A system and method for characterizing bone density in patients undergoing initial total knee arthroplasty.

Abstract

By processing surgical planning information, the system automatically identifies the type of implant or the implant fixation process. It also determines patient-related bone density information by processing at least one image. The system then processes the determined bone density information along with patient-related demographic information to select multiple patients who have previously undergone total knee arthroplasty. Outcome information representing the arthroplasty procedure is processed, and thresholds representing mechanical tolerances are set. Finally, the system determines whether the patient-related bone density information is above or below the threshold.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This patent application claims priority based on U.S. Provisional Patent Application No. 63 / 466,482, filed on May 15, 2023, and the entire content of that application is incorporated herein by reference as if it were expressly set forth herein in its entirety.

[0002] This disclosure generally relates to data management and communication, and more specifically, to systems and methods for analyzing bone density to provide surgical recommendations regarding component selection, alignment, and fixation techniques.

Background Art

[0003] Cementless total knee arthroplasty (TAK) has regained attention because it enables long - term biological fixation and has a low probability of failure. The use of cementless fixation in total knee arthroplasty has increased rapidly in recent years. What was less than 5% of all primary TKA procedures performed in the United States in 2017 exceeded 20% in 2022. This rapid spread of cementless TKA is supported by significant advancements in implant design. For example, implant designs for arthroplasty include the use of highly porous three - dimensional (3D) printed materials and the incorporation of optimized keels and pegs. Also, surgical techniques (e.g., robotic assistance) have improved cementless TKA, providing improved conditions for initial fixation stability and rapid bone ingrowth.

[0004] For the same reasons as in TKA, cementless fixation is becoming increasingly popular in partial knee arthroplasties such as unicompartmental knee arthroplasty.

[0005] Generally, cementless fixation tends to be preferred in younger, more active patients, as it is considered beneficial in that it increases the long-term durability of biological fixation. Furthermore, younger, more active patients are thought to have higher bone quality than older patients, further increasing the likelihood of achieving reliable biological fixation.

[0006] Furthermore, alignment has been shown to affect joint loading and bone mineral density (BMD) in response to changes in joint moment. For example, in genu varum, bone mineral density is higher in the lower medial half of the tibial plateau, while in genu valgus, bone mineral density is higher on the lateral side. Generally, a large load is generated across the entire medial plateau, so a decrease in medial BMD is a concern in fixation.

[0007] Unfortunately, the characteristics of patients who are suitable candidates for cementless fixation remain unclear, which may be a factor hindering the rational use of cementless fixation. As a result, for example, TKA revision may be necessary due to sterile laxity, where laxity of the tibial component is more common than laxity of the femoral component. To avoid laxity in cementless fixation procedures, it is necessary to ensure stable initial fixation that promotes intraosseous growth, as well as sufficient bone support to prevent bone collapse due to load-bearing during daily activities.

[0008] This system and method address these shortcomings and other shortcomings in the art, and the disclosures herein are presented based on these and other considerations. [Overview of the Initiative]

[0009] The present invention provides a computerized method and system. In one or more embodiments of the present disclosure, at least one of the implant type and the implant fixation process is automatically identified by at least one computing device processing surgical planning information representing details related to the patient's total knee arthroplasty procedure. The at least one computing device also determines bone density information related to the patient by processing at least one image of the patient. The at least one computing device selects a plurality of patients who have previously undergone total knee arthroplasty, including at least a portion of the details related to the patient's total knee arthroplasty procedure, by processing the determined bone density information and by processing patient information representing at least demographics related to the patient. Furthermore, the at least one computing device sets thresholds representing mechanical tolerances for the implant fixation by processing outcome information representing the results of the joint replacement procedure related to each of the plurality of patients. The at least one computing device determines whether the determined bone density information related to the patient is above or below the set threshold by processing at least one instruction. Information relating to the determination of whether the bone density information related to the patient is above or below the set threshold is provided by the at least one computing device.

[0010] In one or more embodiments of the present disclosure, the outcome information further represents successful joint replacement procedures and joint replacement procedures that failed due to mechanical reasons.

[0011] In one or more embodiments of the present disclosure, setting the threshold further includes the processing of implant and fixation information representing implants, fixation, implant movement, and pain associated with joint replacement procedures related to each of the plurality of patients by the at least one computing device.

[0012] In one or more embodiments of the present disclosure, the at least one computing device compares the determined bone density information with information representing the bone density of a number of patients who have previously undergone total knee arthroplasty.

[0013] In one or more embodiments of this disclosure, the automatically provided information is at least one of an alert displayed on a screen, an audible alert, and a vibration alert.

[0014] In one or more embodiments of this disclosure, the automatically provided information includes recommendations relating to at least one of component selection, alignment, and fixing methods.

[0015] In one or more embodiments of this disclosure, the automatically provided information is a set of instructions constituting a robotic surgical system.

[0016] In one or more embodiments of the present disclosure, the at least one computing device quantifies the interaction between the implant and bone by providing the determined bone density information as input to a computational model.

[0017] In one or more embodiments of the present disclosure, the at least one computing device converts Hounsfield units into bone density information by processing the at least one image of the patient.

[0018] In one or more embodiments of the present disclosure, the at least one image of the patient is a computerized (CT) scan.

[0019] Additional features, advantages, and embodiments of this disclosure may be shown or become apparent through the detailed description and examination of the drawings. It should be understood that the above summary of the disclosure, the following detailed description, and the drawings are intended to provide further explanation without limiting the scope of the claimed disclosure.

