Operating methods and surgical systems of surgical robots

Abstract

To provide computer-assisted and robotically-assisted surgical systems for joint reconstruction procedures such as total knee arthroplasty (TKA).SOLUTION: A surgical system is provided, comprising: a robotic device; a surgical tool mounted on the robotic device; and a processing circuit. The processing circuit is configured to: receive image data of an anatomy; generate virtual bone models 550, 552 based on the image data; identify a soft tissue attachment point on the virtual bone models 550, 552; plan placement of an implant based on the soft tissue attachment point; generate a control object based on the placement of the implant; and control the robotic device to confine the surgical tool within the control object.SELECTED DRAWING: Figure 6

Description

Technical Field

[0003]

[0001] Cross - reference to Related Applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 785,42 7, filed on December 27, 2018. The entire disclosure of the provisional patent application is incorporated herein by reference into this specification.

Background Art

[0002] The present disclosure generally relates to the field of computer - assisted and robot - assisted surgical systems and methods, and more particularly to the field of computer - assisted and robot - assisted surgical systems for joint reconstruction surgery, such as total knee arthroplasty (T KA). TKA is widely used for the treatment of osteoarthritis and other knee joint injuries of patients by replacing a part of the knee's anatomical structure with prosthetic components. In total knee arthroplasty, the patient's femur and tibia are typically modified to attach to the prosthesis using a series of planar cuts to prepare the bone surface. Then the prosthetic implant is attached to the bone, forming an artificial joint . Next, the prosthetic implant is attached to the bone, forming an artificial joint.

[0003] Computer - assisted surgical systems facilitate the planning and execution of TKA surgery by assisting in bone modification and implant alignment to the bone. However, conventional computer - assisted surgical systems often do not address issues related to soft tissues, such as ligaments like the posterior cruciate ligament (PCL) and the anterior cruciate ligament (ACL), and do not consider soft tissues when planning the surgery. This can result in iatrogenic injury to soft tissues, weakening of the attachment points between bone and soft tissues , ligament impingement by implant components, and other complications. ​​​[Overview of the project]

[0004] One implementation of this disclosure is a surgical system. This surgical system includes a robotic device, The robotic device is equipped with surgical tools and processing circuits. The processing circuits are biological It receives structural image data, generates a virtual bone model based on the said image data, and the virtual bone Identify the soft tissue attachment points of the model and plan the placement of the implants based on these soft tissue attachment points. Based on the placement of the implant, a control target is generated, and the robotic device is controlled, and the hand The technical tool is configured to be limited to the controlled object.

[0005] In some embodiments, the soft tissue attachment point is such that the posterior cruciate ligament or anterior cruciate ligament is attached to the femur. This corresponds to the part that attaches to the tibia. In some embodiments, the processing circuit is the soft tissue Based on the attachment point, the axis of the implant is aligned with the medial edge of the soft tissue attachment point. The system is configured to plan the placement of the implant.

[0006] In some embodiments, the processing circuit further includes a graphical user interface. The graphical user interface is configured to generate the virtual This includes visualization of bone models, implants, and soft tissue attachment points. In some embodiments, The graphical user interface includes a visualization of the controlled object. In the application configuration, the processing circuit is configured to limit the inclusion of soft tissue attachment points in the controlled object. It is composed.

[0007] Another implementation of this disclosure is a method. The method receives image data of a biological structure. The process involves generating a virtual bone model based on the image data, and attaching the virtual bone model to soft tissue. Identifying the point, and determining the size and placement of the implant based on the soft tissue attachment point. to generate a control target based on the size and placement of the implant, the control target This includes restricting or controlling surgical tools mounted on a robotic device based on [the specified criteria].

[0008] In some embodiments, the image data includes computed tomography images, in which case, The method also divides the computed tomography image and identifies one or more bones within the image. This includes, in some embodiments, the soft tissue attachment point is the posterior cruciate ligament or the anterior cruciate ligament This corresponds to the point where it attaches to the femur or tibia. This soft tissue attachment point is where the patellar ligament attaches to the tibia. This may correspond to the site. In some embodiments, the placement of the implant is determined by the This includes aligning the axis of the plant with the inner edge of the soft tissue attachment point.

[0009] In some embodiments, the method also includes the virtual bone model, implants, and soft tissue. This includes generating a graphical user interface that visualizes the attachment points. A graphical user interface can further visualize the controlled object. In one embodiment, the method predicts the line of action of the ligament based on the soft tissue attachment point, The virtual bone model is enhanced with a virtual implant model of the implant, and the function of the ligament. Identifying whether the line intersects the virtual implant model, and the function of the ligament. The user is alerted when it is determined that a line intersects with the virtual implant model. This includes, the method includes, limiting the inclusion of soft tissue attachment sites in the controlled object. obtain.

[0010] Another implementation of the present disclosure, when executed by one or more processors, is a non-transitory computer-readable medium storing program instructions that cause the one or more processors to perform operations. The operations include receiving image data of a biological structure, generating a virtual bone model based on the image data, identifying soft tissue attachment points of the virtual bone model, specifying the size and placement of an implant based on the soft tissue attachment points, generating a control object based on the size and placement of the implant, and constraining or controlling a surgical tool mounted on a robotic device based on the control object. In some embodiments, the operations include limiting the inclusion of the soft tissue attachment points with respect to the control object. In some embodiments, the operations include predicting the line of action of a ligament based on the soft tissue attachment points, enhancing the virtual bone model with a virtual implant model of the implant, determining whether the line of action of the ligament intersects the virtual implant model, and alerting a user in response to determining that the line of action of the ligament intersects the virtual implant model. In some embodiments, specifying the placement of the implant includes aligning the axis of the implant with the inner edge of the soft tissue attachment points.

[0011] In some embodiments, the operations include limiting the inclusion of the soft tissue attachment points with respect to the control object. In some embodiments, the operations include predicting the line of action of a ligament based on the soft tissue attachment points, enhancing the virtual bone model with a virtual implant model of the implant, determining whether the line of action of the ligament intersects the virtual implant model, and alerting a user in response to determining that the line of action of the ligament intersects the virtual implant model. In some embodiments, specifying the placement of the implant includes aligning the axis of the implant with the inner edge of the soft tissue attachment points. In some embodiments, specifying the placement of the implant includes aligning the axis of the implant with the inner edge of the soft tissue attachment points.

[0012] This summary is merely illustrative and is not intended to limit in any way. Other aspects, features, and advantages of the devices or processes described in this specification will become apparent in the embodiments of the invention described in conjunction with the accompanying drawings. In the drawings, like reference numerals will be used to refer to like parts. ​​​​​​​​​​The reference number refers to a similar element. [Brief explanation of the drawing]

[0013] [Figure 1] This is a diagram of a robot-assisted surgical system according to an exemplary embodiment. [Figure 2] This is a block diagram of the processing circuit of the surgical system shown in Figure 1, according to an exemplary embodiment. [Figure 3] This is a flowchart of a process for facilitating arthroscopic surgery of a joint, according to an exemplary embodiment. [Figure 4] This is a flowchart illustrating an exemplary embodiment of the process for preventing collisions during knee arthroscopic surgery. [Figure 5] This figure illustrates the use of bone imaging to plan the placement of implants to avoid collisions during knee arthroscopy, according to an exemplary embodiment. [Figure 6] This is a first diagram of the graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Figure 7] A second diagram of the graphical user interface generated by the processing circuit in Figure 2, according to an exemplary embodiment. [Figure 8A] This is a third diagram of the graphical user interface generated by the processing circuit in Figure 2, according to an exemplary embodiment. [Figure 8B] This is a fourth diagram of the graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Figure 8C] This is a fifth diagram of the graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Figure 8D] A sixth figure shows the graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Figure 8E] Figure seven shows a graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Figure 9] Figure eighth shows a graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Figure 10] This is a flowchart of the process for determining the rotational alignment of an implant component, according to an exemplary embodiment. [Figure 11] This is a diagram of a portion of the process shown in Figure 10, according to an exemplary embodiment. [Figure 12] This is a diagram of a portion of the process shown in Figure 10, according to an exemplary embodiment. [Figure 13] This is a fifth diagram of the graphical user interface generated by the processing circuit of Figure 2, according to an exemplary embodiment. [Modes for carrying out the invention]

[0014] Referring now to Figure 1, an exemplary embodiment shows a surgical system for orthopedic surgery. The 100 is shown. Generally, the surgical system 100 facilitates the planning and execution of surgical procedures. It is configured to facilitate the procedure. The surgical system 100 is designed to take into account the patient's anatomical structure, for example, Figure 1. As shown, the legs 102 of the patient 104, who is sitting or lying on the table 105 It is configured to treat. The leg 102 includes the femur 106 and tibia 108, and the entire knee joint. In arthroscopic surgery, an artificial knee implant is placed during the procedure. Surgical system 100 is... Furthermore, or alternatively, partial knee arthroscopy, or full and / or partial hip arthroscopy. , other joint surgeries, spinal surgeries, and any other surgical procedures (e.g., neurosurgery, orthopedic surgery, It can be configured to facilitate the planning and execution of urological, gynecological, dental, ENT, and oncological procedures. To facilitate the surgery, the surgical system 100 includes a robotic device 120 and a tracking system. Includes 122 and computer system 124.

