Systems and methods for spinal correction surgical planning

Information

  • Patent Grant
  • 11576727
  • Patent Number
    11,576,727
  • Date Filed
    Friday, March 19, 2021
    3 years ago
  • Date Issued
    Tuesday, February 14, 2023
    a year ago
Abstract
A system for surgical planning and assessment of spinal deformity correction is provided that has a spinal imaging system and a control unit. The spinal imaging system is configured to collect at least one digitized position of one or more vertebral bodies of a subject. The control unit is configured to receive the at least one digitized position, and calculate, based on the at least one digitized position, an optimized posture for the subject. The control unit is configured to receive one or more simulated spinal correction inputs, and based on the inputs and optimized posture, predict an optimal simulated postoperative surgical correction.
Description
FIELD

The present disclosure relates generally to spinal surgery, more specifically to systems and methods relating to the planning, predicting, performing, and assessing of spinal deformity correction and compensatory changes. Such devices as well as systems and methods for use therewith are described.


BACKGROUND

The spinal column is a highly complex system of bones and connective tissues that provide support for the body and protect the delicate spinal cord and nerves. The spinal column includes a series of vertebral bodies stack atop one another, each vertebral body including an inner or central portion of relatively weak cancellous bone and an outer portion of relatively strong cortical bone. Situated between each vertebral body is an intervertebral disc that cushions and dampens compressive forces exerted upon the spinal column. A vertebral canal containing the spinal cord is located behind the vertebral bodies. The spine has a natural curvature (i.e., lordosis in the lumbar and cervical regions and kyphosis in the thoracic region) such that the end plates of the upper and lower vertebrae are enclosed toward one another.


There are many types of spinal column disorders, including scoliosis (abnormal lateral curvature of the spine), excess kyphosis (abnormal forward curvature of the spine), excess lordosis (abnormal backward curvature of the spine), spondylolisthesis (forward displacement of one vertebra over another), and other disorders caused by abnormalities, disease, or trauma (such as ruptured or slipped discs, generative disc disease, fractured vertebrae, and the like).


Patients that suffer from such conditions often experience extreme and debilitating pain, as well as diminished nerve function. Posterior fixation for spinal fusions, decompression, deformity, and other reconstructions are performed to treat these patients. The aim of posterior fixation in lumbar, thoracic, and cervical procedures is to stabilize the spinal segments, correct multi-axis alignment, and aid in optimizing the long-term health of the spinal cord and nerves.


Spinal deformity is the result of structural change to the normal alignment of the spine and is usually due to at least one unstable motion segment. The definition and scope of spinal deformity, as well as treatment options, continues to evolve. Surgical objections for spinal deformity correction include curvature correction, prevention of further deformity, improvement or preservation of neurological function, and the restoration of sagittal and coronal balance. Sagittal plane alignment and parameters in cases of adult spinal deformity (ASD) are becoming increasingly recognized as correlative to health related quality of life score (HRQOL). In literature, there are significant correlations between HRQOL scores and radiographic parameters such as Sagittal Vertical Axis (SVA), Pelvic Tilt (PT) and mismatch between pelvic incidence and lumbar lordosis.


Spinal disorders, such as degenerative processes of the human spine, loss of disc height and lumbar kyphosis, result in a reduced HRQQL. The skeleton compensates for changes in the spine caused by these disorders to maintain balance and horizontal gaze of the subject. However, such compensation requires effort and energy from the subject and is correlated to a lower HRQQL score. Current surgical planning tools do not evaluate or include compensatory changes in a subject, leading to an undercorrection of a deformity in a patient that undergoes the surgical plan and procedure. Therefore, a need continues to exist for systems and methods that include compensatory changes as part of surgical planning.


SUMMARY

The needs described above, as well as others, are addressed by embodiments of a system for spinal correction surgical planning described in this disclosure (although it is to be understood that not all needs described above will necessarily be addressed by any one embodiment), as the system for spinal correction surgical planning of the present disclosure is separable into multiple pieces and can be used in methods, such as surgical planning methods. The systems of the present disclosure may be used, for example, in a method of increasing HRQQL in a subject.


In an aspect, a system for surgical planning and assessment of spinal deformity correction in a subject is provided. The system includes a spinal imaging system capable of collecting at least one digitized position, such as on a corner, of one or more vertebral bodies of the subject. In an embodiment, digitized positions are from two or more vertebral bodies. The system includes a control unit in communication with the spinal imaging system. The control unit is configured to receive the at least one digitized position of the one or more vertebral bodies. The control unit is configured to calculate, based on the at least one digitized position, an optimized posture for the subject. The calculation of the optimized posture of a subject may include processing a parametric study. The control unit is configured to receive one or more simulated spinal correction inputs, such as sagittal alignment, muscle recruitment criteria, or surgical procedure, such as intervertebral fusion. The control unit is configured to predict a simulated postoperative surgical correction based on the received one or more simulated spinal correction inputs and the received at least one digitized position of the one or more vertebral bodies. The control unit may be configured to determine, or suggest, a surgical plan based on the predicted simulated postoperative surgical correction. The prediction of simulated postoperative surgical correction may be based on one or more values selected from the group consisting of: knee flexion, pelvic retroversion, center of mass migration, ankle flexion, spinal compensation, and a combination thereof.


In some embodiments of the system, the control unit is configured to communicate the predicted simulated postoperative spinal correction to a user. The control unit may be configured to communicate, or output, a predicted simulated postoperative surgical correction, corresponding to a variance from the calculated optimized posture. The output value of less than 0 may represent a predicted undercorrection, and the output value of greater than 0 may represent an overcorrection. The at least one digitized position of the one or more vertebral bodies may be obtained from X-ray data, computed tomography imaging data, magnetic resonance imaging data, or biplanar X-ray data from the subject. These data may be taken from a patient who is in a lateral standing position.


In an embodiment of the system, the at least one digitized position is processed by the control unit to generate a musculoskeletal model of the subject. The processing of the at least one digitized position may include inverse-inverse dynamics modeling. The musculoskeletal model may include spinopelvic parameters, ligament parameters, joint kinematics, or any combination thereof. The spinopelvic parameters may include parameters selected from the group consisting of: pelvic tilt, sacral slope, pelvic incidence, sagittal vertical axis, lumbar lordosis, thoracic kyphosis, T1 pelvic angle, and combinations thereof. The musculoskeletal model may include muscle force data or muscle activation data. The control unit may be configured to compare the generated musculoskeletal model with predetermined musculoskeletal model data levels. Data from the generated musculoskeletal model, such as muscle force data or muscle activation data, may be communicated to a user. In some embodiments of the system, the control unit is configured to generate a sagittal curvature profile based on the received at least one digitized position of the one or more vertebral bodies. The control unit may be configured to modify the musculoskeletal model data to match the sagittal curvature profile. The musculoskeletal model data may be modified by scaling, adjusting positioning of the one or more vertebral bodies, morphing a simulated subject anatomy, or combinations thereof.


In an embodiment of the system, the simulated postoperative surgical correction includes hip compensation, knee joint compensation, or ankle joint compensation. The prediction of a simulated postoperative surgical correction may also include a prediction of trunk muscle force output and leg muscle force output. The trunk muscle force output may include an erector spinae output, multifidi output, an obliques output, semispinalis output, an abdominal muscles output, or any combination thereof. The leg muscle force output includes a soleus output, a gastrocnemius output, a hip and knee flexors output, a hip and knee extensors output, a gluteus maximus output, a gluteus minimus output, or any combination thereof.


In some embodiments of the system, the simulated postoperative surgical correction includes simulating an implant in the subject.


In another aspect, a system for surgical planning and assessment of spinal deformity correction in a subject includes a spinal imaging system capable of collecting at least one digitized position of one or more vertebral bodies of the subject. The system includes a control unit configured to receive the at least one digitized position of the one or more vertebral bodies of the subject, and calculate, based on morphing and scaling the at least one digitized position onto a model, an optimized posture for the subject.