[0020] Aspects of the present disclosure will be more readily understood by considering the detailed description of the various embodiments set forth below in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0021] [Figure 1] A block diagram showing exemplary embodiments of the present disclosure and a diagram representing the flow of information related to each device. [Figure 2] A block diagram showing functional elements of one or more data processing devices or computing devices. [Figure 3] Shows the standardized protocol used for computed tomography (CT) scans and the process flow obtained by a BMD reference phantom placed within the field of view of each CT scan. [Figure 4] Shows the reduction of BMD from proximal to distal, including total cut, inner half, outer half, and the upper and lower regions of each cut. [Figure 5] Shows a process flow including a BMD analysis section, a bone-implant interaction analysis section, and a surgical technique recommendation section according to an exemplary embodiment of the present disclosure. [Figure 6] Shows a process flow including a joint load analysis section, a bone-implant interaction analysis section, and a surgical technique recommendation section according to an exemplary embodiment of the present disclosure. [Figure 7] Shows a process flow including a BMD analysis section, a joint load analysis section, a bone-implant interaction analysis section, and a surgical technique recommendation section according to an exemplary embodiment of the present disclosure. [Figure 8] A graph showing dynamic KAM and static KAM in relation to joint load. [Figure 9] A process flow showing steps related to an exemplary embodiment of the present disclosure.

Modes for Carrying Out the Invention

[0022] As an introduction and overview, the present disclosure addresses knee joint loading and bone density, the ability of a patient's bone to resist static and repetitive loads, and subsequent long-term implant fixation, including those related to total knee arthroplasty (TKA). In one or more embodiments of the present disclosure, information related to knee joint loading and bone density distribution of the periarticular bone of a patient who is scheduled to undergo TKA or is currently undergoing TKA is provided. The systems and methods of the present disclosure operate to characterize bone mineral density (BMD), knee joint loading and moments, and the transmission of these loads between the implant and the bone, including the proximal tibia. The present disclosure can also be applied to partial knee arthroplasty (e.g., unicompartmental knee arthroplasty) or other bone and joint procedures such as the distal femur, hip joint, ankle joint, or elbow joint.

[0023] From a biomechanical perspective, a decrease in BMD is associated with an increase in micromotion of the tibial baseplate and can lead to the formation of fibrous tissue instead of bone. A decrease in BMD is also associated with an increased risk of bone destruction directly under the tibial baseplate and subsequent implant movement. For example, patients with low BMD experience greater movement as measured by radiostereometric analysis (RSA). Thus, an inverse correlation has been shown between implant movement and BMD. Conventionally, BMD has been measured at central sites such as the femoral neck or spine by dual-energy X-ray absorptiometry (DEXA). A correlation has been established between the measured BMD values at these central sites and the BMD of the knee joint (e.g., proximal tibia), and DEXA can also be obtained at the knee joint (e.g., proximal tibia), but DEXA does not provide information regarding the three-dimensional spatial distribution of BMD that may be important regarding implant subsidence. In addition to BMD, an increase in joint forces and moments may be associated with an increased risk of bone destruction directly under the implant and an increase in implant micromotion. For example, patients with high flexion and varus moments may have a higher risk of implant subsidence and increased micromotion.

[0024] This disclosure addresses concerns about sterile loosening, which remains a common cause of TKA revisions, particularly in light of the increasing use of cementless TKA. For example, this disclosure provides information that can be used to identify patients who are suitable candidates for biological (cementless) fixation. In one or more embodiments, the bone mineral density of the proximal tibia and / or knee joint load can be characterized prior to TKA. BMD can be provided by image analysis, including, but not limited to, a three-dimensional distribution of computed tomography (CT) scans, with respect to the planned intraoperative tibial transsection site of a patient scheduled to undergo (or currently undergoing) TKA. Load can be provided, for example, by an analysis of ground reaction forces acquired and synchronized with radiographic images.

[0025] In conventionally known scenarios, information on bone density is generally unavailable at the time of surgery, and surgeons often determine which patients should receive cemented or cementless implants based on surrogate indicators of bone strength, such as age or sex, or intraoperative assessments. However, this disclosure recognizes that sex or age are not appropriate surrogate indicators of bone density. Therefore, this disclosure identifies significant overlap in bone density between men and women across different age groups. For example, in the entire tibial osteotomy, the medial half of the osteotomy, or the lateral half, bone density in men is not affected by age, but women over 70 may have significantly lower bone density than women aged 60-70. However, in younger women, despite age affecting bone density, the difference in BMD between patients receiving cemented and cementless implants may not be significant. Paradoxically, women who receive cemented implants may have higher bone density than other women of the same age who receive cementless implants.

[0026] Instead of using conventional surrogate indicators of bone quality (e.g., sex, age), analyzing BMD values ​​against past patients who have undergone similar procedures helps surgeons determine whether the BMD is in line with the standard value or higher or lower than that of past patients, thus assisting them in deciding on the type of fixation (e.g., cemented or cementless).

[0027] Furthermore, it is acknowledged herein that analysis based solely on BMD does not take into account patient-specific joint loads and moments that may contribute to implant failure. Therefore, even with high BMD, patients may generate joint forces and moments sufficient to cause implant failure.