[0015] The robotic device 120, under the control of the computer system 124, analyzes the patient's anatomical structure. For example, it is configured to correct the femur (106) of patient 104. Robotic device 120 One embodiment of this is a haptic device. "Haptic" refers to the sense of touch, and The field of articulation, in particular, involves human feedback to operators. Regarding interactive devices. Feedback may include tactile sensations, such as vibration. The feedback also includes giving the user force, such as positive force or resistance to motion. This may include providing resistance. One use of haptics is to provide the user of the device This involves providing guidance or restrictions on the operation of the device. For example, a haptic device. The device is combined with a surgical tool that the surgeon can manipulate to perform surgical procedures. There is. The operation of the surgical tool by the surgeon is performed in the course of the operation of the surgical tool and the surgeon It may also be limited to the use of haptics that provide feedback. .

[0016] Another embodiment of the robotic device 120 is an autonomous robot or a semi-autonomous robot. "Autonomy" means gathering information about the situation, identifying a set of actions, and By performing a series of actions automatically, it can be done independently or semi-independently of human control. This refers to the capabilities of a moving robotic device. For example, in such an embodiment, the robotic device 120 , communicate with the tracking system 122 and the computer system 124 to determine the patient's bone or soft tissue A series of cuts in the weave can be completed autonomously without direct human intervention. In various embodiments According to this, the robotic device 120 can perform unlimited surgeon-controlled movements, as well as tactilely constrained movements. It can perform various combinations of actions and / or automatic or autonomous robotic movements.

[0017] The robotic device 120 includes a base 130, a robotic arm 132, and a surgical tool 134. It is equipped with and is communicatively connected to the computer system 124 and the tracking system 122. The base 130 provides a movable base for the robot arm 132, and the robot arm 1 32 and surgical tools 134 are redistributed to patient 104 and table 105 as needed. The base 130 is also used to position the robotic arm 132 and surgical tools shown below. Includes power systems, calculators, motors, and other electronic or mechanical systems required for 134 functions. It is visible.

[0018] The robotic arm 132 supports the surgical tool 134, and the computer system The system is configured to return feedback as instructed by Tem 124. In some embodiments, The robotic arm 132 allows the user to operate the surgical tool 134, and the user It returns force feedback to the joint 13. The system includes 6 and mount 138, which allow the user to connect the robot arm 132 and surgical tools. The rod 134 is configured to be able to move and rotate freely through acceptable positions. It is equipped with a motor, actuator, or other mechanism, and at the same time, a computer system As instructed by TEM 124, some of the movements of the robotic arm 132 and surgical tool 134 It returns feedback of forces that constrain or prevent it. As will be explained in detail below, the robot The toe arm 132 thereby provides force feedback along the boundary of the controlled object (for example, While returning vibrations (forces that prevent or resist the advance into the boundary), the surgeon, To enable complete control of the surgical tool 134 within the elephant. Several embodiments So, the robot arm 132 is positioned as needed and / or to complete a specific surgical task, such as amputation of femur 106, surgical tools The computer system 124 directs the operation of code 134 without direct user intervention. It is configured to automatically move to a new posture.

[0019] In an alternative embodiment, the robot device 120 is a handheld robot device or other It is a type of robot. In a handheld robot device, the handheld robot device Some parts (e.g., end effectors, surgical tools) are part of the handheld robotic device. The robot can be controlled / operated in relation to the body. The user can use the handheld robot as needed. The robotic part holds, supports, and operates the surgical device, while simultaneously the surgical hand of the surgeon. It is controlled to facilitate the execution of the surgery. For example, the handheld robotic device is used in surgery. Retract the cutting tool to prevent the user from operating the surgical cutting tool in an unsafe area. It may be possible to control it to prevent it. The systems and methods described herein are of various types. Please understand that this can be done with various robotic devices, including their design and configuration.

[0020] In the embodiment shown, the surgical tool 134 cuts, grinds, drills, and parts of bone. It is configured to be excised, reshaped, and / or otherwise modified. For example, surgical two R134 made a series of cuts in the femur 106 to allow the implant to be placed in the femur The surgical tool 134 may be configured to adjust 106 and / or tibia 108. One of several tools that can be interchangeably connected to the robotic device 120 is a suitable tool of the choice. Any one is acceptable. For example, as shown in Figure 1, the surgical tool 134 is a spherical bar. The tool 134 includes, for example, blades arranged parallel to or perpendicular to the axis of the tool. A sagittal saw may also be used. In other embodiments, the surgical tool 134 is one or more different other medical tools. Medical tasks (e.g., soft tissue modification, prosthetic implantation, image generation, data collection, retraction or It may be configured to perform the action of applying tension.

[0021] The tracking system 122 tracks the patient's anatomical structure (e.g., femur 106 and tibia 108). and robotic device 120 (i.e., surgical tool 134 and / or robotic arm 13 2) Tracking and enabling control of the surgical tool 134 coupled to the robotic arm 132 The location and direction of the movements performed by the surgical tool 134 on the patient's anatomical structure are identified. To that end, as well as the femur 106, tibia 108, surgical tool 134, and / or robot The net device 120 is made visible to the user on the display of the computer system 124. It is configured to do so. More specifically, the tracking system 122 is symmetric with respect to the coordinate system ( For example, the position and orientation (i.e., posture) of the surgical tool 134 and the femur 106) are determined. During surgical procedures, track the posture of the subject (i.e., continuously identify it). Various implementations Depending on the configuration, the tracking system 122 is any type of navigation system, that is, non-mechanical tracking systems (e.g., optical tracking systems), mechanical tracking systems (e.g., (tracking based on measurement of the relative angles of the joints 136 of the robot arm 132), or non-mechanical methods. This could also include any combination of mechanical tracking systems.

[0022] In the embodiment shown in Figure 1, the tracking system 122 comprises an optical tracking system. The tracking system 122 then connects to the first fiducial tree 14 attached to the tibia 108. 0, a second fiducial tree 141 connected to the femur 106, and a base 130. The combined third fiducial tree 142, one or more of which are joined to the surgical tool 134 Fiducial 144 and fiducial (i.e., fiducial tree) A detection device 146 is configured to detect the three-dimensional position of markers 140 to 142. It is equipped with. As shown in Figure 1, the detection device 146 has a pair of cameras 148 arranged in a stereoscopic configuration. It is equipped with. The fiducial trees 140-142 are clearly visible to camera 148. The image processing system uses data from camera 148, for example If so, the reflectance to infrared radiation (e.g., emitted from elements of the tracking system 122) A fiduciary is a marker configured to be easily detectable due to its high sensitivity. Includes. Due to the stereoscopic arrangement of the camera 148 of the detection device 146, each fiducial The location is determined in 3D space via triangulation. Each fiducial corresponds to the corresponding Because it has a geometric relationship with the elephant, tracking the fiducial makes it possible to track the object. (For example, by tracking the second fiducial tree 141, tracking system 12 (2 will be able to track the femur 106), the tracking system 122 will then... It is configured to perform a registration process to identify or verify any relationships. It is possible. The unique arrangement of fiducials in fiducial trees 140-142 (sand Well, the first fiducial tree 140 fiducial is the second fiducial (The fiducial of the char tree 141 is arranged in a different shape from the fiducial) This makes it possible to distinguish between the tracking trees and, consequently, the objects being tracked.

[0023] Figure 1 shows the tracking system 122, or several other systems for surgical navigation and tracking. Using this approach, in order to make cuts to the anatomical features of the patient, or the anatomy When using the surgical tool 134 to modify the characteristics of the target structure, the surgical system 100 , the characteristics of the anatomical structure, for example, the position of the surgical tool 134 relative to the femur 106. It is possible.

[0024] The computer system 124 plans the surgery based on medical images or other data. The surgical tool 134 receives data regarding the location of the patient's anatomical structures, and the surgical device The robotic device 120 is configured to be controlled according to the drawing. In particular, as described herein According to various embodiments, the computer system 124 determines the location and attachment points of the soft tissue. A patient-specific surgical plan is developed, and a patient-specific control group is used in accordance with the surgical plan. The computer system 124 is configured to control the bot device 120. The tracking system 122 and the robot device 120 are communicated together, and the robot device 12 To facilitate electronic communication between the tracking system 122 and the computer system 124. Furthermore, the computer system 124 may also be connected to a network, for example By accessing the electronic medical record system, the patient's medical history or other patient information can be obtained. Receive file information, medical images, surgical plans, and surgical procedure information, and perform the surgical procedure. It performs various functions related to the processing circuit 160 and input / Includes output device 162. In some embodiments, the first computer system 124 Computer equipment (for example, located in a surgeon's office and operated by a remote server) This provides preoperative characteristics and a second computer device of computer system 124 (for example) (The robotic device 120, which is placed in the operating room, controls the robotic device 120 and provides intraoperative characteristics.) According to various embodiments, the features and functions attributed to the computer system 124 in this specification may differ. any combination of one or more devices, servers, cloud-based computing resources, etc. It can be implemented using a combination or a distribution between them.

[0025] Input / output device 162 may be used as necessary for the functions and processes described herein. It is configured to receive input from the -er and display output. As shown in Figure 1, input / output The power device 162 includes a display 164 and a keyboard 166. Display 1 64 represents the graphical user interface generated by the processing circuit 160. It is configured to display, for example, information related to the surgical plan, medical images, surgical system Regarding the configuration of TEM 100 and other options, the tracking system 122 and the robotic device 120 Situational information and visualization of tracking based on data provided by the tracking system 122. This includes the keyboard 166 to these graphical user interfaces. It is configured to receive user input and control one or more functions of the surgical system 100. It can be done.

[0026] The processing circuit 160 facilitates the creation of a preoperative surgical plan prior to the surgical procedure, and the surgical plan The processing circuit is configured to facilitate computer and robot assistance during execution. An exemplary embodiment of 160 is shown in Figure 2 and will be described in detail below with reference to it. .