In yet another aspect, a system for surgical planning and providing a personalized implant for a subject includes a spinal imaging system capable of collecting at least one digitized position of one or more vertebral bodies of the subject. The system includes a control unit in communication with the spinal imaging system. The control unit is configured to receive the at least one digitized position of the one or more vertebral bodies of the subject to create an initial musculoskeletal model. The control unit is configured to calculate, based on the initial musculoskeletal model, an optimized posture for the subject. The control unit is configured to generate a simulated implant to change the initial musculoskeletal model towards the calculated optimized posture; and communicate dimensional data of the simulated implant to a user. The system may further comprise a three dimension printer configured to create at least part of the simulated implant.


The above presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key or critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a side elevation view of a spine.



FIG. 2 illustrates a spine of a subject and an X-ray image of a subject.



FIG. 3 illustrates a spine of a subject.



FIGS. 4A-4C illustrate various configurations of a spine.



FIGS. 5A and 5B illustrate a model of a healthy spine and a kyphotic spine, respectively.



FIG. 6 illustrates a musculoskeletal model in an embodiment of the system.



FIGS. 7A-7C illustrate bones in a pelvic region of a subject.



FIG. 8 illustrates steps of generating a musculoskeletal model of a subject according to an embodiment of the system.



FIG. 9 illustrates steps of generating an output according to one embodiment of the system.



FIG. 10 illustrates steps of displaying results of a simulated surgical correction according to an embodiment of the system.



FIG. 11 illustrates steps of displaying results of a simulated surgical correction according to another embodiment of the system.



FIG. 12 illustrates an embodiment of the system.



FIG. 13 illustrates yet another embodiment of the system.



FIG. 14A illustrates steps for transmitting simulated implant data to an additive or subtractive manufacturing device according to an embodiment of the system.



FIG. 14B illustrates an embodiment of the system having an additive or subtractive manufacturing device.



FIG. 15 illustrates steps of inverse-inverse dynamics processing and optimization according to an embodiment of the system.



FIG. 16 illustrates a simulated implant according to an embodiment of the system.





DETAILED DESCRIPTION

Illustrative embodiments of a system for surgical planning and assessment of spinal deformity correction are described below. In the interest of clarity, not all features of an actual implementation are described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure. The system for surgical planning and assessment of spinal deformity correction in a subject and related systems and methods disclosed herein boast a variety of inventive features and components that warrant patent protection, both individually and in combination.


Values given here may be approximate (i.e., +/−20%, or 10%) such as to account for differences in surgical technique and patient-specific factors.


In one embodiment, a system 10 for surgical planning and assessment of spinal deformity correction in a subject 2 includes a spinal imaging system 10 capable, or configured, to collect at least one digitized position 14 of one or more vertebral bodies 4 of the subject 2, shown in FIG. 1. It will be appreciated that the present discussion may be applicable to other structures, such as skull bodies and limb joints. The vertebral bodies 4 may be, for example, cervical, thoracic, lumbar, sacrum, or coccyx. The system 12 includes a control unit 16 containing software configured to receive, or collect, the digitized position 14, as shown in, for example, FIG. 8. The at least one digitized position 14 may be any number of positions that correspond to any number of locations, respectively, on the one or more vertebral bodies 4. For example, there may be at least two positions, at least four positions, at least eight positions, at least sixteen positions, or any number of positions therebetween. The at least one digitized position 14 may correspond to specific locations on the one or more vertebral bodies 4. In one embodiment, the positions 14 correspond to a corner, or multiple corners, of the vertebral bodies 4, as shown in FIG. 2. The control unit 16 may also be configured to collect information of the vertebral bodies 4, such as bone density, fractures, etc. The digitized positions 14 may be extracted from the subject 2 when the subject 2 is in a standing, lateral position.


The control unit 16 may collect the digitized position 14 from any data source of the subject 2 that depicts the vertebral bodies 4 in sufficient detail, including but not limited to, an X-ray image, a computed tomography image, a magnetic resonance imaging image, or biplanar X-ray image of the subject 2. The control unit 16 may contain image recognition software whereby the control unit 16 digitizes data provided, such as an X-ray image, a computed tomography image, a magnetic resonance imaging image, or biplanar X-ray image of the subject 2, and the control unit 16 may select digitized positions 14 based on output from the image recognition software. The image recognition software, by way of example, may process the image and identify and transmit the positions 14, such as the corners of the one or more vertebral bodies 4. In some embodiments, this processing and identification is automatic, while in other embodiments, a user manually selects or verifies the positions 14 from data provided to the control unit 16 such that the control unit 16 receives the digitized positions 14 from the user. In yet another embodiment, the digitized positions 14 are received digitally from a digital imaging component, such as a digital radiography system. The digitized positions 14 may be displayed using medical modeling system 15, such as the archiving and communication system (PACS), shown in FIG. 6.


In an embodiment of the system 10, the control unit 16 is configured to calculate, or determine, based on the at least one digitized position 14, an optimized posture 18 of the subject 2. As used herein, “optimized posture” refers to the posture that would be the desired, or ideal, clinical outcome for the subject 2, as for example, determined by a surgeon seeking to perform a spinal correction surgery on the subject 2 who is in need thereof. The control unit 16 may be configured to calculate the optimized posture 18 by parametric processing. In parametric processing, data regarding the at least one digitized position 14 may be compared to one or more predetermined optimized anatomical posture models 20. The predetermined optimized anatomical posture models 20 may not be patient-specific. The predetermined model 20 selected may be, for example, the predetermined model 20 that most closely corresponds to the anatomical characteristics of the subject 2. By way of example, the control unit 16 may be configured to include, or store, predetermined models 20 for subjects 2 of varying ages, gender and medical conditions (e.g., lordosis, kyphosis, scoliosis), and may select the predetermined model 20 most suitable for the subject 2. The at least one anatomical digitized positions 14 may be morphed, scaled, or adjusted, either manually or automatically, onto corresponding points 21 on the predetermined model 20. As discussed later, the predetermined model 20 may contain logic, inputs, and parameters for the predicting steps when determining optimized posture and/or simulated correction 24.


Based on the received at least one digitized position 14 of the one or more vertebral bodies 4, the control unit 16 is configured to predict, or determine, a simulated postoperative surgical correction 24 (i.e., predict how a surgical correction, such as a posterior lumbar interbody fusion or anterior lumbar interbody fusion, will affect the posture of the subject 2). The control unit 16 may be configured to determine, for example, the simulated postoperative surgical correction 24 that would result in, or close to, the optimized posture 18 for the subject 2. Based on the simulated postoperative surgical correction 24, the control unit 16 may be configured to determine, and display to a surgeon, a recommended surgical plan 26 to implement the predicted simulated postoperative surgical correction 24. The recommended surgical plan 26 may include, by way of example, information regarding surgical procedure, surgical approach, surgical technique, surgical instrument, and implant.


The control unit 16 may communicate the predicted simulated postoperative spinal correction 24, and/or recommended surgical plan 26, to the user. By way of example and as shown in FIG. 9, the control unit 16 may be configured to communicate, or output, the predicted simulated postoperative surgical correction 24, corresponding to a variance from the calculated optimized posture 18. The communicated predicted simulated postoperative spinal correction 24, and/or recommended surgical plan 26 may be transmitted as an output 28. By way of example, the output 28 may be an image representation, a graphical display, or a numerical value.