[0028] These knee joint forces and moments can be calculated from the ground reaction force and its relative position to the knee. Conventional methods require complex, costly, time-consuming, and highly specialized research in a dedicated motion analysis laboratory to determine dynamic joint forces and moments during everyday movements (e.g., walking on level ground). However, it is also possible to obtain static surrogate indices that represent the most important cases in everyday movements. For this purpose, the position of the knee center relative to the ground reaction force can be determined by combining ground reaction force measurements obtained from a force plate with synchronized radiographic images.

[0029] Specifically, biplane radiographic images can be used to obtain the three-dimensional spatial position of the knee joint center relative to the reference coordinate system of the force plate. These acquisitions can be performed in different postures, such as bipedal and single-leg stance, to comprehensively evaluate the knee load environment. Thus, the two-dimensional projection of any three-dimensional object can be modeled using a pinhole camera model, where the external transformation represents the relationship between the global reference system and the camera reference system, and the internal camera transformation matrix generates the two-dimensional projection of the object in the image reference system. The external and internal transformation matrices are geometric functions of the system, including the distance or pixel dimensions (mm) between the camera and the image detector. When using a biplane system, there is an inherent relationship between the two-dimensional projection of an object and its three-dimensional position. This relationship depends on the relative positions of the two cameras. Therefore, by knowing the relative positions of the cameras and detectors in the system and image parameters such as the size (mm) of each image pixel, it is possible to set up an inherent set of external and internal matrices that enables the determination of the three-dimensional position of an object from the biplane image representation. The intrinsic and extrinsic matrices can be obtained through a calibration process that involves taking multiple images of a known 3D object with the system and optimizing various parameters.

[0030] The systems and methods described in this disclosure can be implemented, at least in part, by, for example, an EOS® biplane radiography system. An EOS system typically includes a fixed geometry of a system that creates images with fan beam projection so that there is no vertical magnification. Furthermore, the system corrects for any horizontal magnification to produce an unmagnified image. In this case, the pinhole camera model must be modified to exclude vertical magnification and account for the corrections introduced by the system. Such corrections can be incorporated into a unique camera transformation matrix and can be determined by knowing the system geometry or by a calibration step. For example, magnification correction can be performed so that the image is at true magnification (i.e., true size) at the center of the patient. Thus, the image can be corrected by the ratio of the distance from the source to the patient to the distance from the source to the detector. The three-dimensional position of any object can be obtained by simultaneously solving the back projection problem for both cameras. Thus, the three-dimensional position of the knee joint center can be obtained by selecting corresponding points on the frontal (anterior-posterior) and lateral radiographic images and solving the back projection problem. Similarly, the position of known points on a force plate can be determined. These points can correspond, for example, to points along the edge of the force plate, thereby defining the orientation and position of the force plate relative to the EOS imaging system. As a result, the radiographic measurements of the knee joint center can be spatially and temporally synchronized with the force plate measurements, allowing the joint moment to be calculated as the product of the ground reaction force and the distance from the ground reaction force to the knee joint center.

[0031] The use of EOS and other imaging systems in this specification is presented as an example of implementation, and those skilled in the art will understand that the teachings herein also include other single-plane or biplane radiography systems.

[0032] This disclosure recognizes that these static loads can serve as appropriate surrogates for dynamic joint loads. In particular, the knee adduction moment during single-leg standing can serve as a surrogate for the peak dynamic knee adduction moment during walking on a horizontal surface.

[0033] These loads and moments, along with BMD information, can be further used as input to computational models (e.g., finite element models) to estimate load transfer between the implant and bone and determine the combined effects of BMD and joint loading on risks such as implant sinking, micro-motions between bone and implant, or bone deformation.

[0034] Accordingly, this disclosure addresses the decision of whether or not to recommend each type of fixation by treating sex and age as potentially limited surrogate indicators of bone quality, and not overly weighted, at least in isolation. Such recommendations can be formatted in a variety of ways, including text notifications, voice notifications, vibration notifications, robotic surgical plans, or other appropriate forms. This disclosure applies a more detailed analysis of bone mineral density distribution and / or joint loads and moments, considering bone mineral density and joint loads and moments as inputs to a biomechanical calculation model (e.g., a finite element model), analyzing load transfer between implants and bone, with the ultimate goal of determining which patients are suitable candidates for cementless TKA and, conversely, which patients require cement fixation.

[0035] Furthermore, this disclosure enables the analysis of BMD, joint force and moment, or bone-implant interaction against standard values ​​to determine whether these variables or the values ​​of a particular patient are higher or lower compared to other patients who have undergone the same procedure in the past. For example, by creating a standard curve of BMD along the proximal 12 mm of the tibia of past patients and plotting the patient's BMD against it, the bone mineral density of a patient can be analyzed against a cohort of past patients who have undergone TKA. Similarly, standard values ​​for changes in knee joint force and moment before, after, or in the knee joint force and moment can be obtained from past patients and used to define the standard values ​​to analyze the knee joint loading (i.e., force and moment) of a patient against a cohort of past patients who have undergone TKA.