[0027] Furthermore, referring to Figure 1, according to some embodiments, the "virtual bone model" is used herein. Using a three-dimensional representation of the patient's anatomical structure, also known as a 3D model, preoperative surgical planning can be tailored to the patient's specific needs. It is developed to be different. The "virtual bone model" is a virtual representation of cartilage or other tissues in addition to bone. It may include imaginative expressions. In order to obtain a virtual bone model, the processing circuit 160 performs a surgical procedure. The system receives image data of the patient's anatomical structure (e.g., femur 106). This includes computed tomography (CT) and magnetic resonance imaging, which image the relevant anatomical features. Created using any appropriate medical imaging technique, including (MRI) and / or ultrasound. The image data is then segmented (i.e., images corresponding to different anatomical features). A virtual bone model is obtained (where the regions are distinguished). For example, specific bones, ligaments, cartilage, and To distinguish it from other tissues, MRI-based scan data of the knee was segmented and imaged. It is processed to obtain a three-dimensional model of the anatomical structure.

[0028] Alternatively, the virtual bone model is selected from a database or library of bone models. This may be obtained by selecting a dimensional model. In one embodiment, the user inputs / An appropriate model may be selected using the output device 162. In another embodiment, a processing circuit 160 executes stored instructions and provides appropriate information based on images or other information about the patient. Select a model. The bone model(s) selected from the database are then used to select a specific patient. Based on the characteristics of the individual, the virtual bone model used in the surgical planning and execution described herein is modified. Create.

[0029] A preoperative surgical plan can then be created based on the virtual bone model. This may be automatically generated by the processing circuit 160, or by the input / output device 162. This can be done by the user through an intermediary, or by some combination of these two. (For example, the processing circuit 160 restricts some of the features of the plan created by the user, (For example, generating a plan that can be modified.)

[0030] The preoperative surgical plan should be created using the surgical system 100, and the patient's anatomical information This includes desired cuts, holes, or other modifications to the structure. For example, all of the above as described herein. In knee arthroscopic surgery, the preoperative plan includes the femur 10 to facilitate the implantation of the prosthesis. This includes cutting necessary to form the surface of bones 6 and tibia 108. Thus, processing circuit 160 To facilitate the generation of surgical plans, if a prosthetic model is received, it can be accessed. In some cases, and / or in some cases, this may be stored.

[0031] The processing circuit 160 further generates the control targets of the robotic device 120 according to the surgical plan. It is configured to be such that, in some embodiments described herein, the controlled object is soft tissue. It is patient-specific based on its position and attachment point. The controlled object is of various possible types. It can take various forms depending on the robotic device (e.g., haptic, autonomous, etc.). For example In some embodiments, the controlled object is such that the robotic device 120 moves within the controlled object. Define instructions for the robotic device 120 to control it (i.e., tracking system Autonomous control of one or more dissections in the surgical plan, guided by feedback from Stem 122. (To be performed in a manner). In some embodiments, the controlled object is a hand on the display 164. This includes surgical planning and visualization of the robotic device 120, facilitating surgical navigation for surgeons. Helping to guide the robot to follow the surgical plan (for example, the active robotic device 120) (Without precise control or force feedback). The robot device 120 is a haptic device. In the implementation configuration, the controlled object may be a haptic object as described in the following section.

[0032] In an embodiment where the robot 120 is a haptic device, the processing circuit 160 is particularly soft Considering the location and attachment points of the tissue, one or more haptic pairs based on the preoperative surgical plan. Further configured to generate elephants, allowing for constraints on surgical tools 134 during surgical procedures. This assists the surgeon in carrying out the surgical plan. The haptic object is one-dimensional. It may be formed in two dimensions, or in three dimensions. For example, haptic The object of the haptic test can be a line, a plane, or a three-dimensional volume. The object of the haptic test can be a curved surface. It may be curved and / or have a flat surface, and can be any shape, for example, funnel-shaped. It is possible. Haptic objects vary regarding the movement of surgical tools 134 during surgical procedures. It can be created to represent a desired result. One or more boundaries of a three-dimensional haptic object are It may represent one or more modifications to be made on the surface of the bone, such as a cut. The object may represent a modification to be made on the surface of the bone, such as a cut (e.g., implantation). (This corresponds to the creation of a surface intended to accept dents.)

[0033] In an embodiment where the robot device 120 is a haptic device, the processing circuit 160 further It is configured to generate a virtual tool representation of the surgical tool 134. The virtual tool includes , includes one or more haptic interaction points (HIPs), which are physical This shows the position of the surgical tool 134 and is related to it. The surgical tool 134 is a spherical bur In this embodiment (for example, as shown in Figure 1), HIP represents the center of the spherical bar. To obtain. If the surgical tool 134 has an irregular shape, for example, a sagittal saw, the virtual representation of the sagittal saw is, May contain multiple HIPs. Multiple HIPs to generate haptic force against surgical tools. The use of IP is incorporated herein in whole by reference, 201 U.S. Patent Application No. 13 / 339,369, filed on December 28, 2019, with the title of invention "Sys tem and Method for Providing Substantial It is described in "ly Stable Haptics". In one embodiment of the present invention The virtual tool representing the sagittal saw includes 11 HIPs. A reference is considered to include references to "one or more HIPs." HIPs are as described below. Due to the relationship with the haptic object, the surgical system 100 restricts the surgical tool 134. You will be able to do that.

[0034] Prior to performing the surgical procedure, the patient's anatomical structure (e.g., femur 106) is determined to be appropriate. Known registration techniques allow for registration of a virtual bone model of the patient's anatomical structure. It can be done. One possible registration technique is point-based resist. This is a ration, incorporated herein in whole by reference, 2011. U.S. Patent No. 8,010,180, granted on August 30, with the title "Haptic It is described in "Guidance System and Method". Another method Registration is performed using a handheld radiographic imaging device, or a 2D / 3D registration. This may be achieved by stretching, and by referring to this specification as a whole. Incorporated U.S. Application No. 13 / 562,163, filed on July 30, 2012. The name "Radiographic Imaging Device" is listed as [the name of the device]. Registration also involves the virtual tool representation of surgical tool 134. This also includes registration 4, so that the surgical system 100 can provide (straight) to the patient. This allows for the identification and monitoring of the position of the surgical tool 134 (relating to the femur 106). Registration enables precise navigation, control, and / or force feed. The back can be done during surgery.

[0035] The processing circuit 160 determines the real-world position of the patient's bone (e.g., femur 106), and the surgical tool 1. 34, and one or more lines defined by the forces generated by the robotic device 120, A virtual tool representation, virtual bone model, and controlled object corresponding to a plane or three-dimensional space (for example) For example, it is configured to monitor the virtual location of a virtual haptic target. If the patient's anatomical structures move during the surgical procedure tracked by Tem 122, Circuit 160 moves the virtual bone model accordingly. The virtual bone model is therefore affected The actual (i.e., physical) anatomical structure of the person and in actual / physical space It corresponds to or is associated with the location and orientation of that anatomical structure. Similarly, any Cutting, modification, etc., performed on haptic objects, controlled objects, or their anatomical structures. Other planned automatic movements of the robotic device 120 generated during the linked surgical plans are also Furthermore, it moves in accordance with the patient's anatomical structure. In some embodiments, the surgical system 10 0 minimizes the need to track and process the movement of the femur 106, Includes clamps or braces for substantially securing 06.

[0036] In an embodiment where the robotic device 120 is a haptic device, the surgical system 100 is The surgical tool 134 is configured to constrain the relationship between HIP and the haptic target. This is done. In other words, the processing circuit 160 processes the data supplied by the tracking system 122. Use the surgical tool 13 to allow the user to make virtual contact with the HIP (Hip Injection) and the haptic target. If it detects that 4 is being operated, the processing circuit 160 will perform the following actions on the robot arm 132 A tactile feedback is used to generate control signals and communicate constraints to the movement of the surgical tool 134. The feedback (e.g., force, vibration) is returned to the user. Generally, as used herein, "constraints" The term "to do" is used to indicate a tendency to restrict movement. However, surgery The form of the constraints given to tool 134 depends on the form of the associated haptic object. Haptic objects can be formed in any desired shape or anatomical structure. Three exemplary embodiments include lines, planes, or three-dimensional volumes. In one embodiment, the HIP of the surgical tool 134 is along a linear haptic target. Because the movement is restricted, the surgical tool 134 is limited. In another embodiment, the haptic The target of the haptic test is a three-dimensional volume, and the surgical tool 134 is used to test the three-dimensional haptic test. By effectively preventing the movement of the hip outside the volume enclosed by the elephant wall, This may be constrained. In another embodiment, the haptic object of the plane is outside the plane and the plane To effectively prevent HIP movement outside the boundary of the haptic target of the surface, surgical tools 134 is constrained. For example, the processing circuit 160 is the planar distal of the femur 106. To establish a haptic object on the plane corresponding to the cut and to perform the planned distal cut The surgical tool 134 can be substantially limited to the plane required for this purpose.

[0037] In an embodiment where the robotic device 120 is an autonomous device, the surgical system 100 is a surgical device. The rod 134 is configured to move and operate autonomously according to the controlled object. For example, The controlled area can define the region related to the femur 106 to be amputated. In this case, one or more motors, actuators, and / or other components of the robot arm 132 may be used. The mechanism and surgical tool 134 are used to perform planned cuts, for example, by tracking system 1 Using tracking data from 22, the surgical tool 134 is moved within the controlled area as needed. It can be controlled to operate and enables closed-loop control.

