, the angle between the tibia and femur, and / or other alignment data.

[0163] The embodiments and systems disclosed herein provide a display having a graphical user interface configured to graphically display data, decisions, and / or steps, goals, instructions, or other parameters of a procedure, including preoperative, intraoperative, and / or postoperative procedures. Figures, diagrams, animations, and / or videos displayed through the user interface may be recorded and stored in a memory system.

[0164] The embodiments and systems disclosed herein can be implemented using machine learning techniques. One or more algorithms may be configured to learn or train on patterns and / or other relationships across multiple patients in combination with preoperative information and outputs, intraoperative information and outputs, and postoperative information and outputs. The learned patterns and / or relationships may improve the decisions made by one or more algorithms, and / or the methods of running, configuring, designing, or compiling one or more algorithms. Improvements and / or updates to one or more algorithms may further improve the display and / or graphical user interface (e.g., display of bone recognition and / or decisions, targets, recognition and / or other conditions and / or bone offset).

[0165] Embodiments disclosed herein may be configured to optimize the “fit” or “tightness” of an implant provided to a patient during a medical procedure, based on detection by one or more algorithms. Implant fit can be made tighter by determining a specific type of material or type of implant or prosthesis (e.g., stabilization implant, VVC implant, ADM implant, or MDM implant) by increasing the thickness or other dimensions of the implant, by aligning the implant with shallower bone inclination and / or determining shallower resulting or desired bone inclination. Implant thickness can be obtained by increasing (or decreasing) the size or shape of the implant. Tightness may be affected by gaps and / or the width of the joint cavity, which can be adjusted by inserts, which may vary depending on the type of implant or movement. Gaps may be created by cutting the femur and tibia. Tightness may be further affected by inclination. The range of inclination may be based on the choice of implant, as well as the surgical approach and the patient’s anatomical structure. Implant thickness can also be achieved by adding or removing augments or shims. For example, augments or shims may be stackable and removable, and their thickness may be increased by adding one or more augments or shims, or by adding augments or shims having a predetermined thickness (e.g., exceeding a certain threshold). Fit or tightening can also be achieved by certain types of bone cutting, bone preparation, or tissue cutting that reduce the number and / or invasiveness of incisions during surgery.

[0166] The embodiments disclosed herein may be implemented during robotic medical procedures using robotic devices. The embodiments disclosed herein are not limited to the specific scores, thresholds, etc. described herein. For example, the outputs and / or scores disclosed herein may include hip joint disorder and osteoarthritis scores or other types of scores such as HOOS, KOOS, SF-12, SF-36, Harris hip score, etc.

[0167] The embodiments disclosed herein are not limited to specific types of surgery and may be applied in the context of osteotomy, computer-navigated surgery, neurosurgery, spinal surgery, otolaryngology surgery, orthopedic surgery, general surgery, urological surgery, ophthalmic surgery, obstetric and gynecological surgery, plastic surgery, valve replacement surgery, endoscopic surgery, and / or laparoscopic surgery.

[0168] The embodiments disclosed herein may improve or optimize surgical outcomes, implant design, and / or preoperative analysis, prediction, or workflow. The embodiments disclosed herein may extend a series of treatments to optimize the patient's postoperative outcome. The embodiments disclosed herein may recognize or determine previously unknown relationships to facilitate the optimization of care, the prediction of soft tissue laxity, and / or the optimization of the design of artificial joints or implants.

Claims

1. A method for evaluating joints, Identifying the first osteophyte in the image of the joint, wherein the first osteophyte is located beneath the soft tissue, To identify the cross-sectional area of ​​the first bone spur, The method involves executing an algorithm that determines one or more adjustment parameters based on the identified cross-sectional area of ​​the first bone spur, wherein the algorithm applies an equation that receives the identified cross-sectional area as input, outputs one or more adjustment parameters, and the one or more adjustment parameters are Predicted changes in soft tissue relaxation after the removal of the first identified osteophyte, Adjustment of the depth of the planned osteotomy for one or more of the aforementioned bone cuts, Adjustment of the planned angle of bone resection for one or more bone cuts, and / or The actions described above include, and, adjusting the implant to its planned thickness. A method comprising outputting one or more determined adjustment parameters to a display.