[0036] Furthermore, one or more computing devices can be configured to limit one or more factors that may cause discrepancies by analyzing and considering each implant and implant design, for example, by limiting the analysis to patients with cruciate-retaining inserts. Clinically relevant thresholds for determining bone density can also be established by comparing bone density of successful procedures with those that failed for mechanical reasons, or by relating BMD, joint loading and moment, or implant-bone interaction to outcomes such as implant migration, pain, or patient-reported outcomes. Various aspects of bone quality, such as bone metabolism or collagen content, can be additionally considered by one or more computing devices to assess bone quality as a multifactorial index, which includes factors beyond bone density that may only partially capture the bone's ability to resist joint loading during daily activities. Furthermore, this disclosure may also include analyzing temporal changes in knee joint loading and moment that may occur after TKA, as well as relevant changes in load transfer that may affect the bone's load-resistance capacity. Therefore, evaluation of postoperative changes in knee joint load and moment, as well as related changes in load transfer, may be further included in one or more embodiments of this disclosure.

[0037] Furthermore, one or more computing devices, configured to execute instructions stored in a non-temporary processor-readable medium, can determine information related to the affected bone density. For example, a patient undergoing robotic-assisted total knee arthroplasty (TKA) undergoes a computed tomography (CT) scan preoperatively. The Hounsfield units of the CT scan can be converted to BMD, for example, using a calibration phantom. Calibration can be performed synchronously if the phantom is included in the actual scan, or asynchronously if the phantom is scanned on the same machine with the same scan parameters but at a different time than the target scan. Phantom-less calibration techniques using reference BMD values ​​for specific tissues or air can also be used in this step. Using a robotic surgical plan, one or more computing devices (for example, using information provided by the planned surgical procedure) determine the planned tibial cut and calculate the BMD distribution in cross-sections parallel to the cut at 1 mm intervals, from 2 mm above to 10 mm below the cut. Using the information obtained, bone density can be analyzed with respect to various patient factors such as sex, age, alignment, and type of fixation. By referring to past patients who underwent total knee arthroplasty (TKA) using similar implants, the distribution of bone mass (BMD) can be further analyzed. Calculating the BMD distribution for various possible implant placements can also contribute to determining the optimal implant alignment and fixation method for each patient.

[0038] This disclosure provides the ability to determine information related to affected joint forces and moments by one or more computing devices configured to execute instructions stored in a non-temporary processor-readable medium. For example, a patient undergoing total knee arthroplasty (TKA) can receive biplane radiographic images using, for example, an EOS biplane radiography system. These can be synchronized with force plate measurements of ground reaction forces. By solving a static equilibrium problem, joint moments can be determined as the product of the ground reaction force and the distance from the ground reaction force to the knee center, and joint forces can be determined as the sum of external forces and forces generated by muscles. This analysis can be performed preoperatively, and expected postoperative joint forces and moments can be predicted by finding similar patients with similar preoperative joint forces and moments and the desired alignment.

[0039] This disclosure provides the ability to determine information related to bone-implant interactions by one or more computing devices configured to execute instructions stored in a non-temporary processor-readable medium. For example, using the BMD distribution and calculated joint and moment, a patient-specific computational finite element model can be generated to calculate the relative motion between the implant and bone, which is an indicator of intraosseous growth, or the stress and strain of the bone, which can determine the risk of bone fracture in relation to bone strength. The relative motion between the implant and bone can be compared to known thresholds for intraosseous growth. For example, it has been shown that if the relative motion exceeds 150 micrometers, only fibrous tissue will form between the implant and bone, but if it is less than 40 micrometers, intraosseous growth may occur. Similarly, bone strain can be compared to known bone strength. The fracture compressive strain of the tibia is set to 7300 microstrain, and the fracture tensile strain to 6500 microstrain. By comparing bone strain to these set thresholds, the amount of bone at risk of mechanical fracture can be calculated and reported. Thus, the surface area of ​​an implant susceptible to intraosseous growth can be calculated and reported as the region where micromotion and strain are within acceptable limits. Therefore, based on this information, recommendations can be made regarding the use of cemented or cementless fixation, or for the specific implant position and size.

[0040] As shown in Figure 1, a block diagram illustrating an exemplary embodiment of the present disclosure is shown, representing the association of multiple devices and the flow of information associated with the devices 108. In the example shown in Figure 1, various computing devices 102 and 104 are shown, each capable of running web browser applications for desktop and / or mobile computing devices, including MICROSOFT EDGE, INTERNET EXPLORER, CHROME, FIREFOX, and others (e.g., SAFARI, OPERA). In addition to standard web browser application functionality, user information can be collected via push notifications, and information can be retrieved from computing devices using a "REST" interface. Various mobile devices running different operating systems are shown, including iOS, ANDROID®, and others (e.g., PALM, WINDOWS®, or other mobile devices).

[0041] In the example shown in Figure 1, one or more data processing devices 102 are operably coupled to one or more user computing devices 104. Each device 102 / 104 may be operated by one or more users proficient in using the proposed workflow, and these users include, but are not limited to, healthcare providers and related staff, medical professionals, and / or biomechanics experts. Healthcare providers may include, for example, physicians, medical assistants, nurses, therapists, and / or other healthcare service providers. Biomechanics experts may include, for example, engineers specializing in biomechanics. The data processing devices 102 and / or user computing devices 104 are operable to access and / or store various information in a database 103, including, for example, past medical and procedural information such as patient, physician, and device. Also shown in Figure 1 is a robotic surgical system 109, which may include, for example, one or more processors, network interfaces, imaging technology, mechanical arms, and surgical instruments, in various forms known in the art.