[0038] Referring now to Figure 2, a detailed block diagram of the processing circuit 160 according to an exemplary embodiment. This is shown. The processing circuit 160 comprises a processor 200 and a memory 202. Rosser 200 is used as a general-purpose processor and as an application-specific integrated circuit (ASIC). As one or more field-programmable gate arrays (FPGAs), a group of processing It can be implemented as a logic component or as another suitable electronic processing component. Memory 202 (e.g., memory, memory device, storage device, etc.) is one of the various types described in this application. Data and / or computers to complete or facilitate processes and functions One or more devices for storing code (e.g., RAM, ROM, flash memory, etc.) It is equipped with disk storage devices, etc. The memory 202 may also be equipped with volatile memory. If present, it may also be equipped with non-volatile memory. Memory 202 is one of the various types described in this application. Database components and object code components to support operation and information structure. Comprising components, script components, or any other type of information structure According to an exemplary embodiment, the memory 202 is accessed by a processor via the processing circuit 160. It is connected to 200 in a communicative manner and performs one or more of the processes described herein (for example, process (Equipped with computer code for the circuit 160 and / or processor 200) ru.

[0039] As shown in Figure 2, the processing circuit 160 also includes the user interface circuit 204, and divides Circuit 206, attachment point identification circuit 208, implant placement circuit 210, surgical planning circuit 212 It also includes an intraoperative control circuit 214 and a communication interface 216. Various circuits 204 ~216 are connected to each other as processor 200 and memory 202, and communication interfaces. It is connected to 216 for communication. Figure 2 shows it as an integrated device, but several implementations In terms of form, the processing circuit 160 and its elements (i.e., processor 200, memory 202, Circuits 204-214 and communication interface 216) are connected to multiple computer devices. It may also be distributed across servers, robots, cloud resources, etc.

[0040] The communication interface 216 consists of the processing circuit 160 in Figure 1, the input / output device 162, and To facilitate communication between the trace system 122 and the robot device 120. Communication interface S216 also includes a processing circuit 160 and a preoperative imaging system 218 or the patient's anatomical structure. Other systems configured to provide preoperative medical images of the device to the processing circuit 160 (for example) Facilitates communication between electronic medical records and patient information databases. (Communication interface) 216 establishes secure communication sessions, including encryption and decryption capabilities, and cybersecurity To prevent or substantially mitigate the risk of harm, and to comply with patient record privacy laws and regulations. You may comply with this.

[0041] The user interface circuit 204 provides input / output devices 16 for one or more users. Generate various graphical user interfaces to provide via 2, and input The output device 162 is configured to receive, analyze, and interpret user input. A typical graphical user interface is shown in Figures 6-9, and you can refer to it. I will explain in detail below. As will be explained in detail below, the user interface circuit 204 is Various circuits 206-214 are connected in a communicative manner, and graphics from circuits 206-214 The system receives information for displaying the user interface and uses circuits 206-214. Provides the input.

[0042] The splitting circuit 206 receives medical images from the preoperative imaging system 218 and processes the medical images It is configured to divide the image and generate a three-dimensional virtual bone model based on the medical image. In this embodiment, medical images include computed tomography (CT) and magnetic resonance imaging (MRI). The image may be captured using one or more of various imaging techniques, including ultrasound. In the embodiment described in this document, the splitting circuit 206 mainly receives and utilizes CT images. Furthermore, the following description refers to CT images / imaging. However, various other embodiments may be described. Then, the processing circuit 160 processes various other images in addition to or in a different manner than the CT image. Types of medical imaging, such as magnetic resonance imaging (MRI), ultrasound, and / or two-dimensional X-rays. It is permissible to use X-rays, including three-dimensional reconstruction / modeling from line / fluorescence fluoroscopy images. I want to be understood.

[0043] The CT images received by the split circuit 206 are of the patient's femur 106 and / or tibia 108. Capture multiple views. These multiple views represent a series of slices, i.e., the patient's leg. Each CT image can therefore be a cross-sectional view at multiple locations along the path. A two-dimensional slice of the person's leg may be shown. The position and order of the CT images may be known.

[0044] The splitting circuit 206 separates bone (for example, femur 10) from surrounding tissues, fluids, etc. shown in the CT image. It is configured to divide to distinguish between the 6 and tibia (108). For example, a dividing circuit 20 6 can identify the bone boundaries shown in each CT image. In some embodiments, a splitting circuit 206 automatically identifies the boundary using automated image processing technology (automatic segmentation). In the implementation configuration, the division circuit 206 instructs the user to input the display of the boundaries of each image. CT images for integration into a graphical user interface - Provided to circuit 204. When user input is received that completely divides all images. Sometimes it's a combination of user input and automatic splitting, and sometimes some combination of user input and automatic splitting is used. For example, the user might be instructed to check the accuracy of the automatic splitting and adjust it as needed. There are also others.

[0045] The splitting circuit 206 further generates a virtual bone model (i.e., based on the split CT images) It is configured to generate a three-dimensional model. In the embodiment shown, the partition circuit 206 is The system generates virtual bone models of the femur 106 and tibia 108. The division circuit 206 performs the division process. Using the bone boundaries in each CT image slice defined by the method, the image slices are superimposed. The boundaries may be arranged sequentially at known intervals, and a surface corresponding to the boundaries may be generated. Circuit 206 thereby enables a three-dimensional surface, a set of voxels, or a given coordinate system. It is possible to generate a virtual bone model that can be defined as some other representation.

[0046] The attachment point identification circuit 208 receives a virtual bone model from the division circuit 206, and the virtual bone model Identify one or more soft tissue attachment points on the bone. Soft tissue attachment points are points on the bone where soft tissue attaches to the bone. Representation of a virtual bone model in a coordinate system, for example, the point or region where a ligament attaches to a bone. Therefore, according to various embodiments, and also in the case of various soft tissues and / or surgeries, soft tissue An attachment point is a point, line, surface, voxel or set of voxels, or any other representation. It can be defined as follows.

[0047] In the embodiments described herein, the attachment point identification circuit 208 identifies the patient's posterior cruciate ligament (PC). Identify the PCL attachment point corresponding to the site where L) attaches to the patient's tibia 108. In the embodiment, the attachment point identification circuit 208 also identifies the patient's anterior cruciate ligament (ACL) from the patient's tibia. The ACL attachment point corresponding to the area where 108 will attach is also identified. The attachment point identification circuit 208 also Furthermore, the ligament attachment points corresponding to the sites where the ACL and PCL attach to the femur 106 can also be identified. Various other soft tissue attachment points can also be identified.

[0048] In some embodiments, the attachment point identification circuit 208 automatically identifies soft tissue attachment points. For example, the attachment point identification circuit 208 identifies the extrema, inflection point, or other on the surface of the virtual bone model. Identifiable features can be identified. As another example, the attachment point identification circuit 208 uses machine learning to... A trained neural network is then used to identify soft tissue attachment points.

[0049] In some embodiments, the attachment point identification circuit 208 is connected to the user interface circuit 20 For 4, a graphic instructs the user to select the soft tissue attachment point of the virtual bone model. By instructing the system to generate a user interface, it recognizes soft tissue attachment points. Separate. The user interface circuit 204 visualizes the virtual bone model and provides input / output. The device 162 provides a tool for selecting one or more points in the virtual bone model. A graphical user interface can be generated. The attachment point identification circuit 208 can generate a graphical user interface. The system can receive input and define one or more soft tissue attachment points based on the user input.

[0050] The implant placement circuit 210 determines the size and placement of the surgical implants, and the virtual bone model It is configured to identify based on the delta and soft tissue attachment points. In some embodiments, The implant placement circuit 210 is based on the PCL attachment point of the tibia 108, under whole knee arthroscopy. It is configured to identify the placement of tibial and femoral implants in the event of surgery. In this embodiment, the implant placement circuit 210 is connected to a virtual bone model of the tibia 108. By superimposing a virtual tibial implant onto a virtual femoral bone model of femur 106, the virtual femoral implant is superimposed. The implant placement circuit 210 is superimposed on the virtual bone based on the PCL attachment point. Place the virtual tibial implant in the model (for example, to avoid interference with the PCL attachment point). Therefore, to optimize rotation and coverage based on the PCL attachment point. Figure 5 shows the impact of the ligament. Images to identify PCL attachment points in a bone model to determine if a protrusion may occur. Usage (frames A and B) and placement of the implant model onto the bone model (frame C ), as well as visually depicting the relationship between the planned placement of implants and the PCL. This shows the use of the image (frame D). A graphical user interface is used to demonstrate this alignment. The interface is shown in Figures 6 and 7.

[0051] In some embodiments, the implant placement circuit 210 is located in the femur 106 and tibia 10 Based on the PCL attachment point on 8 and the ACL attachment point of the PCL and ACL, ACL and PC It is configured to predict the line of action of L. That is, the implant placement circuit 210 is located in the tibia. A virtual ACL model for predicting the positions of the ACL and PCL between bone 108 and femur 106. The implant placement circuit 210 is then configured to generate a virtual PCL model. Through the entire range of motion of the knee, the virtual tibial implant and virtual femoral implant provide virtual Avoid conflicts (i.e., interference, pinching, restriction, etc.) between the ACL model and the virtual PCL model. The virtual tibial implant and virtual femoral implant can be positioned accordingly. The placement circuit 210 thereby allows the ACL or PC to be implanted by the implant component. This can make it easier to prevent collisions with L.