As illustrated in FIG. 10, in embodiments having output 28 as a numerical value, the output value of less than 0 may represent a predicted undercorrection 58 as compared to the optimized posture 18 and the output value of greater than 0 may represent an overcorrection 62 as compared to the optimized posture 18. A value of 0 may represent a desired, or optimal, spinal correction 60 that achieves the optimized posture 18 in the subject 2. Thus, the value of the output 28 may correspond to the variance of the predicted simulated postoperative surgical correction 24 with the optimized posture 18, with a higher degree positively correlating with higher variance. As used herein, “undercorrection” means that the predicted simulated postoperative surgical correction 24 is not able to fully correct the medical condition being corrected of the subject 2, and “overcorrection” means that that the predicted simulated postoperative surgical correction 24 overly corrects the medical condition being corrected of the subject 2. The value of the output 28 may correspond to any, or any combination, of measurements such as, a value of muscle activation in a patient, a value of kyphosis, a value of lordosis, and a value of Cobb angle.


As described in FIG. 11, if the simulated postoperative surgical correction 24 results in a significant overcorrection or an undercorrection, the system 10 may display the output 28 in red, such as a red number or a red symbol. On the other hand, if the simulated postoperative surgical correction 24 results in an output 28 equal, or substantially equal, to the corresponding value in the optimized posture 18, the system 10 may display an output in green, such as a green number or a green symbol. The control unit 16 may be configured to transmit the outputs 28. Thus, the user (i.e., surgeon) can iteratively change an input plan or input parameters until the goal, such as optimal posture, is achieved.


By way of example, in the case of the subject 2 having Scoliosis, an X-ray image of the subject's 2 spine may be received by the control unit 16. The control unit 16 may automatically process the X-ray image to determine digitized positions 14, such as on points corresponding to corners of vertebrae bodies 4 of the subject 2. Using the digitized positions 14, the control unit 16 may calculate the optimized posture 18 of the subject 2. The control unit 16 may morph and scale the digitized positions 14 onto a predetermined model 20 to create a simulated model 32 of the subject's 2 spine. The optimized posture 18 may have a spine with a Cobb angle of between 0 and 10 degrees, 2 and 8 degrees, or 2 and 6 degrees, or any combination of those values. The Scoliosis subject 2 may have a spinal Cobb of greater than 10 degrees, greater than 15 degrees, greater than 20 degrees, greater than 40 degrees, greater than 50 degrees, or greater than 60 degrees. The control unit 16 may communicate the Cobb value of the optimized posture 18 to the user. The control unit 16 may be configured to receive an input surgical correction 30, such as spinal fusion of specific vertebrae, to calculate the predicted simulated postoperative spinal correction 24, and/or recommended surgical plan 26. In some embodiments of the system 10, multiple plans 26 are recommended. If the optimized posture 18 has a Cobb angle of 0, and the simulated postoperative spinal correction 24 has a Cobb angle of 0, the control unit 16 would communicate to the user that the input surgical correction 30 achieves the optimized posture 18, such as by returning a value of 0. In contrast, if the optimized posture 18 has a Cobb angle of 0, and the simulated postoperative spinal correction 24 has a Cobb angle of −5 or +5, the control unit 16 would communicate to the user that the input surgical correction 30 results in an undercorrection of −5 or overcorrection of +5, respectively. Of course, the values that represent an undercorrection and overcorrection, such as degree and positivity, may be varied. In some embodiments, the control unit 16 may calculate and determine the predicted simulated postoperative surgical correction 24 to achieve the Cobb angle of 0 and determine a recommended surgical plan 26 that would result in the subject 2 having a Cobb angle of 0. The control unit 16 may be configured to communicate the simulated correction 24 and/or plan 26 to the user.


As can be appreciated, the system 10 may have numerous advantages. For example, the system 10 may provide the user with the optimized posture 18 of the subject 2. Using the optimized posture 18, the user may determine the optimal surgical plan 26 to achieve the optimized posture of the subject 2. In embodiments of the system 10 where the control unit 16 is configured to receive an input surgical correction 30 and output a simulated correction 24, the system 10 enables the user to remove the uncertainty, or “guesswork,” as to the clinical outcome of a surgical correction. Advantageously, this feature of the system 10 would provide the user with information, such as whether the proposed surgical correction would result in an undercorrection of the medical condition being treated, that would allow the user to choose the surgical correction that would result in an efficacious clinical outcome for the subject 2 that avoids undercorrection or overcorrection. In embodiments where the system 10 predicts optimal correction 24 and/or plan 26 and communicates correction 24 and/or plan 26 to the user, the system 10 provides the user with an efficacious surgical correction that a surgeon can implement that avoids undercorrection or overcorrection. Indeed, the described system 10 is a new technological tool for improving surgical outcomes in subjects 2, particularly human subjects in need of and who receive spinal correction surgery.


The control unit 16 is configured to process various values and factors, as well as contain various logics, to calculate optimized posture 18 and simulated postoperative surgical correction 24. For example, the control unit 16 may be configured to receive and process one or more compensation values 56 selected from the group consisting of: knee flexion, pelvic movement, ankle flexion, shoulder movement, lumbar movement, thoracic movement, cervical movement, spinal compensation, including ribs and neck, and a combination thereof, as shown in FIG. 5B. The control unit 16 may also be configured to receive and process center of mass migration 57. Knee flexion refers to joint angle between the bones of the limb at the knee joint. Knee flexion values may be, for example, between minus 10 and 150 degrees. Pelvic movement may include pelvic retroversion, pelvic anteversion, and pelvic tilt. Pelvic retroversion may be, for example, less than 50 degrees, less than 30 degrees, less than 25 degrees, less than 20 degrees, less than 15 degrees, less than 10 degrees, less than 5 degrees, or any range thereof. Center of mass migration 57, as shown in FIG. 3, refers to the point on the ground over which the mass of the subject 2 is centered, typically the center of mass migrations falls between the ankles of the subject 2. Ankle flexion refers to a joint angle between the bones of the limb at the ankle joint. These values may be taken from the subject 2 who is in a suitable position, such as standing, supine, and prone. Processing compensation values 56 and mass migration 57 is a technical problem much more difficult than that of processing a rigid skeleton with no compensation (FIG. 5A) that is overcome by the practicing of the present disclosure.



FIG. 4A illustrates a non-degenerated spine with the spine in balance. FIG. 4B illustrates a generated spine and retroversion of the pelvis to compensate for the degeneration. FIG. 1C depicts a generated spine and flexion of the knee to compensate for such degeneration. Beneficially, the disclosed system and methods herein can account for these compensations, among other things, to produce a realistic and accurate model for surgical planning.


As shown in FIG. 12, the control unit 16 may be configured to generate, or create, a musculoskeletal model 32 of the subject 2. The control unit 16 may be configured to compare the model 32 with the predetermined model 20 for the control unit's 16 calculation of the optimized posture 18. The control unit 16 may receive the digitized positions 14 to generate the musculoskeletal model 32 of the subject 2. The control unit 16 may also receive inputs 22, such as spinopelvic parameters, ligament parameters, joint kinematics, sagittal alignment measurements, spinal instability, and muscle recruitment criteria, and intervertebral fusion. As shown in FIGS. 7A-7C, the spinopelvic parameters may include parameters such as pelvic tilt (PT), sacral slope (SS), pelvic incidence (PI), sagittal vertical axis (SVA), lumbar lordosis, thoracic kyphosis, T1 pelvic angle, and combinations thereof. Further, the control unit 16 may input or use global alignment parameters such as global sagittal axis, three-dimensional parameters such as rotation and scoliosis, and cervical parameters. In some embodiments of the system 10, the spinopelvic parameters are used to assess, or determine, how far a subject is from a normal or optimum posture. The model 32 may also include muscle 36 force data or muscle activation data 38. The control unit 16 may be configured to use the inputs 22 to generate the musculoskeletal model 32 of the subject 2 and optimized posture 18 of the subject 2, which can include any, or all, of these parameters and inputs that reflect their respective values, or age-adjusted respective values, on the model 32. The control unit 16 may be configured to receive these inputs 22 manually or automatically. The control unit 16 may use these inputs 22 to compare and process in comparison to corresponding values on a predetermined model 20 in calculating optimized posture 18 and simulated surgical correction 24. Models 20, 32 may each have, or exclude, any parameter, logic, algorithm, input, or output discussed herein.