[0042] As shown in Figure 1, a network 106 is shown which can be configured as a local area network (LAN), a wide area network (WAN), a peer-to-peer network ("P2P"), a multi-peer network, the Internet, one or more telephone networks, or a combination thereof, and which is capable of operating to connect data processing devices 102 and / or other devices. Many of the examples and implementations shown and described herein relate to recommendations for products and / or services, but many other forms of content can be provided and / or delivered by system 100.

[0043] Figure 2 is a block diagram showing one or more functional elements of a data processing device 102 or computing device 104, which preferably include one or more central processing units (CPUs) 202 that execute software code to control various operations, including the operation of the data processing device 102; read-only memory (ROM) 204; random access memory (RAM) 206; one or more network interfaces 208 for sending and receiving data to and from other computing devices via a communication network; storage devices 210 such as hard disk drives, solid-state drives, USB drives, floppy disk drives, tape drives, CD-ROMs or DVD drives for storing program code, databases and application code; one or more input devices 212 such as keyboards, mice and trackballs; and a display 214.

[0044] The various components of device 102 and / or 104 do not need to be physically housed in the same enclosure and do not need to be located in a single location. For example, the storage device 210 may be located separately from the rest of the computing devices 102 and / or 104 and connected to the CPU 202 via the network interface 208 through the communication network 106.

[0045] The functional elements shown in Figure 2 (indicated by reference numerals 202-214) preferably belong to the same category as functional elements that are preferably present in computing devices 102 and / or 104. However, not all elements are necessary, such as storage devices in mobile computing devices (e.g., smartphones), and the capacities of various elements are adjusted to meet the expected user needs. For example, the CPU 202 in computing device 104 may have a smaller capacity than the CPU 202 present in data processing device 102. Similarly, data processing device 102 may include a storage device 210 with a much larger capacity than the storage device 210 present in computing device 104. Naturally, those skilled in the art will understand that the capacities of functional elements can be adjusted as needed. For example, one or more graphics processing units (GPUs) can be used to process and provide the functions shown and described herein. In addition, or alternatively, a cluster of computing devices may operate to provide the functions shown and described herein.

[0046] Due to the nature of this disclosure, a person skilled in the art with expertise in creating computer executable code (software) can implement the described functions using one or more common computer programming languages, including but not limited to C++, JAVA®, ACTIVEX, HTML, XML, ASP, SOAP, IOS, OBJECTIVE C, ANDROID, TORR, PYTHON®, MATLAB®, and various web application development environments, or a combination thereof.

[0047] In this specification, displaying data on computing device 104 refers to the process of transmitting data to computing device 104 via communication network 106 and processing the data so that it can be displayed on the user's computing device 104 display 214 using a web browser, custom application, etc. The display screen of computing device 102 / 104 displays regions within system 100, thereby allowing the user to move between regions within system 100 by selecting desired links. Thus, each user's experience in system 100 is based on the order in which they move between display screens. In other words, because the system is not entirely hierarchical in the arrangement of display screens, the user can move between regions without "going back" through a series of display screens. Therefore, unless otherwise specified, the following description is intended to describe the components of system 100, rather than representing a series of operational steps.

[0048] One or more computing devices can be configured to process information related to patients who have undergone preoperative computed tomography (CT) scans prior to robotically assisted initial total knee arthroplasty (TKA). For example, information can be processed from a standardized protocol used for the CT scan (e.g., 120kV, 200mA, slice interval: 0.625mm) and a BMD reference phantom placed within the field of view of each CT scan (see, for example, Figure 3). As shown in Figure 3, the BMD reference phantom can consist of five rods, each containing a different known amount of K2HPO4 and water, which can be used to convert Hounsfield units into volumetric BMD, which correlates highly with physically measured bone ash density.

[0049] Each patient can receive the same implant design that preserves the posterior cruciate ligament (PCL). Other types of implants may also be considered. Using this identified information, one or more computing devices preoperatively determine and analyze bone density in patients undergoing total knee arthroplasty using CT, calculate the load on each knee joint using EOS images synchronized with force plate measurements, and provide surgical recommendations regarding component selection, alignment, and fixation techniques.

[0050] More specifically, the tibia was segmented from the CT scan, and patient-specific and scan-specific relationships obtained using a BMD reference phantom were used to convert each voxel to Hounsfield units in BMD units (mg / cm³ of K2HPO4). 3 ) can be converted to BMD. For this purpose, a scan-specific linear relationship can be obtained between Houndsfield units and BMD by associating the Houndsfield units of a material with known BMD (e.g., a BMD calibration phantom) with their known BMD values. Such a relationship can be applied to each voxel to convert Houndsfield units to BMD. This provides the volume distribution of BMD for each patient, which can be analyzed relative to the superior-inferior, medial-lateral, and anterior-posterior axes of each tibia, defined from bone landmarks used to create robotic surgical plans. These can be identified, for example, by matching the position on the CT scan (Figure 3) using image recognition processes, machine learning, or artificial intelligence. Tibial transsections can be reconstructed by considering posterior tilt (i.e., rotation around the medial-lateral axis), varus-valgus alignment (i.e., rotation around the anterior-posterior axis), and the transsection thickness relative to a reference landmark on the plateau retrieved from the saved intraoperative robotic plan.