[0052] In some embodiments, the user interface circuit 204 is shown, for example, in Figures 6-9. As shown and described in detail with reference thereto, the virtual bone model is superimposed It generates a graphical user interface that shows the placement of the virtual implants. A graphical user interface allows the user to adjust the placement of the virtual implant. It may become possible to arrange it. In such an embodiment, the implant placement circuit 210 This limits the placement options available to the user, and the user can attach the virtual implant to the attachment point. Prevents positioning in a way that interferes with or collides with the ACL or PCL. In some cases, the implant placement circuit 210 is an input / output device 162 Issue a notice or warning (e.g., text message, alarm) to the user When allowing the user to select such arrangement while informing the server of the interference or collision. There is.

[0053] The surgical planning circuit 212 receives virtual implant placement information from the implant placement circuit 210. The system receives a virtual bone model. The surgical planning circuit 212 then adjusts the femur 106 and tibia 108. The implant is then accepted at the position identified by the implant placement circuit 210. It is configured to plan the necessary cuts to the femur 106 and tibia 108. In other words, the surgical planning circuit 212, by the implant placement circuit 210, the virtual femur and The femur and tibia are positioned at the same location where the tibial implant is placed in the virtual bone model. The femur 106 is positioned so that the plant can be placed on the femur 106 and tibia 108. And identify how the tibia 108 needs to be modified. The surgical planning circuit 212 is The surgery involves a series of planned planar cuts to be made to the femur 106 and tibia 108. The plan can be decided.

[0054] The surgical planning circuit 212 adjusts the planned cuts based on one or more soft tissue attachment points. It can be configured in such a way. For example, the surgical planning circuit 212 can be configured such that the planned cut is at the soft tissue attachment point. This can involve crossing, weakening soft tissue attachment points, or crossing with soft tissue attached to soft tissue attachment points. It is important to ensure that it does not puncture or otherwise pose a risk of damaging soft tissue. It can be configured to provide evidence. In the position identified by the implant placement circuit 210 If such cutting is required to position the plant, the surgical planning circuit 212 is in Send an error or warning message to the plant placement circuit 210, and implant placement The circuit 210 may require a review of the implant placement.

[0055] Based on the planned cuts, the surgical planning circuit 212 controls each of the planned cuts. A target (for example, a virtual haptic target) is generated, and these are attached to one or more soft tissues. It is based on points. Therefore, the surgical planning circuit 212 uses the soft tissue attachment points to be patient-specific. It generates a control target. That is, the surgical planning circuit 212 defines one or more of these control targets. This restricts the surgical tool from affecting one or more soft tissue attachment points. For example, A certain control target may correspond to a planar cross-section to be performed on the distal surface of the tibia 108. The circuit 212 shapes the controlled object so that it does not intersect with the PCL attachment point of the tibia. This is possible. Figures 8-9 show diagrams of such controlled objects, and the details below refer to them. explain.

[0056] The intraoperative control circuit 214 facilitates the execution of the surgical plan generated by the surgical planning circuit 212. The intraoperative control circuit 214 controls the tracking system 122 and the robotic device. It can communicate with 120, and for registration, navigation, and tracking, for example, Refer to Figure 1 and proceed as described above. The intraoperative control circuit 214 has one or more soft tissues attached. The landing site can be registered and tracked. The intraoperative control circuit 214 also controls the surgical planning circuit 2 The robot device 120 is configured to control the controlled object generated by 12. In an embodiment where the robot device 120 is a haptic device, the intraoperative control circuit 214 The surgical tool 134 is limited to the controlled object, for example, as described above with reference to Figure 1. The robot device 120 is controlled to set the following. The robot device 120 operates autonomously or automatically. In the embodiment of the robotic device, the intraoperative control circuit 214 controls the surgical tool 134 to cut ( To perform (multiple actions are possible), for example, refer to Figure 1 and proceed as described above within the controlled object. The robotic device 120 is controlled to move. The intraoperative control circuit 214 thereby controls the device. The robotic device 120 is controlled according to the control target generated by the surgical planning circuit 212. This allows for the protection of one or more soft tissue attachment points during the surgical procedure.

[0057] Referring now to Figure 3, the positions of one or more soft tissue attachment points according to an exemplary embodiment are A flowchart of Process 300 is shown to facilitate arthroscopic surgery of the joint. Process 300 is performed on the surgical system 100 in Figure 1 and the processing circuit 160 in Figure 2. Therefore, it can be done, and these will be referred to in the following explanation. As explained below, Process 300 facilitates complete knee arthroscopic surgery. However, Process 300 , and it may also be performed on various other systems, and among other surgeries in particular, Partial knee arthroscopy, bilateral cruciate ligament-preserving total knee replacement, partial hip arthroscopy, It is also applicable to various other surgeries, including total hip arthroscopy, and reconstructive knee and hip surgeries. Please understand that this may be the case.

[0058] In step 302, the processing circuit 160 processes the target anatomical structure, for example, patient 104. Receive computed tomography (CT) images of the tibia 108 and femur 106 of leg 102. The CT image is transmitted to a preoperative image system 218 (for example, a CT scanner) that can communicate with the processing circuit 160. The CT images may be captured by a CT scanner at various positions along the leg 102. This may include a set of two-dimensional images showing cross-sectional slices of leg 102.

[0059] In step 304, the CT image distinguishes the various tissues and structures shown in the image. It is divided for this purpose. For example, corresponding to tibia 108 or femur 106 in each CT image. The region can be identified. The contour of the tibia 108 or femur 106 in each CT image is drawn. Boundaries can be defined and saved. In some embodiments, the processing circuit 160 processes the tibia 10 Image the area corresponding to 8 or femur 106 in the rest of the image (i.e., soft tissue or other anatomy) The CT image is automatically divided to distinguish it from a portion of the image showing a specific structure. Other implementations Morphologically, the processing circuit 160 processes the boundary of the tibia 108 or femur 106 in each CT image. A graphical user interface that allows the user to manually input the display. To generate. In yet another embodiment, some combination of automatic division and user input By using this, the efficiency and accuracy of the division process are increased. The processing circuit 160 thereby, The various layers along bone 108 show the shape / boundary of tibia 108 and / or femur 106. Obtain split CT images.

[0060] In step 306, the processing circuit 160 processes the tibia 108 and / Alternatively, a virtual bone model of femur 106 is generated, that is, defined in each segmented CT image. The boundaries of the tibia 108 and / or femur 106 are superimposed with known separation between each CT image. The stacked images are then processed to create a tertiary representation of the tibia 108 and / or femur 106. It is possible to generate a virtual bone model (for example, a virtual tibia model and a virtual femur model).

[0061] In step 308, one or more soft tissue attachment points are identified on the virtual bone model. Furthermore, one or more sites where soft tissue attaches to the tibia 108 or femur 106 are said to be the virtual bone model The location of the part in the coordinate system of the virtual bone model is identified from the and / or CT images. The target is identified and defined as a soft tissue attachment point. For example, the PCL attaches to tibia 108. The PCL attachment points corresponding to the site can be identified and defined in this manner. Several embodiments Then, the processing circuit 160 allows the user to display or adjust the position of one or more soft tissue attachment points. To generate a graphical user interface that enables such a graphical user. Examples of physical user interfaces are shown in Figures 6-7, and refer to them for further details. This will be explained below. In some embodiments, the processing circuit 160 automatically processes one or more soft tissue attachment points. It is configured to identify dynamically.

[0062] In some embodiments, the soft tissue attachment points are determined using bone density information appearing in the CT image. It can be identified. For example, the part of the bone that attaches to soft tissue may be denser than other parts of the bone. Yes, these high-density areas can be distinguished from CT images, for example, from CT images. In some cases, the image may appear more opaque, while in other cases, it may appear brighter. The processing circuit 160 performs image recognition Using recognition techniques or automated segmentation methods, identify one or more regions in CT images associated with high bone density. In addition, these regions may be associated with soft tissue attachment points. The processing circuit 160 then... Furthermore, the soft tissue attachment points are automatically determined based on bone density information captured by CT images. It can be identified.

[0063] In step 310, the size and placement of the implant are determined based on the identified soft tissue attachment points. The following is determined. For example, the processing circuit 160 is used for tibial implants and femoral implants. A virtual implant model can be generated. The processing circuit 160 then processes the virtual implant The 3D model is superimposed onto the virtual bone model, and its size, placement, orientation, alignment, etc., are determined. There may be cases where evaluation is performed. The virtual implant model is shown superimposed on the virtual bone model. Examples of graphical user interfaces are shown in Figures 6-7, and further details can be found by referring to them. I will explain in detail.

[0064] The processing circuit 160 places the implant model on one or more soft tissue attachment points, covering the attachment points. This may necessitate the removal or weakening of the attachment point, or the impact of the implant on the tendon. By causing a sputter, it is possible to ensure that there is no interference. For example, processing circuit 160 The size of the tibial component of the knee implant is based on the PCL attachment point of the tibia 108. , position, and / or rotation can be determined.

[0065] In some embodiments, the processing circuit 160 processes the tibial bone corresponding to the PCL and patellar ligament. Identify the rotation or direction of the attachment point of the component base. More specifically, the PCL attached The rotation of the tibial component is determined using a line connecting the point of contact and the patellar ligament attachment point. Obtained. In some embodiments, one or more soft tissue attachment points are defined or manipulated by rotation. It is used as a landmark to obtain.

[0066] In step 312, the processing circuit 160 processes the implant identified in step 310. Based on size and placement, and based on one or more soft tissue attachment points, patient-specific A control target is generated. The control target is the femur 106 and tibia 10 by the surgical tool 134. Facilitating cutting or other modifications to 8, the femur at the position identified in step 310 and to prepare the femur 106 and tibia 108 to receive the tibial implant. The controlled object is shaped to avoid crossing or other interference with the soft tissue attachment point. and may be arranged.