The control unit 16 may process the digitized positions 14 by inverse-inverse dynamics modeling (FIG. 15). Advantageously, inverse-inverse dynamics modeling enables the system 10 to create a fluid model as opposed to a rigid model. Indeed, inverse-inverse dynamics modeling solves the technical problem of simulating how fluid joints and connectors (e.g., inputs 22) of subjects 2 affect a corrective surgery, particularly in instances where a rigid model would generate a model that would result in an undercorrection if implemented in a surgical correction. The control unit 16 may contain anatomical modeling software capable of, or configured to, simulate kinematics and muscular and joint loads in the full body for typical activities of a subject 2 and for fundamental human body motions. An example of such software is ANYBODY MODELING SYSTEM™ software, available from ANYBODY TECHNOLOGY™ of Aalborg, Denmark, configured to execute the inverse-inverse dynamics modeling. Moreover, the inverse-inverse dynamics model improves the functioning of control unit 16, as inverse-inverse dynamics enables control unit 16 to more accurately simulate the simulated surgical correction's interactions with anatomical properties of subject 2, especially properties specific to that subject 2, such as compensation, muscle elasticity, and joint elasticity.


As illustrated in FIG. 13, the control unit 16 may be configured to generate a sagittal curvature profile 34 based on the received digitized positions 14 and inputs 22. The profile 34 may be both a sagittal and coronal. The control unit 16 may morph (i.e., modify) the model 32 to match the profile 34. The musculoskeletal model data may be modified by scaling, adjusting positioning of the one or more vertebral bodies 4, morphing the simulated subject anatomical model 32, or combinations thereof.


Some, or all, of the inputs 22 may be predetermined, or manually or automatically received. The control unit 16 may be configured to apply logic parameters 36, such as that a subject 2 maintains a center of mass over the ankles; maintains a constant horizontal gaze; stands in a posture where postural muscle energy is minimized; has an arm position matching the patient during imaging (i.e., scaling); has no coronal plane deformity, or any combination of these logic parameters 36.


The control unit 16 may be configured to compare the calculated, or generated, musculoskeletal model 32 with predetermined musculoskeletal model data levels. Data from the calculated musculoskeletal model 32, such as muscle force data 36 or muscle activation data 38, may be used to calculate the simulated surgical correction 24 and communicated to a user through a display 52.


The control unit 16 may receive and process compensation values 56. In some embodiments, these values may be stored on the control unit 16. The control unit 16 may calculate compensation data 38, for example, hip compensation, ankle joint compensation, knee joint compensation, shoulder compensation, lumbar compensation, thoracic compensation, cervical compensation, or spinal compensation, including ribs and neck, to generate the model 32. Including compensation values 56 and/or compensation data 38 is particularly useful in some embodiments of the system 10, as the compensation values 56 and compensation data 38 considers that joints compensate for spinal changes, such as a degenerated spine. Thus, by including the values and data 56, 38, model 32 may be more accurately the subject's anatomy and compensation. The control unit 16 may also store predetermined compensation data 38 that is associated with the predetermined model 20.


The control unit 16 may also be configured to include a prediction of trunk muscle force 40 output and leg muscle force output 42 in the prediction of the simulated postoperative surgical correction 24. The trunk muscle force output may include cervical output, an erector spinae output, multifidi output, an obliques output, semispinalis output, an abdominal muscles output, or any combination thereof. The leg muscle force output includes a soleus output, a gastrocnemius output, a hip and knee flexors output, a hip and knee extensors output, a gluteus maximus output, a gluteus minimus output, or any combination thereof. These outputs 42, 44 may be communicated to a user through the display 52.


As shown in FIG. 14A, in some embodiments of the system 10, the simulation of the postoperative surgical correction 24 includes simulating an implant 46 (FIG. 16) in the simulated model 32 of the subject 2. For example, a user of the system 10 may select, or design using engineering software, a simulated implant 46 to use in conjunction with the simulated postoperative surgical correction 24. The control unit 16 may be configured to receive input from the user for the location, orientation, type, size, and profile of the implant 46. In some embodiments of the system 10, the control unit 16 is configured to determine the simulated implant 46 that would achieve optimal posture 18 in the simulated corrective surgery 24. The determination may include the dimensions, location, orientation, type, size, and profile of the implant 46.


As illustrated in FIG. 14B, the system 10 may include a three dimensional printer (i.e., an additive manufacturing device or a subtractive manufacturing device) 48 in communication with the control unit 16. The three dimensional printer 48 may be configured to create, or partially create, the determined implant 46. Advantageously, this feature of the described disclosure allows for personalized surgical implants that are optimized for clinical benefit in the subject 2 to achieve optimized posture 18. The control unit 16 may be configured to transmit digital data 50 about the implant 46 for the printer 48 to manufacture the implant 46. The implant 46 may be designed on design software executed by the control unit 16 to achieve a desired structure and exported, for example as a .STL file, for preparation to be built with the three dimensional printer 48. The implant 46 may be designed to have a profile 49 to custom fit the morphology of vertebral body endplates of the subject 2, which may vary from subject to subject. The implant manufactured from simulated implant 46 may be constructed of any number, including multiple, suitable biocompatible material, such as titanium, titanium-alloy or stainless steel, surgical steel, or non-metallic compounds such as polymers.


In another aspect, a system 10 for surgical planning and assessment of spinal deformity correction in a subject 2 includes a spinal imaging device capable of collecting and transmitting to a control unit 16 at least one digitized position 14 of one or more vertebral bodies 4 of the subject 2. The control unit 16 is may be configured to receive the at least one digitized position 14 of the one or more vertebral bodies 4 of the subject 2, and calculate, based on morphing and scaling the at least one digitized position 14 onto a predetermined model 20 to form a simulated model 32, an optimized posture 18 for the subject 2.


The control unit 16 may be configured to execute software including optimization algorithms that tailor the profile of the implant 46 based upon loading conditions imparted upon the implant 46, including: compression, shear, and torsion. The control unit 16 may include optimization algorithms that may be executed in order to produce a low-density, material efficient implant 46. This is accomplished by applying multiple, clinically-relevant, loading conditions to the implant 46 in the software program and allowing a finite element solver to optimize and refine, for example, a body lattice structure 47 of the implant 46.


The system 10 may include a display 52, such as a monitor, in communication with the control unit 16. The display 52 may be capable of receiving input from the user in addition to communicating feedback information to the user. By way of example (though it is not a necessity), a graphical user interface 54 (GUI) is utilized to enter data directly from the screen display 52.


It is to be understood that any given elements of the disclosed embodiments of the invention may be embodied in a single structure, a single step, a single substance, or the like. Similarly, a given element of the disclosed embodiment may be embodied in multiple structures, steps, substances, or the like.


The foregoing description illustrates and describes the processes, machines, manufactures, compositions of matter, and other teachings of the present disclosure. Additionally, the disclosure shows and describes only certain embodiments of the processes, machines, manufactures, compositions of matter, and other teachings disclosed, but as mentioned above, it is to be understood that the teachings of the present disclosure are capable of use in various other combinations, modifications, and environments and are capable of changes or modifications within the scope of the teachings as expressed herein, commensurate with the skill and/or knowledge of a person having ordinary skill in the relevant art. The embodiments described hereinabove are further intended to explain certain best modes known of practicing the processes, machines, manufactures, compositions of matter, and other teachings of the present disclosure and to enable others skilled in the art to utilize the teachings of the present disclosure in such, or other, embodiments and with the various modifications required by the particular applications or uses. Accordingly, the processes, machines, manufactures, compositions of matter, and other teachings of the present disclosure are not intended to limit the exact embodiments and examples disclosed herein. Any section headings herein are provided only for consistency with the suggestions of 37 C.F.R. § 1.77 or otherwise to provide organizational queues. These headings shall not limit or characterize the invention(s) set forth herein.