[0051] Continuing with the example shown in Figure 3, the volumetric BMD distribution can be used to extract the two-dimensional distribution of BMD in various cross-sections parallel to the robotic tibial section. These cross-sections can be equally spaced at 1 mm intervals, for example, from 2 mm above to 10 mm below the section. For this purpose, a pixel grid with the same dimensions as the voxel size of the axial slice (0.488 × 0.488 mm) can be defined in each plane parallel to the tibial section. One or more computing devices can then be configured to execute instructions stored in a non-temporary processor-readable medium to determine the intersection of each three-dimensional voxel with a different plane, thereby obtaining the BMD distribution at each pixel in the plane. The average BMD can be calculated for each cross-section as a whole, for the inner and outer halves of the cross-section, or for other subdivisions of the cross-section. See, for example, Figure 4. As shown in Figure 4, the respective BMDs (mg / cm²) include 5%–95% (shown in light gray) and 25%–75% (shown in dark gray). 3 The percentage of patients with ) is shown. Furthermore, the mean BMD is shown by a solid black line. The computing device can further analyze BMD as a function of tibial resection depth (distance from tibial resection) and correlate BMD in the robotic resection and the medial and lateral halves of the resection with the patient's sex, age, preoperative alignment (i.e., neutral / varus vs. valgus), and intraoperative fixation method selected (i.e., cemented vs. cementless). The BMD analysis can be limited to any region of the resection. For example, according to the robotic surgical plan, the baseplate can be virtually positioned in the planned resection. The baseplate positioning can be done manually or automatically. The BMD analysis can then be limited to the bone directly beneath the baseplate (see Figure 5).

[0052] Figure 5 shows a process flow including a BMD analysis section, a bone-implant interaction analysis section, and a surgical procedure recommendation section according to an exemplary embodiment of the present disclosure. As shown in Figure 5, the BMD analysis section 502 includes robotic planning information and BMD distribution (see, for example, Figure 3). The BMD analysis section 502 also includes the mean BMD for each cross-section, and the calculated mean BMD for the medial and lateral halves of the cross-section (see, for example, Figure 4), as well as a line representing the current patient's BMD (shown as a light green line). Subsequently, the bone-implant interaction analysis section 504 determines the planned joint replacement procedure, and then the surgical procedure recommendation section 506 can provide recommendations regarding implant fixation techniques, alignment, or other recommendations. Based on the BMD values ​​for the patient cohort, surgical recommendations can be made. For example, if a patient's BMD is in the bottom 25% of a baseline cohort, a warning message may be issued to inform the patient that their BMD is relatively low and suggest considering a different fixation method, such as cement fixation, or considering a different alignment. Furthermore, using the segmented bone volume obtained from CT scans, computational biomechanical models can be generated, such as finite element models or other models capable of calculating bone deformation under load. These loads can correspond to loads from daily activities or other loads. The BMD distribution of the entire bone volume or a sub-region of the bone volume (e.g., the most proximal 100 mm) can be used as input to the model to account for the non-uniform spatial distribution of bone material properties. For this purpose, empirical relationships can be used to convert BMD to the elastic modulus of bone. In addition, virtual surgery can be performed according to the plan to determine bone-implant interactions such as micromotion or bone deformation, and this information can be used to further reflect in the surgical plan. Recommendations can then be made based on values ​​calculated from the bone-implant interactions.Furthermore, by incorporating BMD or computational models into the algorithm, it is possible to identify BMD or bone-implant interactions for various alternative alignments or fixations of implants, and provide surgical recommendations based, for example, on implant positions that maximize BMD or minimize micromovement or bone distortion.

[0053] Figure 6 shows a process flow including a joint load analysis section 602 containing load information (see, for example, Figure 3). Subsequently, the bone-implant interaction analysis section 504 determines the planned joint replacement procedure, and then the surgical procedure recommendation section 506 can provide recommendations regarding implant fixation techniques, alignment, or other recommendations. In addition to, and similar to, BMD analysis, analysis based on the determined joint load can provide recommendations for patients with loads outside the normative values ​​of a reference patient cohort. Such recommendations may include, for example, the use of cement fixation or a change in alignment if the load is in the top 75% of the reference patient cohort. Load information can also be incorporated into algorithms that determine alignment ranges such that the expected joint load is below the 75th percentile of the load in the reference patient cohort. Furthermore, load information can be input into computational models, such as finite element models, to determine bone-implant interactions under patient-specific load conditions.

[0054] Figure 7 shows a process flow including the BMD analysis section 502 (Figure 5), the joint loading analysis section 602 (Figure 6), and the bone-implant interaction analysis section 504, which determine the planned joint replacement procedure, and subsequently, the surgical procedure recommendation section 506 provides recommendations regarding implant fixation techniques, alignment, or other recommendations. Joint loading information can also be combined with BMD information to provide a comprehensive and composite assessment for preoperative planning. For example, BMD information can be analyzed together with loading information from a baseline patient cohort to determine whether the combined effect of BMD and joint loading is suitable for cementless fixation, or whether changes to the surgical plan, such as alternative fixation methods or alternative alignments, are necessary. More specifically, patients with high BMD may generate large loads, which could compromise fixation. Therefore, composite analysis can identify such situations and provide recommendations. These recommendations can be further refined based on a computational model that determines bone-implant interaction, using the patient's BMD and joint loading as inputs.

[0055] Figure 8 is a graph showing dynamic and static knee adduction moment (KAM) in relation to joint loading. Each point in the graph corresponds to a different patient, and it shows that the static knee adduction moment obtained from radiography, represented on the horizontal axis, is a suitable surrogate for the dynamic knee adduction moment obtained during motion analysis, represented on the vertical axis. This is supported by values ​​close to the gray line, where the static and dynamic knee adduction moments are equal.