[0067] In step 314, the surgical tool 134 controls and / or restricts based on the controlled object. In an embodiment where the robotic device 120 is an autonomous robotic system, the surgical tool 1 34 modifies the femur 106 and / or tibia 108 via one or more controlled objects. The controlled object is controlled to move autonomously. The controlled object is one or more on the virtual bone model. Because it is shaped to avoid soft tissue attachment points, the robotic device is made from an actual femur 106 Alternatively, the corresponding attachment site on the tibia 108 or the ligament or other attachment site It is controlled to avoid contact with the tissue. The robotic device 120 is a haptic device. In this configuration, the processing circuit 160 robotically restricts the surgical tool 134 within the controlled object. The device 120 controls the surgical tool 134, thereby allowing the ligament or other tissue to be 1 Contact with the attachment sites is restricted as the substance adheres to more than one attachment site. A series of surgical cuts are performed. This protects ligaments or other soft tissues from iatrogenic injury and complications associated with weakened attachment points. It can be done while limiting the risks.

[0068] Referring now to Figure 4, an exemplary embodiment shows an implant component Process 400 for preventing ACL and / or PCL collision in patients is shown. Process 400 is performed by the surgical system 100 in Figure 1 and the processing circuit 160 shown in Figure 2. It may be performed. While this specification describes its application to total knee arthroplasty, process 400 is... It can also be used in partial knee arthroplasty, early intervention knee surgery, etc. Process 400 is particularly, It may be suitable for bilateral cruciate ligament-preserving knee arthroplasty. Process 400 is the same as process 30 shown in Figure 3. This could be an example of step 310 for step 0.

[0069] In step 402, the processing circuit 160 determines the line of action of the ACL / PCL based on the CT image. Predicts the following: the CT image, the virtual bone model, and / or the identified femur 106 and tibia. Based on the attachment points of the ACL and PCL of bone 108, the processing circuit 160 processes the ACL and PCL. L predicts the space that extends to the full range of motion of the knee. For example, the processing circuit 160 predicts the ACL and / or Alternatively, a PCL model can be generated and added to the virtual bone model.

[0070] In step 404, the processing circuit 160 processes the ACL or PCL with the implant. The potential for collision is identified. The processing circuit 160 generates a virtual implant model, and Figure 3 is generated. Refer to the above explanation and superimpose the virtual implant model onto the virtual bone model. This may be the case. The ACL / PCL model may also be included. The processing circuit 160 is as follows: This involves obtaining virtual models including virtual bone models, virtual implant models, and virtual ligament models. It is possible to obtain. The processing circuit 160 examines the virtual model across its entire range of motion and determines the possibility of collision. Any overlap between the virtual implant model and the virtual ligament model shown can be detected. The overlap between the hypothetical implant model and the virtual ligament model is such that the implant is represented as This indicates that a collision may occur when the model is installed.

[0071] In step 406, the processing circuit 160, based on the likelihood of collision, the implant The size and position are selected. In other words, the processing circuit 160 eliminates the possibility of collisions. Alternatively, select the size and position of the implant to minimize the risk of collision. For example, the processing circuit 160 processes the virtual model, the virtual implant model and the virtual ligament model. The implant can be examined across the entire range of motion without creating overlaps between the joints. The size and position of the unit can be changed. The processing circuit 160 is as described above with reference to Figure 5. The images are used to determine the appropriate size and position of the implant to avoid collisions. A graphical user interface is provided, and this is the planned implementation. In an embodiment that allows the user to change the size and position of the element, processing time Route 160 is predicted to cause a collision if the proposed planned implant location is found to be The processing circuit 160 may also generate warnings or alerts to show the user. Prevent the user from selecting an option where the risk of conflict exceeds a threshold. In some cases, this may be the case.

[0072] In step 408, the processing circuit 160 uses the MRI image to analyze the virtual model and the collision The prediction is verified. The processing circuit 160 receives MRI images from the preoperative imaging system 218. The MRI image shows the femur, tibia, patella, cartilage, and ligaments (e.g., A) in the knee. It can be divided to distinguish between the CL, PCL, and patellar ligament. One of the bones, ligaments, and cartilage The above three-dimensional model can be created using, for example, the same method described above for the CT images. It may be generated by the MRI-based three-dimensional model of the planned implant. They may be placed in Dell. These models are then used, and the CT-based models will accurately impact It can be verified that a collision or non-collision was predicted. For example, if the MRI-based model predicts a non-collision If the CT-based model predicts no collision, then the MRI-based model predicts The CT-based model can be verified, and the planned position of the implant can be approved. Seth explained above regarding steps 312 and 314 of process 300, soft assembly Although selected to prevent the possibility of fabric collision, based on the size and position of the implant. This allows for the continued generation of patient-specific control targets.

[0073] Referring now to Figures 6-9, the processing circuit 160 according to an exemplary embodiment (example For example, it is generated by the user interface circuit 204 and sent to the input / output device 162. Therefore, the graphical user interface is displayed (i.e., shown on display 164). Various diagrams of Interface 500 are shown. Figures 6-9 each show graphical representations. User interface 500 is a virtual bone model of femur 106 (virtual femur model 55 The upper panel shows a visualization of 0) a virtual bone model of 502 and tibia 108 (virtual tibia model 552). The graphical user interface 500 also includes a lower section 504 showing a visualization of the graphical user interface 500. This also includes three columns corresponding to three sets of visualizations of virtual femur model 550 and virtual tibia model 552. The first row 506 shows frontal views of the virtual femur model 550 and the virtual tibia model 552. The second row 508 shows distal views of the virtual femur model 550 and the virtual tibia model 552. Row 3, 510, shows lateral views of the virtual femur model 550 and the virtual tibia model 552. Figures 6, 7, 8A, and 9 show visualizations of the virtual femoral implant 554 and the virtual tibial implant Includes visualization of plant 556. The graphical user interface 500 is This displays the planned implant placement to the user.

[0074] In the configuration shown in Figure 6, the graphical user interface 500 is a virtual thigh Bone model 550, virtual femoral implant 554, virtual tibia model 552, and virtual tibia A cross-sectional view of implant 556 is shown. Circle 558 is the PCL attachment point of the virtual tibia model 552. It surrounds (for example, with it as the center). In some embodiments, the position of circle 558 is The definition of the PCL attachment point can be adjusted by the user. (Dashed line 5) 60 indicates the height or plane associated with the PCL attachment point. In some embodiments, Line 560 can be adjusted by the user to move the position of the PCL attachment point. As shown in 6, the virtual tibial implant 556 is positioned so as not to interfere with the PCL attachment point. It is placed.

[0075] In the configuration shown in Figure 6, the graphical user interface 500 is a virtual thigh Bone model 550, virtual femoral implant 554, virtual tibia model 552, and virtual tibia Three-dimensional rendering of implant 556 is shown. Virtual femur model 550 and virtual tibia. Model 552 demonstrates the effect of planned cuts on the femur 106 and tibia 108. This is corrected to mean that the parts of the femur 106 and tibia 108 that should be removed during surgery are It has also been removed from the virtual femur model 550 and the virtual tibia model 552. The user can, Subsequently, the planned cut may be altered, damaged, crossed, interfered with, or altered at one or more soft tissue attachment points. You may also want to check if it has an impact in other ways. Graphical User Interface Face 500 is equipped with arrow buttons 600, which allow the user to input a virtual femur Adjusting the position, size, rotation, etc., of plant 554 and virtual tibial implant 556. You will be able to do that.

[0076] In the configuration shown in Figure 8A, the graphical user interface 500 is a virtual large Femoral bone model 550, virtual femoral implant 554, virtual tibia model 552, and virtual tibia A cross-sectional view of bone implant 556 is shown. In Figure 8A, the virtual boundary 700 is the controlled boundary. This indicates that the virtual boundary 700 is defined by the surgical tool 134 and the controlled object and the robotic device 120. This defines a boundary that restricts intersections. As shown in Figure 8, the virtual boundary 700 is It includes a concave notch 702, where the virtual boundary 700 is PCL attachment as indicated by highlight 704. It curves along the point. The virtual boundary 700 thereby allows the controlled object to move the surgical tool 134. This indicates that the system is configured to restrict access to the PCL attachment point.

[0077] In the configuration shown in Figures 8B-8C, the graphical user interface 500 is total A virtual femur model 550 and virtual tibia, including a virtual boundary 700 corresponding to the defined tibia section. Figure 8B shows a cross-sectional view of bone model 552, and Figure 8C shows a cross-sectional view of the right knee. As shown in Figures 8B-C, the virtual femur model 550 and the virtual tibia model 552 are used by patients. This includes CT images and / or other medical images showing bone density in various regions of the femur and tibia. can be visualized as such. This is useful for the user when identifying one or more soft tissue attachment points, when identifying areas of strong bone suitable for engagement with the implant, and / or for other planning or diagnostic purposes. As shown in FIGS. 8B - C, the virtual femur implant 55 4 and the virtual tibia implant 556 are not visible from the graphical user interface 500, but present a simplified view, thereby allowing the user to clearly see the planned cut facilitated by the virtual boundary 700.

[0078] As shown in FIGS. 8D - E, areas of higher density bone can be shown on the graphical user interface 500 as areas indicated by color - coded regions, shaded regions, or other differentiations. In the example shown, the differentiation 750 indicates the high - density bone area. The point 752 indicates the center of mass of the high - density bone area. The region 754 corresponds to the PCL and / or the PCL attachment point. The differentiation 750, the point 752, and / or the region 754 can facilitate implant planning. For example, the surgeon (user) can align the groove of the virtual tibia implant 556 with the point 752 (as shown in FIG. 8E) to optimize internal / external rotation. As another example, the surgeon can adjust the varus / valgus rotation of the tibial resection to optimize the density of the cut surface. Various further advantageous alignments and planning are facilitated by including the differentiation 750, the point 752, and / or the region 754 in the graphical user interface 500.