Claims
  • 1. A method for surgical planning and assessment of spinal deformity correction in a subject, the method comprising: obtaining a set of anatomical positions of a subject, the set of anatomical positions including anatomical positions of at least two vertebrae of the subject;determining a model of the subject based on the set of anatomical positions, wherein the model defines a pelvic tilt value, a pelvic incidence value, a sagittal vertical axis value, and a lumbar lordosis value;receiving one or more simulated spinal correction inputs corresponding to a surgical procedure;predicting a simulated postoperative surgical correction based on the received one or more simulated spinal correction inputs and the model;determining a surgical plan based on the predicted simulated postoperative surgical correction; andproviding the determined surgical plan via a display.
  • 2. The method of claim 1, wherein the model is a musculoskeletal model.
  • 3. The method of claim 1, wherein determining the model comprises using inverse-inverse dynamics modeling.
  • 4. The method of claim 1, further comprising: generating a sagittal curvature profile based on the anatomical positions of at least two vertebrae of the subject; andmodifying the model to match the sagittal curvature profile, including: scaling, adjusting, or positioning portions of the model corresponding to the at least two vertebrae of the subject.
  • 5. The method of claim 1, wherein the prediction of the simulated postoperative surgical correction comprises a prediction of simulated anterior lumbar interbody fusion surgery.
  • 6. The method of claim 1, wherein obtaining the set of anatomical positions includes applying image recognition software to an anatomical image of the subject in a standing lateral position.
  • 7. The method of claim 1, wherein predicting the simulated postoperative surgical correction is based on one or more values associated with knee flexion, ankle flexion, pelvic retroversion, or spinal compensation.
  • 8. The method of claim 1, wherein the one or more simulated spinal correction inputs includes at least one of sagittal alignment and muscle recruitment criteria.
  • 9. The method of claim 1, wherein the simulated postoperative surgical correction includes at least one of hip compensation, knee joint compensation, and ankle joint compensation.
  • 10. The method of claim 1, further comprising: outputting a value, based on the predicted simulated postoperative surgical correction, corresponding to a variance from an optimal posture.
  • 11. The method of claim 1, wherein the simulated postoperative surgical correction is a simulated implant in the subject.
  • 12. The method of claim 1, further comprising: providing a classification of the surgical plan as representing an overcorrection or an undercorrection.
  • 13. The method of claim 1, further comprising: receiving a modification of the surgical plan;modifying the surgical plan based on the modification to form a modified surgical plan; andproviding the modified surgical plan via the display.
  • 14. The method of claim 1, further comprising: providing a classification of the surgical plan as representing an overcorrection or an undercorrection.
  • 15. A method comprising: determining a model of a subject based on a position of two or more vertebral bodies of the subject in a standing lateral position, wherein the model includes spinopelvic parameters, ligament parameters, and joint kinematics;calculating an optimized posture for the subject based on morphing and scaling the determined model of the subject;predicting a simulated postoperative surgical correction that maintains a horizontal gaze based on the calculated optimized posture for the subject; anddetermining a surgical plan based on the predicted simulated postoperative surgical correction and communicating the determined surgical plan via a display.
  • 16. The method of claim 15, wherein the model is a musculoskeletal model.
  • 17. The method of claim 15, wherein determining the model comprises using inverse-inverse dynamics modeling.
  • 18. The method of claim 15, further comprising: obtaining an image of the subject that is an X-ray image, a computed tomography image, a magnetic resonance imaging image, or a biplanar X-ray image; andobtaining a set of anatomical positions of the subject, the set of anatomical positions including the position of the two or more vertebral bodies, wherein the obtaining includes: applying image recognition software to the image of the subject.
  • 19. A method comprising: determining a model of a subject based on at least one position of one or more vertebral bodies of the subject, wherein the model defines a pelvic tilt value, a pelvic incidence value, a sagittal vertical axis value, and a lumbar lordosis value;receiving one or more simulated spinal correction inputs corresponding to a surgical procedure;predicting a simulated postoperative surgical correction that maintains a center of mass over the subject's ankles based on the received one or more simulated spinal correction inputs;determining a surgical plan based on the predicted simulated postoperative surgical correction;communicating the determined surgical plan via a display; andcommunicating the predicted simulated postoperative spinal correction via the display.
  • 20. The method of claim 19, further comprising: generating a sagittal curvature profile based on the at least one position; andmodifying the model to match the sagittal curvature profile, wherein modifying of the model comprises at least one of scaling, adjusting, and positioning of the one or more vertebral bodies.
CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S. Ser. No. 16/582,760, filed Sep. 25, 2019, which is a continuation of U.S. Ser. No. 15/448,119 (now U.S. Pat. No. 10,463,433), filed on Mar. 2, 2017, which claims the benefit of the priority date from U.S. 62/302,725, filed on Mar. 2, 2016, the entire contents of which are hereby expressly incorporated by reference into this disclosure as if set forth fully herein.

