Devices, methods and systems for assessing suitability of spinal implants

Abstract

A system for evaluating a spinal implant includes: at least one processor; and a memory storing instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: receive first image data of a spine of a patient; determining a property of a first bone anatomy in at least a first portion of the spine based on the first image data; determining an initial screw trajectory for inserting a screw into the spine based on the property of the first bone anatomy; virtually inserting the screw along the initial screw trajectory using the first image data to generate modified first image data; performing an initial loading simulation on the virtually inserted screw using the modified first image data; and determining a suitability of the initial screw trajectory for implanting the screw in the spine based on the initial loading simulation.

Description

Technical Field

[0001] The present technology generally relates to assessing the suitability of spinal implants and, more particularly, to evaluating spinal implants used in orthopedic surgery. Background Art

[0002] Lumbar spinal stenosis or neuronal compression due to narrowing of the spinal canal is one of the leading causes of spinal problems in patients over 65 years old. Spinal decompression procedures for relieving spinal compression are delicate, time-consuming, and high-risk tasks. The diagnosis of spinal stenosis is typically accomplished using a combination of patient imaging, patient verbal input, patient-reported functionality, and physical examination of the patient. Once diagnosed, spinal stenosis can be treated with non-invasive methods (such as massage, chiropractic treatment, and / or acupuncture) and / or with invasive procedures (including laminectomy, laminotomy, discectomy, etc.).

[0003] Entry of bone anatomy and / or soft tissue into the spine and removal of bone anatomy and / or soft tissue from the spine can affect the integrity of the spine, including the stability and / or mobility of the spine. Iatrogenic instability is instability of the spine caused by surgical or medical intervention and can have a negative impact on the patient's quality of life.

[0004] Conventional methods for assessing spinal integrity (including the risk that a given procedure will cause iatrogenic instability) and / or the effectiveness of spinal implants rely on the surgeon's prior experience, knowledge, and judgment. Such conventional methods are time-consuming, subjective, complex, and may not be recordable for future reference or use. Whether spinal fusion or other methods for improving spinal integrity and / or a particular implant is warranted after decompression is a subjective determination. Summary of the Invention

[0005] Embodiments of the present disclosure provide objective techniques for determining the suitability of spinal implants in a pre-operative setting, where such spinal implants can correspond to screws and / or intervertebral implants for spinal fusion procedures.

[0006] Example aspects of the present disclosure include:

[0007] A system for evaluating a spinal implant, the system comprising: at least one processor; and a memory that stores instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: receive first image data of a patient's spine; determine a property of a first bone anatomy in at least a first portion of the spine based on the first image data; determine an initial screw trajectory for inserting a screw into the spine based on the property of the first bone anatomy; virtually insert the screw along the initial screw trajectory using the first image data to generate modified first image data; perform an initial loading simulation on the virtually inserted screw using the modified first image data; and determine the suitability of the initial screw trajectory for implanting the screw into the spine based on the initial loading simulation.

[0008] In any aspect of the aspects herein, wherein the suitability of the initial screw trajectory is determined based on a comparison that compares the property of the first bone anatomy with the property of the virtually inserted screw during a simulated motion cycle of the spine.

[0009] In any aspect of the aspects herein, wherein the property of the virtually inserted screw includes the rigidity of the virtually inserted screw, and wherein the property of the first bone anatomy includes the rigidity of the first bone anatomy.

[0010] In any aspect of the aspects herein, wherein the property of the first bone anatomy is based on a bone density measurement of the first skeletal anatomy expressed in Hounsfield units.

[0011] In any aspect of the aspects herein, wherein the comparison is performed between a set of points on the virtually inserted screw and a corresponding set of points on the first bone anatomy.

[0012] In any aspect of the aspects herein, wherein the set of points includes at least one point in a vertebral body of the spine and at least one point in a pedicle of the spine.

[0013] In any aspect of the aspects herein, wherein the memory stores additional instructions that when executed further cause the at least one processor to: when the initial screw trajectory is determined to be unsuitable, determine an updated screw trajectory for the screw, the updated screw trajectory having parameters changed compared to the initial screw trajectory; virtually insert the screw according to the updated screw trajectory; and perform an updated loading simulation on the virtually inserted screw inserted along the updated screw trajectory.