[0079] In the configuration shown in FIG. 9, the graphical user interface 500 includes the virtual femur model 550, the virtual femur implant 554, the virtual tibia model 552, and the virtual tibia Figure 9 shows a three-dimensional rendering of implant 556. It also shows the virtual boundary 700. In the embodiment shown, the controlled object is a planar controlled object, and the virtual boundary 700 is in the second row 5 Oriented to be visible only from the distal view of 08. Graphical User Interface The 500 is equipped with an arrow button 600, which allows the user to access a virtual femoral implant. The position, size, rotation, etc., of 554 and the virtual tibial implant 556 can be adjusted. This allows you to adjust the position, size, rotation, etc. of the virtual tibial implant 556. In response to the input, the processing circuit 160 appropriately adjusts the controlled object. The virtual boundary 700 of interface 500 will also be updated. Therefore, the user By adjusting the position, size, rotation, etc. of the virtual tibial implant 556, as needed... You may adjust the virtual boundary 700.

[0080] Referring now to Figures 10-12, we see the attachment of the patellar ligament to the tibia in an exemplary embodiment. A process 1000 for determining the rotational alignment of an implant based on this is illustrated. Figure 10 shows the flowchart of process 1000. Figures 11-12 show process 10 A diagram useful for explaining 00 is shown. Process 1000 is executed by the processing circuit 160 in Figure 1. It is possible. Process 1000 is part of an embodiment of process 300 in Figure 3, for example. This may be included as steps 304-310 in Figure 3.

[0081] Step 1002 involves identifying the tibia and patellar ligament attachment point (tibial tuberosity) in order to identify the patellar ligament attachment point (tibial tuberosity). The band is identified from the CT image. That is, the CT image shows the tibial tuberosity and / or patellar ligament. The image is divided to define the pixels or coordinates of the CT image corresponding to the band. By identifying the patellar ligament and the tibia, the patellar ligament attaches to the tibia (for example, by division). The regions that have been processed (and are adjacent to each other) become easier to identify. In some embodiments, the processing Circuit 160 automatically identifies the tibial tuberosity and / or patellar ligament in the CT image. (i.e., automatic division). In other embodiments, the processing circuit 160 is a graphical user • Generate an interface that provides the user with a table showing the location of the patellar ligament and / or tibial tuberosity. The system instructs the user to input (e.g., contour, region). The processing circuit 160 receives the user input. Based on the user input, the location of the tibial tuberosity and / or patellar ligament can be identified. For example. Figure 10 shows the division boundary 1008, which is identified as the lateral boundary of the patellar ligament at the tibial tuberosity. .

[0082] In step 1004, the medial edge of the patellar ligament at the tibial tuberosity is identified. In other words, Circuit 160 is the innermost point of the patellar ligament at the tibial tubercle where the patellar ligament attaches to the tibia (i.e.) Identify the innermost point (1110) in Figure 11. To identify this point, the patellar ligament and A "slice" of the CT image is selected at the level of the tibial tuberosity, which shows the attachment between the tibiae (for example, CT image in Figure 11 (1101). At that level, the medialmost point of the tibial tuberosity and / or patellar ligament. However, based on the segmentation data from step 1002, for example, the segmentation corresponding to the patellar ligament It is identified by selecting the point closest to the inner boundary of the CT image from the selected region. Circuit 160 thereby determines the coordinates associated with the medial edge of the tibial tuberosity and / or patellar ligament. Identify.

[0083] In step 1006, the rotation of the tibial component of the artificial implant is specified by aligning the axis of the tibial component with the tibial tubercle and / or the medial edge of the knee ligament (i.e., the innermost of the attachment regions of the knee ligament on the tibia). The processing circuit 160 generates a virtual implant model (e.g., virtual tibial implant 556) of the tibial component for alignment with the virtual bone model (e.g., as generated in step 306 of process 300). In some displays of the virtual implant model, for example, as shown in FIG. 11, the virtual implant model may include a representation 1100 of two or more axes that define one or more rotations of the virtual implant model. More specifically, representation 1100 shows the rotational alignment of the virtual tibial implant superimposed on the CT image 1001 of the tibia 108. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. In step 1006, the processing circuit 160 defines the rotation of the virtual tibial model such that the second axis 1104 intersects the medial edge of the knee ligament at the attachment point / region between the knee ligament and the tibia. In step 1006, the rotation of the tibial component of the artificial implant is specified by aligning the axis of the tibial component with the tibial tubercle and / or the medial edge of the knee ligament (i.e., the innermost of the attachment regions of the knee ligament on the tibia). The processing circuit 160 generates a virtual implant model (e.g., virtual tibial implant 556) of the tibial component for alignment with the virtual bone model (e.g., as generated in step 306 of process 300). In some displays of the virtual implant model, for example, as shown in FIG. 11, the virtual implant model may include a representation 1100 of two or more axes that define one or more rotations of the virtual implant model. More specifically, representation 1100 shows the rotational alignment of the virtual tibial implant superimposed on the CT image 1001 of the tibia 108. In step 1006, the processing circuit 160 generates a virtual implant model (e.g., virtual tibial implant 556) of the tibial component for alignment with the virtual bone model (e.g., as generated in step 306 of process 300). In some displays of the virtual implant model, for example, as shown in FIG. 11, the virtual implant model may include a representation 1100 of two or more axes that define one or more rotations of the virtual implant model. More specifically, representation 1100 shows the rotational alignment of the virtual tibial implant superimposed on the CT image 1001 of the tibia 108. In some displays of the virtual implant model, for example, as shown in FIG. 11, the virtual implant model may include a representation 1100 of two or more axes that define one or more rotations of the virtual implant model. More specifically, representation 1100 shows the rotational alignment of the virtual tibial implant superimposed on the CT image 1001 of the tibia 108. As shown in FIG. 11, the virtual implant model may include a representation 1100 of two or more axes that define one or more rotations of the virtual implant model. More specifically, representation 1100 shows the rotational alignment of the virtual tibial implant superimposed on the CT image 1001 of the tibia 108. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. In step 1006, the rotation of the tibial component of the artificial implant is specified by aligning the axis of the tibial component with the tibial tubercle and / or the medial edge of the knee ligament (i.e., the innermost of the attachment regions of the knee ligament on the tibia). The processing circuit 160 generates a virtual implant model (e.g., virtual tibial implant 556) of the tibial component for alignment with the virtual bone model (e.g., as generated in step 306 of process 300). In some displays of the virtual implant model, for example, as shown in FIG. 11, the virtual implant model may include a representation 1100 of two or more axes that define one or more rotations of the virtual implant model. More specifically, representation 1100 shows the rotational alignment of the virtual tibial implant superimposed on the CT image 1001 of the tibia 108.

[0084] As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image. As shown in FIG. 11, the first axis 1102 of representation 1100 may extend substantially end-to-end (i.e., from the inside to the outside), and the second axis 1104 may be perpendicular to the first axis 1102 in the plane of the CT image (i.e., the plane defined by the normal vector substantially parallel to the length of the tibia). The rotational alignment of the virtual implant model can be adjusted by rotating the first axis 1102 and the second axis 1104 around the intersection point 1106 of the first axis 1102 and the second axis 1104 in the plane of the CT image.

[0085] In step 1006, the processing circuit 160 defines the rotation of the virtual tibial model such that the second axis 1104 intersects the medial edge of the knee ligament at the attachment point / region between the knee ligament and the tibia. In step 1006, the processing circuit 160 defines the rotation of the virtual tibial model such that the second axis 1104 intersects the medial edge of the knee ligament at the attachment point / region between the knee ligament and the tibia. As shown in Figure 11, the second axis 1104 is the most important part of the dividing boundary 1108 corresponding to the patellar ligament. It extends through the medial point 1110. In other words, the rotation of the virtual tibial implant is as shown in Figure 1. The second axis 1104 shown in 1 reliably passes through the innermost coordinate identified in step 1004. It will be set to exceed the limit.

[0086] In step 1008, the rotational alignment of the virtual tibial implant is determined by the implant Rotation of a virtual model of the femoral component (e.g., virtual femoral implant 554) Used to determine alignment. For example, the rotational alignment of the virtual tibial implant. The rotational alignment of the implant and the virtual femoral implant is determined by a preset geometrical system. There may be a relationship, which is that the processing circuit 160 of the virtual tibial implant To determine the rotational alignment of the virtual femoral implant based on rotational alignment. It can be used for this purpose. Figure 12 shows the placement of a virtual femoral implant on the femur (i.e., large This shows representation 1200 (overlaid on CT image 1202 of the femur). As shown in Figure 12, The representation 1200 of the alignment of the virtual femoral implant is the virtual tibial implant This matches the alignment representation 1100.

[0087] The processing circuit 160 thereby adjusts the rotational alignment of the implant to the patellar ligament of the tibia. Identify based on the attachment points of the band.

[0088] As described above, process 1000 can be executed as a sub-part of process 300. Therefore, after step 1008, the processing circuit 160 determines the other size of the implant. Alternatively, identify the characteristics of the placement (i.e., step 310), and the placement of the identified implants. and generate a control target based on the attachment point of the patellar ligament to the tibia (i.e., step 312 ), the surgical tool 134 can be constrained or controlled based on the controlled object (i.e., step (P314).