US Referenced Citations (384)
Number Name Date Kind
2693798 Haboush Nov 1954 A
3866458 Wagner Feb 1975 A
4409968 Drummond Oct 1983 A
4411259 Drummond Oct 1983 A
4474046 Cook Oct 1984 A
4653481 Howland et al. Mar 1987 A
4773402 Asher Sep 1988 A
4887595 Heinig et al. Dec 1989 A
5099846 Hardy Mar 1992 A
5113685 Asher et al. May 1992 A
5161404 Hayes Nov 1992 A
5239716 Fisk Aug 1993 A
5271382 Chikama Dec 1993 A
5290289 Sanders et al. Mar 1994 A
5389099 Hartmeister et al. Feb 1995 A
5490409 Weber Feb 1996 A
5548985 Yapp Aug 1996 A
5564302 Watrous Oct 1996 A
5591165 Jackson Jan 1997 A
5658286 Sava Aug 1997 A
5672175 Martin Sep 1997 A
5704937 Martin Jan 1998 A
5740802 Nafis et al. Apr 1998 A
5765561 Chen et al. Jun 1998 A
5799055 Peshkin et al. Aug 1998 A
5819571 Johnson Oct 1998 A
5819580 Gauthier Oct 1998 A
5880976 DiGioia III et al. Mar 1999 A
D415665 Nordell, II et al. Oct 1999 S
6006581 Holmes Dec 1999 A
6015409 Jackson Jan 2000 A
6024759 Nuss et al. Feb 2000 A
6035691 Lin et al. Mar 2000 A
6128944 Haynes Oct 2000 A
6205411 DiGioia, III et al. Mar 2001 B1
6226548 Foley May 2001 B1
6264658 Lee et al. Jul 2001 B1
6285902 Kienzle, III Sep 2001 B1
6327491 Franklin et al. Dec 2001 B1
6332780 Traxel et al. Dec 2001 B1
6529765 Franck Mar 2003 B1
6592585 Lee et al. Jul 2003 B2
6596008 Kambin Jul 2003 B1
6644087 Ralph et al. Nov 2003 B1
6701174 Krause Mar 2004 B1
6711432 Krause Mar 2004 B1
7346382 McIntyre Mar 2008 B2
7454939 Garner et al. Nov 2008 B2
7488331 Abdelgany Feb 2009 B2
7634306 Sarin et al. Dec 2009 B2
RE42226 Foley et al. Mar 2011 E
7957831 Isaacs Jun 2011 B2
8101116 Lindh, Sr. et al. Jan 2012 B2
8126736 Anderson et al. Feb 2012 B2
8177843 Schalliol May 2012 B2
8235998 Miller et al. Aug 2012 B2
8255045 Gharib et al. Aug 2012 B2
8298242 Justis et al. Oct 2012 B2
8374673 Adcox et al. Feb 2013 B2
8442621 Gorek et al. May 2013 B2
8459090 Wilcox et al. Jun 2013 B2
8506603 McClintock et al. Aug 2013 B2
8549888 Isaacs Oct 2013 B2
8607603 Justis et al. Dec 2013 B2
8668699 Thomas et al. Mar 2014 B2
8714427 McClintock et al. May 2014 B2
8744826 Skalli et al. Jun 2014 B2
8753346 Suarez et al. Jun 2014 B2
8770006 Harper Jul 2014 B2
8831324 Penenberg Sep 2014 B2
8885899 Illes et al. Nov 2014 B2
8951258 Peultier Feb 2015 B2
8983813 Miles et al. Mar 2015 B2
8992542 Hagag et al. Mar 2015 B2
9119670 Yang et al. Sep 2015 B2
9129054 Nawana et al. Sep 2015 B2
9204937 Edelhauser et al. Dec 2015 B2
9211145 Pereiro de Lamo et al. Dec 2015 B2
9233001 Miles et al. Jan 2016 B2
9248002 McCarthy Feb 2016 B2
9320604 Miles et al. Apr 2016 B2
9408698 Miles et al. Aug 2016 B2
9452050 Miles et al. Sep 2016 B2
9572682 Aghazadeh Feb 2017 B2
9597157 Hagag et al. Mar 2017 B2
9662228 McCarthy May 2017 B2
9700292 Nawana et al. Jul 2017 B2
9724167 Ziaei et al. Aug 2017 B2
9757072 Urbalejo Sep 2017 B1
9785246 Isaacs Oct 2017 B2
9861446 Lang Jan 2018 B2
9877847 Bettenga Jan 2018 B2
9962166 Sachs et al. May 2018 B1
9968408 Casey May 2018 B1
10139920 Isaacs Nov 2018 B2
10188480 Scholl Jan 2019 B2
10420480 Schermerhorn Sep 2019 B1
10444855 Isaacs Oct 2019 B2
10463433 Turner Nov 2019 B2
10507060 Casey Dec 2019 B2
10507061 Casey Dec 2019 B2
10684697 Isaacs Jun 2020 B2
10695099 Scholl Jun 2020 B2
10709509 Scholl Jul 2020 B2
10987169 Turner Apr 2021 B2
11207132 Isaacs Dec 2021 B2
11207136 Casey Dec 2021 B2
11229493 Finley Jan 2022 B2
11231787 Isaacs Jan 2022 B2
11376045 Scholl Jul 2022 B2
20020107573 Steinberg Aug 2002 A1
20030055435 Barrick Mar 2003 A1
20030055502 Lang Mar 2003 A1
20030060824 Viartetal Mar 2003 A1
20040044295 Reinert et al. Mar 2004 A1
20040068187 Krause Apr 2004 A1
20040087962 Gorek May 2004 A1
20040097952 Sarin May 2004 A1
20040144149 Strippgen et al. Jul 2004 A1
20040147927 Tsougarakis Jul 2004 A1
20040152972 Hunter Aug 2004 A1
20050043660 Stark et al. Feb 2005 A1
20050054917 Kitson Mar 2005 A1
20050119593 Gallard et al. Jun 2005 A1
20050192575 Pacheco Sep 2005 A1
20050203511 Wilson-MacDonald et al. Sep 2005 A1
20050245817 Clayton Nov 2005 A1
20050262911 Dankowicz et al. Dec 2005 A1
20050288809 Spaeth Dec 2005 A1
20060015030 Poulin et al. Jan 2006 A1
20060082015 Happonen et al. Apr 2006 A1
20060150698 Garner et al. Jul 2006 A1
20060150699 Garner et al. Jul 2006 A1
20060212158 Miller Sep 2006 A1
20060235338 Pacheco Oct 2006 A1
20060235427 Thomas et al. Oct 2006 A1
20060264973 Abdelgany Nov 2006 A1
20070073137 Schoenefeld Mar 2007 A1
20070093689 Steinberg Apr 2007 A1
20070118055 McCombs May 2007 A1
20070172797 Hada Jul 2007 A1
20070174769 Nycz Jul 2007 A1
20070227216 Schalliol Oct 2007 A1
20070233079 Fallin et al. Oct 2007 A1
20070269544 Erickson et al. Nov 2007 A1
20070288064 Butson et al. Dec 2007 A1
20080009945 Pacheco Jan 2008 A1
20080039717 Frigg Feb 2008 A1
20080103500 Chao et al. May 2008 A1
20080161680 von Jako Jul 2008 A1
20080177203 von Jako Jul 2008 A1
20080212858 Boese et al. Sep 2008 A1
20080262812 Arata et al. Oct 2008 A1
20080269596 Revie Oct 2008 A1
20080319275 Chiu Dec 2008 A1
20090018808 Bronstein Jan 2009 A1
20090024164 Neubardt Jan 2009 A1
20090099605 Fallin et al. Apr 2009 A1
20090118714 Teodorescu May 2009 A1
20090132050 Holm May 2009 A1
20090138020 Park et al. May 2009 A1
20090157083 Park Jun 2009 A1
20090222020 Schmuck et al. Sep 2009 A1
20090226055 Dankowicz et al. Sep 2009 A1
20090226068 Fitz Sep 2009 A1
20090249851 Isaacs Oct 2009 A1
20090254097 Isaacs Oct 2009 A1
20090254326 Isaacs Oct 2009 A1
20090276045 Lang Nov 2009 A1
20090287271 Blum et al. Nov 2009 A1
20090299477 Clayton et al. Dec 2009 A1
20100030232 Zehavi et al. Feb 2010 A1
20100042105 Park et al. Feb 2010 A1
20100042154 Biedermann et al. Feb 2010 A1
20100076563 Otto et al. Mar 2010 A1
20100100011 Roche Apr 2010 A1
20100101295 Miller et al. Apr 2010 A1
20100111631 Trieu et al. May 2010 A1
20100177948 Le Bras Jul 2010 A1
20100183201 Schwab et al. Jul 2010 A1
20100191071 Anderson Jul 2010 A1
20100191100 Anderson Jul 2010 A1
20100217109 Belcher Aug 2010 A1
20100268119 Morrison Oct 2010 A1
20100292963 Schroeder Nov 2010 A1
20110040340 Miller et al. Feb 2011 A1
20110054870 Dariush et al. Mar 2011 A1
20110066193 Lang Mar 2011 A1
20110071802 Bojarski Mar 2011 A1
20110084108 McClintock et al. Apr 2011 A1
20110093108 Ashby et al. Apr 2011 A1
20110121049 Malinouskas May 2011 A1
20110245871 Williams Oct 2011 A1