[0056] Figure 9 is a process flow illustrating steps relating to an exemplary embodiment of the present disclosure. As shown in Figure 9, at least one computing device automatically identifies at least one of the implant type and implant fixation method by processing surgical planning information representing details related to the patient's total knee arthroplasty procedure (step 902). At least one computing device also determines patient-related bone density information by processing at least one image of the patient (step 904). At least one computing device selects a number of patients who have previously undergone total knee arthroplasty, including at least some of the details related to the patient's total knee arthroplasty procedure, by processing the determined bone density information and by processing patient information representing at least demographics related to the patient (step 906). Furthermore, at least one computing device sets thresholds representing mechanical tolerances for implant fixation by processing outcome information representing the results of the joint replacement procedure related to each of the number of patients (step 908). At least one computing device determines whether the determined bone density information related to the patient is above or below the set threshold by processing at least one instruction (step 910). Information related to the determination of whether the patient's bone density information is above or below a set threshold is provided by at least one computing device (step 912).

[0057] Accordingly, as shown and described herein, this disclosure provides a computational framework for a comprehensive understanding of the biomechanics of the knee after total knee arthroplasty. The framework objectively evaluates potentially important trade-offs between joint-level mechanics and fixation-level mechanics. The framework can then be used to optimize implant placement to maximize the long-term durability and function of total knee arthroplasty and to create patient-specific preoperative plans. To this end, a workflow for quantifying joint-level mechanics, including tibiofemoral joint forces, using a musculoskeletal model is employed as input to a finite element model to quantify fixation-level mechanics. As shown and described herein, a demonstration of the framework for determining the relationship between knee AP movement and the risk of bone-implant micromovement and bone destruction is provided.

[0058] Furthermore, in one or more embodiments, the proposed framework can be applied to the creation of preoperative plans for initial and revision artificial joint replacement surgery.

[0059] Furthermore, as described herein, known biomechanical studies provide detailed information relating to either joint-level mechanics or fixation-level mechanics. However, musculoskeletal models used to predict the kinematics and loading of the tibiofemoral joint after TKA from whole-body kinematics and ground reaction forces assume that bone is rigid, which may hinder the evaluation of bone-implant interactions. Conversely, in connection with this disclosure, finite element models are used to evaluate the impact of joint loading on the micromotion and bone destruction risk of bone-implant interactions.

[0060] The integrated modeling method of this disclosure improves the evaluation of trade-offs between joint-level mechanics and fixation-level mechanics by combining studies on joint-level mechanics and fixation-level mechanics.

[0061] Therefore, this disclosure combines research on joint-level mechanics and fixation-level mechanics to identify the trade-offs between function and fixation in total knee arthroplasty. This disclosure focuses on the relationship between joint kinematics and bone-implant interaction and can be used to optimize fixation mechanics while maintaining appropriate joint mechanics (e.g., kinematics). Furthermore, this workflow is applicable to a wide range of clinically important issues in the biomechanics of total knee arthroplasty regarding how patient factors, surgical factors, and implant factors affect the function and long-term durability of total knee arthroplasty.

[0062] This disclosure describes a web-based system using a web browser, a custom application, a website server (data processing device 102), and a mobile computing device as an example, but the system 100 is not limited to a specific configuration. The system 100 can be configured such that the computing device 104 communicates with the data processing device 102 using any known communication and display method, such as a combination of a Windows viewer other than an internet browser and a local area network protocol such as Internetwork Packet Switching (IPX), and displays data received from the data processing device 102. Furthermore, any suitable operating system can be used on the computing device 104, such as WINDOWS, MAC OS, OSX®, LINUX, IOS, ANDROID, and any suitable PDA or other computer operating system.

[0063] As used herein, the terms “function” or “module” refer to hardware, firmware, or software combined with hardware and / or firmware that implements the functions described herein. From a hardware perspective, a module is a functional hardware unit designed to be used in conjunction with other components or modules. For example, a module may be implemented using individual electronic components or form part of an entire electronic circuit, such as an application-specific integrated circuit (ASIC). There are many other possibilities, and those skilled in the art will understand that a system can be implemented as a combination of hardware and software modules. From a software perspective, a module may be implemented as logic executed by a set of software instructions, which may have entry and exit points, written in a programming language such as Java, Lua, C, or C++. A software module may be compiled and linked into an executable program, installed in a dynamic-link library, or written in an interpreted programming language such as Perl or Python. It will be understood that a software module may be callable from other modules or from itself, and may be called in response to detected events or interrupts. Software instructions may be incorporated into firmware. Furthermore, the modules described herein can be implemented as software modules, but can also be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that can be combined with other modules or divided into submodules, regardless of their physical configuration or storage form.

[0064] The operations shown and described herein may be performed in a specific order, but this should not be understood as requiring that such operations be performed in a specific or sequential order, or that all of the operations shown be performed, in order to obtain the desired result. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated into a single software product or packaged into multiple software products.