[0089] In some embodiments, process 1000 further controls the density of grooves in the axial plane, the coronal plane, and Used to determine the orientation of the tibial baseplate in the sagittal plane, and the neutral implant alignment The process includes steps to achieve a compression. In some embodiments, coronal and sagittal density profiles Using a file, a good bone stock (bone) for optimal implant fixation. The stock can be identified.

[0090] Referring now to Figure 13, a graphical user interface according to an exemplary embodiment Face 1300 is shown. The graphical user interface 1300 is It is generated by the processing circuit 160 (for example, by the user interface circuit 204), Displayed by the input / output device 162 (i.e., shown on the display 164) In some embodiments, the graphical user interface 1300 is a graph Part of the visual user interface 500, for example, the second column 508 and the lower row 5 in Figure 7. This corresponds to the part seen in 04.

[0091] In the embodiment shown in Figure 13, the tool visualization 1302 of the surgical tool 134 is a virtual tibial model. It is superimposed on the cross-sectional end view of 552 (for example, on the tibia as shown by a CT image). The position of the visualized 1302 is updated in real time, and the surgery is performed on the actual tibia 108. The actual location of tool 134 is tracked / naked by, for example, the tracking system 122. This can be shown based on the navigation data. Graphical User Interface 130 0 also indicates an indicator 130 that highlights the virtual boundary 700 and the location of the PCL attachment point. This also includes 4. The user (for example, a surgeon) can use surgical tool 13 during surgical procedures. 4. On the virtual boundary 700 and the graphical user interface 1300, indicators The user may see the relative position of the attachment point indicated by -1304. Therefore, proper placement of the cut made using the surgical tool 134 with respect to the attachment point and / or Or, alignment can be supported.

[0092] This disclosure focuses on PCLs and ACLs, but also includes the systems described herein. The method can be adapted to identify and protect other soft tissues such as the medial collateral ligament (MCL). This disclosure is intended to provide a possibility.

[0093] As used herein, the term “circuit” is used to perform the functions described herein. It may include structured hardware. In some embodiments, each "circuit" This refers to a machine-readable medium for configuring the hardware to perform the functions described herein. The circuit may include a body. The circuit may include a processing unit, an electrical circuit, a network interface, or peripherals. One or more electrical circuits, including but not limited to input devices, output devices, and sensors. It can be embodied as a path component. In some embodiments, the circuit is one or more Analog circuits, electronic circuits (e.g., integrated circuits (ICs), discrete circuits, systems) On-chip (SOC) circuits, telecommunications circuits, hybrid circuits, and any other types It can take the form of a "circuit" of type Ip. In this regard, the "circuit" performs the operations described herein. It may include any type of component to achieve or facilitate the achievement. For example, the circuits described herein include one or more transistors, logic gates (e.g., NA ND, AND, NOR, OR, XOR, NOT, XNOR, etc.), resistors, multiplexers This may include resistors, capacitors, inductors, diodes, wiring, etc.

[0094] The “circuit” is also one or more that are communicably coupled to one or more memories or storage devices. This may include a processor. In this context, one or more processors are stored in the memory. In some cases, an instruction may be executed, or an instruction that can access one or more processors in other ways may be executed. In some embodiments, the one or more processors may execute various This can be embodied by the following methods. The one or more processors perform at least the operations described herein. It can be constructed in a manner sufficient to do so. In some embodiments, the one or more processors , can be shared by multiple circuits (for example, circuits A and B are several exemplary circuits) In the embodiment, it is stored in different areas of memory or accessed in other ways. (This may include, or may share, the same processor capable of executing the instructions.) Or, furthermore, the one or more processors are independent of the one or more coprocessors. It can be structured to perform a specific operation or to be executed in other ways. Other examples In the embodiment, two or more processors are coupled via a bus, independently, in parallel, and pipelined. It may enable multi-threaded instruction execution. Each processor has one or more general-purpose functions. Processors, application-specific integrated circuits (ASICs), field-programmable gates • Array (FPGA), Digital Signal Processor (DSP), or memory Other appropriate electronic data processing components structured to execute the instructions provided It can be implemented as a single-core processor. The one or more processors may be single-core processors, multi-core processors, etc. Core processors (for example, dual-core processors, triple-core processors, It can take the form of a quad-core processor, a microprocessor, etc. Several implementation forms In this configuration, the one or more processors may be located outside the device, for example, one or more The processor mentioned above could be a remote processor (for example, a cloud-based processor). Alternatively or further, the one or more processors may be located inside and / or local to the device. It could be. In this regard, a given circuit or its components may be locally (for example) (Also, it may be deployed as part of a local server, local computer system, etc.) If available, remotely (for example, as part of a remote server such as a cloud-based server) They may also be placed in one or more locations. For that purpose, the “circuits” described herein are located in one or more locations. It may include distributed components.

[0095] The construction and arrangement of the systems and methods shown in these various exemplary embodiments are for illustrative purposes only. While this disclosure has described only a few embodiments in detail, many modifications are possible. (For example, the size, dimensions, shape and proportions of various elements, parameter values, and material usage) (Variations in color, direction, etc.). For example, you can reverse the position of an element, or change it in other ways. Often, the properties or number of individual elements or positions may be changed. All such modifications are intended to be included within the scope of this disclosure. The order or arrangement of the steps of the method may be changed or arranged according to alternative embodiments. You may revise it. Other substitutions, modifications, changes, and omissions will not deviate from the scope of this disclosure. This may be done in the design, operating conditions, and arrangement of exemplary embodiments.

Claims

1. A method for operating a surgical robot, The processor incorporated in the surgical robot automatically identifies soft tissue attachment points in a virtual bone model based on bone image data, The processor plans the placement of the implant by using coronal and sagittal density profiles to avoid collisions of the soft tissue attachment points and to identify bone stock for implant fixation, and by optimizing internal / external rotation to align the virtual tibial implant based on the center of mass of the high-density bone regions identified from the image data. The processor generates a control target based on the arrangement of the implant, The processor controls the surgical robot to restrict the surgical tool to the controlled object. A method of operation, including the method of operation.

2. Identifying the soft tissue attachment point includes applying image recognition to the image data. The operating method of claim 1, wherein the image data includes CT images showing the coronal and sagittal density profiles.

3. The operating method of claim 1, wherein the identification of the soft tissue attachment points is performed by a neural network based on the virtual bone model.

4. The method of operation of claim 3, wherein the processor trains the neural network using machine learning.

5. The method of operation according to claim 1, wherein the processor includes identifying a bone stock for fixing the implant.

6. The operating method of claim 1, wherein the processor displays implant rotation based on the density of the cross-section of the bone relating to the bone receiving the implant in the position of the implant.

7. The operating method of claim 1, wherein the processor automatically categorizes the soft tissue attachment points in the virtual bone model based on the identification.

8. The processor predicts the line of action of the soft tissue based on the soft tissue attachment point, The processor enhances the virtual bone model with the virtual implant model of the implant, The processor identifies the relationship between the line of action and the virtual implant model. The operating method of claim 1, including the method described in claim 1.

9. The operating method of claim 8, further comprising the processor adjusting the orientation of the implant based on the relationship.

10. The operating method of claim 8, further comprising the processor selecting the size of the implant based on the relationship.

11. The operating method of claim 8, wherein the soft tissue is a tendon.

12. The processor includes generating a virtual anterior cruciate ligament model and a virtual posterior cruciate ligament model. The method of operation of claim 1, wherein planning the placement of the implants is further based on the virtual anterior cruciate ligament model and the virtual posterior cruciate ligament model.

13. It is a surgical system, Surgical robots and Computers and Includes, The aforementioned computer, Automatically identifying soft tissue attachment points in a virtual bone model based on bone image data, The placement of the implant is planned by using coronal and sagittal density profiles to avoid collisions of the soft tissue attachment points and to identify bone stock for implant fixation, and by optimizing internal / external rotation to align the virtual tibial implant based on the center of mass of the high-density bone region identified from the image data. To generate a control target based on the arrangement of the implants, Controlling the surgical robot to restrict the surgical tool to the controlled object A surgical system programmed to perform this procedure.

14. The computer is programmed to automatically identify the soft tissue attachment points by applying image recognition to the image data. The surgical system of claim 13, wherein the image data includes CT images showing the coronal and sagittal density profiles.

15. The surgical system according to claim 13, wherein the computer is programmed to automatically identify the soft tissue attachment points using a neural network applied to the virtual bone model.

16. The aforementioned computer, Predicting the line of action of the soft tissue based on the aforementioned soft tissue attachment points, The virtual bone model is enhanced with the virtual implant model of the implant, To identify the relationship between the aforementioned line of action and the aforementioned virtual implant model. A surgical system according to claim 13, which is programmed to perform the following.

17. The surgical system according to claim 16, wherein the computer is programmed to adjust the position of the implant based on the relationship.

18. The surgical system of claim 16, wherein the computer is programmed to select the size of the implant based on the relationship.

19. The computer is programmed to generate a virtual anterior cruciate ligament model and a virtual posterior cruciate ligament model. The surgical system of claim 13, wherein the computer is further programmed to plan the position of the implant based on the virtual anterior cruciate ligament model and the virtual posterior cruciate ligament model.

20. The surgical system of claim 13, wherein the computer is programmed to set the soft tissue attachment point as a rotation point and to allow adjustment of the position of the implant around the soft tissue attachment point.

21. The operating method of claim 1, wherein the processor plans the placement of the implant by aligning the axis of the implant with the inner edge of the soft tissue attachment point based on the soft tissue attachment point.

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