20110253760 McClintock et al. Oct 2011 A1
20110265538 Trieu et al. Nov 2011 A1
20110270262 Justis et al. Nov 2011 A1
20110295378 Bojarski et al. Dec 2011 A1
20110305379 Mahfouz Dec 2011 A1
20110319745 Frey Dec 2011 A1
20120010710 Frigg Jan 2012 A1
20120014580 Blum Jan 2012 A1
20120035507 George et al. Feb 2012 A1
20120041562 Shachar et al. Feb 2012 A1
20120047980 Harper Mar 2012 A1
20120116203 Vancraen May 2012 A1
20120141034 Iannotti et al. Jun 2012 A1
20120186411 Lodahi et al. Jul 2012 A1
20120230573 Ito et al. Sep 2012 A1
20120247173 Paris et al. Oct 2012 A1
20120265268 Blum et al. Oct 2012 A1
20120274631 Friedland Nov 2012 A1
20120289965 Gelaude et al. Nov 2012 A1
20130053854 Schoenefeld Feb 2013 A1
20130060130 Park et al. Mar 2013 A1
20130072980 Biedermann et al. Mar 2013 A1
20130072982 Simonson Mar 2013 A1
20130091921 Wilcox et al. Apr 2013 A1
20130096625 McClintock et al. Apr 2013 A1
20130113791 Isaacs May 2013 A1
20130123850 Schoenefeld May 2013 A1
20130131480 Ruhl et al. May 2013 A1
20130131486 Copf et al. May 2013 A1
20130144342 Strauss et al. Jun 2013 A1
20130173240 Koell et al. Jul 2013 A1
20130218163 Frey Aug 2013 A1
20130296954 Skaggs et al. Nov 2013 A1
20130303883 Zehavi et al. Nov 2013 A1
20130304217 Reeber et al. Nov 2013 A1
20130304429 Haimerl Nov 2013 A1
20130307955 Deitz et al. Nov 2013 A1
20130325069 Pereiro de Lamo et al. Dec 2013 A1
20130345757 Stad Dec 2013 A1
20140025118 Fallin et al. Jan 2014 A1
20140031871 Fallin et al. Jan 2014 A1
20140066994 Dominik et al. Mar 2014 A1
20140076883 Brailovski et al. Mar 2014 A1
20140081659 Nawana Mar 2014 A1
20140100582 Koch et al. Apr 2014 A1
20140135841 Wallenstein May 2014 A1
20140135842 Wallenstein May 2014 A1
20140135843 Barrus May 2014 A1
20140135844 Ark et al. May 2014 A1
20140137618 Isaacs May 2014 A1
20140168121 Chou Jun 2014 A1
20140188121 Lavallee Jul 2014 A1
20140207197 Reisberg Jul 2014 A1
20140240355 Isaacs Aug 2014 A1
20140244220 McKinnon et al. Aug 2014 A1
20140249591 Peultier et al. Sep 2014 A1
20140260484 Harper Sep 2014 A1
20140272881 Barsoum Sep 2014 A1
20140275981 Selover et al. Sep 2014 A1
20140278322 Jaramaz Sep 2014 A1
20140284838 Pfeffer et al. Sep 2014 A1
20140311203 Crawford et al. Oct 2014 A1
20140364860 Knoepfle et al. Dec 2014 A1
20140378828 Penenberg et al. Dec 2014 A1
20150073265 Popovic et al. Mar 2015 A1
20150100091 Tohmeh et al. Apr 2015 A1
20150150523 Sirpad et al. Jun 2015 A1
20150157416 Andersson Jun 2015 A1
20150216568 Sanpera Trigueros et al. Aug 2015 A1
20150227679 Kamer et al. Aug 2015 A1
20150238271 Wollowick et al. Aug 2015 A1
20150282796 Nawana et al. Oct 2015 A1
20150282797 O'Neil Oct 2015 A1
20150328004 Mafhouz Nov 2015 A1
20160100907 Gomes Apr 2016 A1
20160117817 Seel Apr 2016 A1
20160157751 Mahfouz Jun 2016 A1
20160191887 Casas Jun 2016 A1
20160210374 Mosnier et al. Jul 2016 A1
20160220318 Falardeau et al. Aug 2016 A1
20160235479 Mosnier et al. Aug 2016 A1
20160235480 Scholl Aug 2016 A1
20160242857 Scholl Aug 2016 A1
20160262800 Scholl et al. Sep 2016 A1
20160270772 Beale et al. Sep 2016 A1
20160296285 Chaoui et al. Oct 2016 A1
20160354161 Deitz Dec 2016 A1
20170071682 Bar et al. Mar 2017 A1
20170119472 Hermann et al. May 2017 A1
20170128145 Hasser et al. May 2017 A1
20170135707 Frey et al. May 2017 A9
20170135770 Scholl et al. May 2017 A1
20170165008 Finley Jun 2017 A1
20170215857 D'urso Aug 2017 A1
20170231710 Scholl Aug 2017 A1
20170252107 Turner et al. Sep 2017 A1
20170252123 D'urso Sep 2017 A1
20170258526 Lang Sep 2017 A1
20170360493 Zucker et al. Dec 2017 A1
20170367738 Scholl et al. Dec 2017 A1
20180008349 Gillman Jan 2018 A1
20180092699 Finley Apr 2018 A1
20180098715 Deitz Apr 2018 A1
20180104479 Grill et al. Apr 2018 A1
20180116727 Caldwell et al. May 2018 A1
20180132942 Mosnier et al. May 2018 A1
20180228566 McAfee Aug 2018 A9
20180233222 Daley et al. Aug 2018 A1
20180253838 Sperling et al. Sep 2018 A1
20180254107 Casey et al. Sep 2018 A1
20180263701 Casey et al. Sep 2018 A1
20180301213 Zehavi et al. Oct 2018 A1
20180303552 Ryan et al. Oct 2018 A1
20180310993 Hobeika et al. Nov 2018 A1
20180368921 Jeszenszky et al. Dec 2018 A1
20190029757 Roh et al. Jan 2019 A1
20190046268 Mosnier et al. Feb 2019 A1
20190046269 Hedblom et al. Feb 2019 A1
20190069956 Ryan et al. Mar 2019 A1
20190099221 Schmidt et al. Apr 2019 A1
20190146458 Roh et al. May 2019 A1
20190167435 Cordonnier Jun 2019 A1
20190209212 Scholl et al. Jul 2019 A1
20190216452 Nawana et al. Jul 2019 A1
20190254750 Metz Aug 2019 A1
20190254769 Scholl et al. Aug 2019 A1
20190269459 Mosnier et al. Sep 2019 A1
20190314094 Crawford Oct 2019 A1
20190350657 Tolkowsky Nov 2019 A1
20190362028 Mosnier et al. Nov 2019 A1
20190380782 McAfee et al. Dec 2019 A1
20190388099 Zuhars et al. Dec 2019 A1
20200015857 Rout et al. Jan 2020 A1
20200022758 Shoham et al. Jan 2020 A1
20200038109 Steinberg Feb 2020 A1
20200038111 Turner et al. Feb 2020 A1
20200060768 Mosnier et al. Feb 2020 A1
20200085503 Casey et al. Mar 2020 A1
20200093542 Arramon et al. Mar 2020 A1
20200093613 Arramon et al. Mar 2020 A1
20200107883 Herrmann et al. Apr 2020 A1
20200121394 Mosnier et al. Apr 2020 A1
20200129217 Zucker et al. Apr 2020 A1
20200129240 Singh et al. Apr 2020 A1
20200138519 Frey et al. May 2020 A1
20200155236 Chi May 2020 A1
20200188026 de Souza et al. Jun 2020 A1
20200197100 Leung et al. Jun 2020 A1
20200202515 Prasad et al. Jun 2020 A1
20200214854 O'Grady Jul 2020 A1
20200222121 Ignasiak Jul 2020 A1
20200261120 Scholl Aug 2020 A1
20200268452 Rezach et al. Aug 2020 A1
20200305985 Tolkowsky Oct 2020 A1
20200311318 Suddaby Oct 2020 A1
20200315708 Mosnier et al. Oct 2020 A1
20200330160 Dace et al. Oct 2020 A1
20200345420 Hobeika et al. Nov 2020 A1
20200352651 Junio et al. Nov 2020 A1
20200375636 Hobeika et al. Dec 2020 A1
20200405397 Liu et al. Dec 2020 A1
20200411163 Zehavi et al. Dec 2020 A1
20210030443 Scholl et al. Feb 2021 A1
20210038333 Kostrzewski et al. Feb 2021 A1
20210059838 Bodner Mar 2021 A1
20210093393 Quist et al. Apr 2021 A1
20210145518 Mosnier et al. May 2021 A1
20210145519 Mosnier et al. May 2021 A1
20210153942 Scheltienne et al. May 2021 A1
20210161682 O'Neil et al. Jun 2021 A1
20210186615 Shmayahu et al. Jun 2021 A1
20210210189 Casey et al. Jul 2021 A1
20210216671 Mosnier et al. Jul 2021 A1