[0014] In any aspect of the aspects herein, wherein the changed parameters relate to screw type, screw diameter, screw position, implant reinforcement, or the angle of the screw trajectory.

[0015] In any aspect of the various aspects of this document, where the memory stores additional instructions, the additional instructions, when executed, further cause the at least one processor to: generate a surgical plan for inserting the screw into the spine along the initial screw trajectory when the initial screw trajectory is determined to be suitable.

[0016] In any aspect of the various aspects of this document, where the memory stores additional instructions, the additional instructions, when executed, further cause the at least one processor to: receive second image data of the spine of the patient; determine the nature of a second bony anatomy in at least a second portion of the spine based on the second image data; determine an initial position for inserting an intervertebral implant into the spine based on the nature of the second bony anatomy; virtually remove the spinal anatomy at the initial position using the second image data to generate modified second image data; virtually insert the intervertebral implant at the initial position using the modified second image data; perform a first loading simulation on the virtually inserted intervertebral implant; and determine the suitability of the intervertebral implant at the initial position based on the first loading simulation.

[0017] In any aspect of the various aspects of this document, where the memory stores additional instructions, the additional instructions, when executed, further cause the at least one processor to: generate a surgical plan for implanting the intervertebral implant at the initial position when the initial position is determined to be suitable.

[0018] In any aspect of the various aspects of this document, where the nature of the second bony anatomy is based on a bone density measurement of the second skeletal anatomy expressed in Hounsfield units.

[0019] In any aspect of the various aspects of this document, where the first loading simulation includes performing a comparison that compares the nature of the second bony anatomy with the nature of the virtually inserted intervertebral implant during a simulated motion cycle of the spine.

[0020] In any aspect of the various aspects of this document, where the comparison is performed between a set of points on the virtually inserted intervertebral implant and a corresponding set of points on the second bony anatomy.

[0021] In any aspect of the various aspects of this document, where the second bony anatomy corresponds to an upper endplate, a lower endplate, or both, and where the spinal anatomy corresponds to at least a portion between the upper endplate and the lower endplate of the intervertebral disc.

[0022] A system for evaluating a spinal implant, the system comprising: at least one processor; and a memory that stores instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: receive image data of a patient's spine; determine a property of a bone anatomy in at least a first portion of the spine based on the image data; determine an initial plan for inserting an implant into the spine based on the property of the bone anatomy; virtually insert the implant according to the initial plan using the image data to generate modified image data; perform a first loading simulation on the virtually inserted implant using the modified image data; and determine the suitability of the initial plan for implanting the implant based on the first loading simulation.

[0023] In any aspect of the aspects herein, wherein the implant corresponds to an intervertebral implant, and wherein the intervertebral implant is virtually inserted between an upper endplate and a lower endplate.

[0024] In any aspect of the aspects herein, wherein the implant corresponds to a screw, and wherein the screw is virtually inserted into a pedicle and a vertebral body along a screw trajectory.

[0025] A system for evaluating a spinal implant, the system comprising: at least one processor; and a memory that stores instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: receive first image data of a patient's spine; determine a property of a first bone anatomy in at least a first portion of the spine based on the first image data; determine an initial position for inserting an intervertebral implant into the spine based on the property of the first bone anatomy; virtually remove a spinal anatomy at the initial position using the first image data to generate modified first image data; virtually insert the intervertebral implant at the initial position using the modified first image data; perform a first loading simulation on the virtually inserted intervertebral implant; and determine the suitability of the intervertebral implant at the initial position based on the first loading simulation.

[0026] In any aspect of the aspects herein, wherein the memory stores additional instructions, the additional instructions when executed further causing the at least one processor to: receive second image data of the patient's spine; determine a property of a second bone anatomy in at least a second portion of the spine based on the second image data; determine an initial screw trajectory for inserting a screw into the spine based on the property of the second bone anatomy; virtually insert the screw along the initial screw trajectory using the second image data to generate modified second image data; perform a second loading simulation on the virtually inserted screw using the modified second image data; and determine the suitability of the initial screw trajectory for implanting the screw into the spine based on the second loading simulation.

[0027] Details of one or more aspects of the present disclosure are set forth in the following drawings and the specification. Other features, objects, and advantages of the technology described in the present disclosure will be apparent from the specification, the drawings, and the claims.