[0065] The terms used herein are for the sole purpose of describing specific embodiments and are not intended to limit the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to also include the plural forms unless otherwise explicitly indicated in the context. The terms “comprises” and / or “comprising,” as used in this disclosure, express the presence of the features, integers, steps, operations, elements, and / or components mentioned, but it will be understood that they do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0066] It should be noted that in a claim, the use of ordinal numbers such as "first," "second," and "third" modifying elements of a claim does not, in itself, imply that an element of a claim has priority, superiority, or order over an element of another claim, or that the actions of the method are performed in a temporal order. Rather, it is simply used as a label to distinguish an element of a claim having a particular name from another element having the same name (except for the use of ordinal numbers).

[0067] Furthermore, the words and terms used herein are for illustrative purposes only and should not be considered limiting. In this specification, the use of “includes,” “equips,” “possesses,” “contains,” “includes,” and variations thereof means to include the items listed thereafter and their equivalents, as well as any additional items.

[0068] This disclosure describes specific embodiments of the subject matter described herein. Other embodiments are included in the following claims. For example, the operations described in the claims may be performed in a different order to obtain the desired results. As an example, the processes shown in the accompanying drawings do not necessarily require the specific order or sequence shown to obtain the desired results. In certain embodiments, multitasking and parallel processing may be advantageous.

Claims

1. At least one computing device processes surgical planning information representing details related to the patient's total knee replacement procedure to identify at least one of the implant type and the implant fixation process, The at least one computing device processes at least one image of the patient to determine bone density information related to the patient, The process of selecting a number of patients who have previously undergone total knee arthroplasty, including at least a portion of the details relating to the total knee arthroplasty procedure of the patient, by having at least one computing device process the determined bone density information and having at least one computing device process patient information representing at least demographics related to the patient, The at least one computing device processes outcome information representing the results of joint replacement procedures associated with each of the multiple patients to set a threshold representing the mechanical tolerance of the implant fixation, The at least one computing device processes at least one instruction to determine whether the determined bone density information related to the patient is above or below the set threshold, A computerization method comprising providing, by at least one computing device, information relating to a determination of whether the bone density information relating to the patient is above or below a set threshold.

2. The method according to claim 1, wherein the outcome information further represents successful joint replacement procedures and joint replacement procedures that failed due to mechanical reasons.

3. The method according to claim 1, further comprising setting the thresholds, the at least one computing device processing implant and fixation information representing implants, fixation, implant movement, and pain related to joint replacement procedures associated with each of the plurality of patients.

4. The method according to claim 1, further comprising using at least one computing device to compare the determined bone density information with information representing the bone density of a plurality of patients who have previously undergone total knee replacement surgery.

5. The method according to claim 1, wherein the automatically provided information is at least one of an alert displayed on a screen, an audible alert, and a vibration alert.

6. The method according to claim 1, wherein the automatically provided information includes recommendations regarding at least one of component selection, alignment, and fixing methods.

7. The method according to claim 1, wherein the automatically provided information is a command constituting a robotic surgical system.

8. The method according to claim 1, further comprising quantifying the interaction between the implant and bone by having at least one computing device provide the determined bone density information as input to a computational model.

9. The method according to claim 1, further comprising the at least one computing device processing the at least one image of the patient to convert Hounsfield units into bone density information.

10. The method according to claim 1, wherein the at least one image of the patient is a computerized (CT) scan.

11. When an instruction stored in a processor-readable medium is executed, The steps include: processing surgical planning information that represents details related to the patient's total knee arthroplasty procedure to identify at least one of the implant type and the implant fixation process; The steps include determining bone density information related to the patient by processing at least one image of the patient, The steps include: selecting a number of patients who have previously undergone total knee arthroplasty, including at least some of the details relating to the total knee arthroplasty procedure for the patient, by processing the determined bone density information and processing patient information that represents at least some of the demographics related to the patient; The steps include: setting a threshold representing the mechanical tolerance of the implant fixation by processing outcome information representing the results of the joint replacement procedure associated with each of the aforementioned patients; A step of determining whether the determined bone density information relating to the patient is above or below a set threshold by processing at least one command, A computerized system comprising at least one computing device configured to perform a step including: providing information relating to a determination of whether the bone density information relating to the patient is above or below a set threshold.

12. The system according to claim 11, wherein the outcome information further represents successful joint replacement procedures and joint replacement procedures that failed due to mechanical reasons.

13. The system according to claim 11, wherein setting the threshold further includes the at least one computing device processing implant and fixation information representing implants, fixation, implant movement, and pain related to joint replacement procedures associated with each of the plurality of patients.

14. The aforementioned at least one computing device further includes: The system according to claim 11, further configured to perform a step of comparing the determined bone density information with information representing the bone density of several patients who have previously undergone total knee replacement surgery.

15. The system according to claim 11, wherein the automatically provided information is at least one of an alert displayed on a screen, an audible alert, and a vibration alert.

16. The system according to claim 11, wherein the automatically provided information includes recommendations regarding at least one of component selection, alignment, and fixing methods.

17. The system according to claim 11, wherein the automatically provided information is a command constituting a robotic surgical system.

18. The aforementioned at least one computing device further includes: The system according to claim 11, further configured to perform a step of quantifying the interaction between the implant and bone by providing the determined bone density information as input to a calculation model.

19. The aforementioned at least one computing device further includes: The system according to claim 11, configured to perform a step further comprising the step of converting Hounsfield units into bone density information by processing the at least one image of the patient.

20. The system according to claim 11, wherein the at least one image of the patient is a computerized (CT) scan according to claim 1, and the outcome information further represents successful joint replacement procedures and joint replacement procedures that failed due to mechanical reasons.

Images