20210244447 Schroeder Aug 2021 A1
20210264601 Pasha Aug 2021 A1
20210275227 Park et al. Sep 2021 A1
20210298834 Schlosser Sep 2021 A1
20210313062 Junio Oct 2021 A1
20210315515 Benson Oct 2021 A1
20210346092 Redmond et al. Nov 2021 A1
20210346093 Redmond et al. Nov 2021 A1
20220000556 Casey et al. Jan 2022 A1
20220013211 Steinberg et al. Jan 2022 A1
20220031396 Ryan et al. Feb 2022 A1
20220071710 Casey et al. Mar 2022 A1
20220096157 Pollock et al. Mar 2022 A1
20220117754 Sullivan et al. Apr 2022 A1
20220125602 Zucker Apr 2022 A1
20220142709 Zucker May 2022 A1
20220151699 Schmidt et al. May 2022 A1
20220240986 Scholl Aug 2022 A1
Foreign Referenced Citations (61)
Number Date Country
2826947 Mar 2014 CA
2885154 Apr 2007 CN
200966629 Oct 2007 CN
101647724 Feb 2010 CN
202161397 Mar 2012 CN
202982181 Jun 2013 CN
107157579 Sep 2017 CN
107647914 Feb 2018 CN
109124763 Jan 2019 CN
109124763 Sep 2020 CN
9408154 Jul 1994 DE
29510041 Oct 1995 DE
29609276 Aug 1996 DE
10314882 Oct 2004 DE
102004008870 Oct 2004 DE
102007033219 Jan 2009 DE
102010033116 Feb 2012 DE
102011006574 Oct 2012 DE
20201400218 Mar 2014 DE
2017785 Jan 2009 EP
2153785 Feb 2010 EP
2468201 Jun 2012 EP
2522295 Nov 2012 EP
2730242 May 2014 EP
2401811 Apr 2013 ES
2975583 Nov 2012 FR
3004100 Oct 2014 FR
2267757 Dec 1993 GB
H-04297270 Oct 1992 JP
2007213015 Aug 2007 JP
2007283081 Nov 2007 JP
2013230221 Nov 2013 JP
2015531661 Nov 2015 JP
2016093497 May 2016 JP
2016536051 Nov 2016 JP
103823 Mar 2009 PT
1747045 Jul 1992 SU
199808454 Mar 1998 WO
2007009263 Jan 2007 WO
2009035358 Mar 2009 WO
2009039371 Mar 2009 WO
2009140294 Nov 2009 WO
2011038845 Apr 2011 WO
2012062464 May 2012 WO
2012135653 Oct 2012 WO
2013070628 May 2013 WO
2013085982 Jun 2013 WO
2013150233 Oct 2013 WO
2014016824 Jan 2014 WO
2014043661 Mar 2014 WO
2014055081 Apr 2014 WO
2014074850 May 2014 WO
2014088801 Jun 2014 WO
2014107144 Jul 2014 WO
2014143762 Sep 2014 WO
2015054543 Apr 2015 WO
WO-2015054543 Apr 2015 WO
2015195843 Dec 2015 WO
2017064719 Apr 2017 WO
2017127838 Jul 2017 WO
2021160599 Aug 2021 WO
Non-Patent Literature Citations (29)
Entry
Aubin et al., “Preoperative planning simulator for spinal deformity surgeries ”, Spine, 2008, pp. 2143-2152, 33, No. 20.
Aurouer et al., “Computerized preoperative planning for correction of sagittal deformity of the spine ”, Surg Radiol Anat, 2009, pp. 781-792, 31, No. 10.
Farahani et al., “Prediction of the movement patterns for human squat jumping using the inverse-inverse dynamics technique ”, XIII International Symposium on Computer Simulation in Biomechanics, 2011, 2 p.
Miajdouline et al., “Computer simulation for the optimization of Instrumentation strategies in adolescent idiopathic scoliosis.”, Med Biol Eng Comput, 2009, pp. 1143-1154, 47, No. 11.
AnyBody Publication List, located online on Sep. 29, 2022 at: https://anybodytech.com/resources/anybodypublications/, 90 pages.
AnyBody Technology, “ARO Medical breaks the degenerative spiral”, ARO Medical, Version 1.3, Nov. 20, 2013, 1 page.
AnyScript.org—Wiki: AnyScript Support Wiki, Main Page, located online on the Wayback Machine on Sep. 29, 2022 at: https://web.archive.org/wb/201602243555/http://wiki.anyscript.org:80/index.php/Main_Page, page last modified Oct. 5, 2015, 2 pages.
Australian Exam Report in Application 2017225796, dated Nov. 13, 2020, 5 pages.
Australian Exam Report in Application 2021203401, dated Jan. 7, 2022, 3 pages.
European Extended Search Report in Application 17760840.3, dated Sep. 30, 2019, 9 pages.
European Extended Search Report in Application 21168383.4, dated Jun. 28, 2021, 8 pages.
Israeli Exam Report in Application 26132818, dated Jun. 14, 2021, 3 pages.
Japanese 2nd Written Opinion in Application 2018-545412, dated Jan. 17, 2022, 2 pages.
Japanese Decision of Refusal in Application 2018-545412, dated Jun. 7, 2022, 3 pages.
Japanese Notice of Reasons for Refusal in Application 2018-545412, dated Nov. 2, 2021, 6 pages.
Japanese Notice of Reasons for Refusal in Application 2018-545412, dated Mar. 2, 2021, 8 pages.
Japanese Search Report in Application 2018-545412, dated Feb. 15, 2021, 13 pages.
Japanese Written Opinion in Application 2018-545412, dated Jun. 1, 2021, 3 pages.
K2M Pre-Bent Rod Tool video, located online at: https://www.youtube.com/watch?v=GE-UqEOFXFk, duration 3:50, uploaded by Surgimap on Feb. 16, 2017, last accessed on Sep. 12, 2022, 1 page.
KEOPS Demostration Video, located online at: https://www.youtube.com/watch?v=5f_SoE6Ze8g, duration 5:11, uploaded by SMAIO69 on Nov. 29, 2012, last accessed on Sep. 12, 2022, 1 page.
Lehman et al., “Do intraoperative radiographs in scoliosis surgery reflect radiographic result?”, Clinical Orthopaedics and Related Research, 2010, pp. 679-686, 468, No. 2.
Medicrea UNiD Spinal Rod Used at Scoliosis and Spinal Surgery video, located online at: https://www.youtube.com/watch?v=E-MonYoKSEg, duration 1:58, uploaded by John Henry Krause Voice Actor on Jul. 28, 2015, last accessed on Sep. 12, 2022, 3 pages.
PCT International Preliminary Report on Patentability in International Application PCT/US2017/020491, dated Sep. 13, 2018, 7 pages.
PCT International Search Report and Written Opinion in International Application PCT/US2017/020491, dated May 26, 2017, 10 pages.
Roussouly, Pierre et al., “Sagittal Parameters of the Spine: Biomechanical Approach”, Eur. Spine J (Jul. 11, 2011); 20 (Suppl. 5):S578-S585.
Schlenk et al., “Biomechanics of spinal deformity”, Neurosurgical Focus, 2003, 14, No. 1.
Smith et al., “Clinical and Radiographic evaluation of the adult spinal deformity patient”, Neurosurg Clin N Am, 2013, pp. 143-156, 24, No. 2.
Tanquay et al., “Relation between the sagittal pelvic and lumbar spine geometries following surgical correction of adolescent idiopathic scoliosis”, European Spin Journal, 2007, pp. 531-536, 16, No. 4.
The NHS Innovations & EOS video, location online at: https://www.youtube.com/watch?v=GeU9kWcSY-I, duration 10:17, uploaded by EOS Imaging on Apr. 29, 2014, last accessed on Sep. 12, 2022, 9 pages.
Related Publications (1)
Number Date Country
20210212766 A1 Jul 2021 US
Provisional Applications (1)
Number Date Country
62302725 Mar 2016 US
Continuations (2)
Number Date Country
Parent 16582760 Sep 2019 US
Child 17206256 US
Parent 15448119 Mar 2017 US
Child 16582760 US