2. The method according to claim 1, wherein identifying the first osteophyte and / or identifying the cross-sectional area of ​​the first osteophyte includes analyzing the image using one or more image processing techniques.

3. The method according to claim 1, wherein determining the cross-sectional area of ​​the first osteophyte includes analyzing the first dimension and the second dimension of the first osteophyte, and ignoring the third dimension of the first osteophyte.

4. The method according to claim 1, wherein the one or more adjustment parameters include adjustments to the planned depth of the bone resection of the one or more bone cuts, and adjustments to the planned angle of the bone resection of the one or more bone cuts.

5. The method according to claim 1, wherein determining the cross-sectional area of ​​the first osteophyte involves determining that the image of the joint is an image in a set of images showing the maximum extent of the first osteophyte in a first dimension.

6. The aforementioned equation is a linear equation, and the aforementioned linear equation is, A linear relationship between the specified cross-sectional area and the adjustment to the planned depth of bone resection, wherein the larger the specified cross-sectional area, the greater the reduction in the planned depth of bone resection, and / or The method according to claim 1, comprising a linear relationship between the specified cross-sectional area and the planned thickness of the implant, wherein the larger the specified cross-sectional area, the greater the increase in the planned thickness of the implant.

7. To identify the second osteophyte in the image of the joint located beneath the soft tissue, To identify the cross-sectional area of ​​the second bone spur, Identifying the location of the first bone spur, and Further including identifying the location of the second bone spur, The method according to claim 1, wherein the algorithm is performed to determine the one or more adjustment parameters, further based on the identified cross-sectional area of ​​the second osteophyte, the identified location of the first osteophyte, and the identified location of the second osteophyte.

8. Based on the identified location of the first bone spur and the identified location of the second bone spur, it is determined that the first bone spur is located on the first side of the joint and the second bone spur is located on the second side of the joint opposite to the first side, and The method of claim 7, comprising determining the difference between the specified cross-sectional area of ​​the first osteophyte and the specified cross-sectional area of ​​the second osteophyte, wherein the equation includes a linear relationship between the determined difference in the specified cross-sectional areas and the adjustment to the planned angle of osteotomy, and further comprising determining the angle such that the larger the determined difference in the specified cross-sectional areas, the larger the adjustment to the planned angle of osteotomy.

9. The method further includes determining whether the identified difference in cross-sectional area is greater than or equal to a predetermined difference threshold, If the determined difference of the identified cross-sectional area is less than or equal to the predetermined difference threshold, then it is determined that the first bone spur and the second bone spur are symmetrical, and The method according to claim 8, wherein if the determined difference of the identified cross-sectional areas is greater than the predetermined difference threshold, it is determined that the first bone spur and the second bone spur are asymmetrical.

10. If it is determined that the first bone spur and the second bone spur are symmetrical, then the algorithm is executed. The depth of the planned bone resection should be reduced by a predetermined amount for each specified amount of the first bone spur and / or the second bone spur, or The planned thickness of the implant includes determining that the planned thickness of the first osteophyte and / or the second osteophyte should increase by a predetermined amount of depth for each predetermined amount of the specified cross-sectional area, If it is determined that the first bone spur and the second bone spur are asymmetrical, the algorithm is executed. Based on the specified cross-sectional area of ​​the first bone spur and the specified cross-sectional area of ​​the second bone spur, it is determined whether the first bone spur is larger than the second bone spur. If it is determined that the first osteophyte is larger than the second osteophyte, it is determined that the planned angle of osteotomy should be adjusted in the first direction or orientation by a predetermined amount of the angle of osteotomy for each predetermined difference between the specified cross-sectional area of ​​the first osteophyte and the specified cross-sectional area of ​​the second osteophyte. The method according to claim 9, comprising determining that the second bone spur is larger than the first bone spur if it is determined that the first bone spur is not as large as the second bone spur, and determining that the planned angle of bone resection should be adjusted by a predetermined amount of bone resection angle for each predetermined difference in the second direction or orientation opposite to the first direction or orientation.

11. To determine that the first bone spur and the second bone spur are asymmetrical, It is determined that both the specified cross-sectional area of ​​the first bone spur and the specified cross-sectional area of ​​the second bone spur are larger than a predetermined cross-sectional area. Determining that the difference in the specified cross-sectional area is greater than a predetermined difference, The depth of the planned bone resection should be reduced by a predetermined amount for each predetermined amount of the specified cross-sectional area of ​​the first bone spur and / or the second bone spur, or The method of claim 10, further comprising determining that the planned thickness of the implant should increase by a predetermined amount of depth for each predetermined amount of the specified cross-sectional area of ​​the first osteophyte and / or the second osteophyte.

12. The aforementioned joint is the knee joint, The first bone spur is a lateral bone spur, and the second bone spur is a medial bone spur. The aforementioned one or more bone cuts include a tibial cut or a femoral cut. The first direction is varus of the tibia or varus of the femur, The method according to claim 10, wherein the second direction is eversion of the accessory navicular bone or eversion of the femur.

13. The predetermined depth is in the range of 0.4 mm to 0.6 mm. The specified cross-sectional area of ​​the first bone spur and / or the second bone spur is 85 mm². ２ ~100mm ２ It is within the range, The angle of the predetermined amount of bone resection is in the range of 0.4° to 0.6°. The difference of the predetermined amount between the specified cross-sectional area of ​​the first bone spur and the specified cross-sectional area of ​​the second bone spur is 80 mm ２～ 100 mm ２ The method according to claim 12, which is within the range of claim 12.

14. The predetermined depth is in the range of 0.05 mm to 1.5 mm. The specified cross-sectional area of ​​the first bone spur and / or the second bone spur is 15 mm². ２ ~25mm ２ It is within the range, The angle of the predetermined amount of bone resection is in the range of 0.05° to 1.5°. The difference of the predetermined amount between the specified cross-sectional area of ​​the first bone spur and the specified cross-sectional area of ​​the second bone spur is 15 mm ２ ~25mm ２ The method according to claim 12, which is within the range of claim 12.

15. The method according to claim 1, wherein the equation includes a nonlinear relationship.

16. A method for evaluating joints, Receiving an image of a joint including an osteophyte, wherein the image shows a view of the joint in a first and second dimension, where soft tissue extends above the osteophyte. Receiving a treatment plan that includes a plan to remove the bone spur after one or more bone cuts have been performed, wherein the one or more bone cuts are configured to place an implant during the treatment, To identify the cross-sectional area of ​​the bone spur in the first dimension and the second dimension, The execution of an algorithm that determines one or more adjustment parameters, wherein the algorithm applies an equation that takes the identified cross-sectional area of ​​at least one of the bone spurs as input, outputs one or more adjustment parameters, the one or more adjustment parameters including adjustments to one or more osteoctomy parameters and / or adjustments to implant parameters of the received treatment plan, and A method comprising outputting one or more determined adjustment parameters to a display.

17. The method according to claim 16, wherein the one or more bone resection parameters include the planned depth of the one or more bone resections and / or the planned angle of the one or more bone resections.

18. A system configured to evaluate joints, An image acquisition device configured to acquire an image of at least one of the aforementioned joints, A memory configured to store information, wherein the information includes imaging data relating to at least one acquired image, and the imaging data includes the cross-sectional area and position of at least one identified portion of the bone of the joint, It is a controller, The controller is configured to execute an algorithm that determines one or more adjustment parameters based on at least one acquired image and / or stored imaging data, the algorithm applying an equation to receive the cross-sectional area of ​​at least one identified portion of the bone as input, and outputting one or more adjustment parameters, the one or more adjustment parameters including adjustments for bone resection parameters and / or implant parameters, and A system comprising a display configured to display one or more of the determined adjustment parameters.

19. The image acquisition device is a computed tomography (CT) acquisition device, and the at least one acquired image is a CT scan. The system according to claim 18, wherein the CT scan provides views of the joint in a first and second dimension in which the soft tissue is stretched, and the cross-sectional area is determined using the dimensions of the identified portion of the bone in the first and second dimensions.

20. The system according to claim 18, wherein the one or more adjustment parameters include coronal plane alignment, and the bone resection parameter is used to determine the coronal plane alignment.

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