[0028] The phrases "at least one", "one or more", and "and / or" are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions "at least one of A, B, and C", "at least one of A, B, or C", "one or more of A, B, and C", "one or more of A, B, or C", and "A, B, and / or C" means only A, only B, only C, A and B together, A and C together, B and C together, or A, B, and C together. When each of A, B, and C in the above expressions refers to an element such as X, Y, and Z or a class of elements such as X 1 -X n 、Y 1 -Y m and Z 1 -Z o of a class of elements, the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., X 1 and X 2 ), and a combination of elements selected from two or more classes (e.g., Y 1 and Z o ).

[0029] The term "a" entity refers to one or more of that entity. Thus, the terms "a", "one or more", and "at least one" may be used interchangeably herein. It should also be noted that the terms "comprising", "including", and "having" may be used interchangeably.

[0030] The foregoing is a simplified overview of the present disclosure to provide an understanding of some aspects of the present disclosure. This summary of the invention is neither an extensive overview nor an exhaustive overview of the present disclosure and its various aspects, embodiments, and configurations. It is neither intended to identify the key or important elements of the present disclosure nor to delineate the scope of the present disclosure, but rather to present selected concepts of the present disclosure in a simplified form as an introduction to the more detailed description presented below. As should be understood, other aspects, embodiments, and configurations of the present disclosure may utilize one or more of the features set forth above or described in detail below, either alone or in combination.

[0031] Many additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the embodiments described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are incorporated in and form a part of this specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the present disclosure. The drawings merely illustrate how to implement and use the preferred and alternative examples of the present disclosure, and these examples should not be construed as limiting the present disclosure only to the examples illustrated and described. Additional features and advantages will become apparent from the following more detailed description of various aspects, embodiments, and configurations of the present disclosure, as illustrated by the accompanying drawings referred to below.

[0033] Figure 1 is a block diagram of a system according to at least one embodiment of the present disclosure;

[0034] Figure 2 is a flowchart of a method according to at least one embodiment of the present disclosure;

[0035] Figure 3A is a side image of a spinal region according to at least one embodiment of the present disclosure;

[0036] Figure 3B is according to at least one embodiment of the present disclosure Figure 3A an overhead image of a vertebra within the spinal region of;

[0037] Figure 4A is another side image of a spinal region according to at least one embodiment of the present disclosure;

[0038] Figure 4B is according to at least one embodiment of the present disclosure Figure 4A an overhead image of a vertebra within the spinal region of;

[0039] Figure 5A is a side image of a spinal region according to at least one embodiment of the present disclosure;

[0040] Figure 5B is according to at least one embodiment of the present disclosure Figure 4A an overhead image of a vertebra within the spinal region of;

[0041] Figure 6 is another flowchart of a method according to at least one embodiment of the present disclosure;

[0042] Figure 7 is another flowchart of a method according to at least one embodiment of the present disclosure;

[0043] Figure 8 is another flowchart of a method according to at least one embodiment of the present disclosure;

[0044] Figure 9A is another flowchart of a method according to at least one embodiment of the present disclosure;

[0045] Figure 9B illustrates a visual representation of certain steps in Figure 9A in accordance with at least one embodiment of the present disclosure;

[0046] Figure 10A is another flowchart of a method in accordance with at least one embodiment of the present disclosure; and

[0047] Figure 10B illustrates a visual representation of certain steps in Figure 10A in accordance with at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] It should be understood that the various aspects disclosed herein can be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that depending on the example or embodiment, certain actions or events of any of the processes or methods described herein can be performed in a different order, can be added, combined, or entirely omitted (e.g., all of the described actions or events may not be necessary for performing the techniques). Additionally, although certain aspects of the present disclosure are described as being performed by a single module or unit for clarity, it should be understood that the techniques of the present disclosure can be performed by a combination of units or modules associated with, for example, a computing device and / or a medical device.

[0049] In one or more examples, the described methods, processes, and techniques can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium can include a non-transitory computer-readable medium that corresponds to a tangible medium, such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0050] The instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionic processors; or any other general-purpose microprocessor), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Thus, as used herein, the term “processor” may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Additionally, these techniques may be fully implemented in one or more circuits or logic elements.

[0051] Before explaining any embodiments of the present disclosure in detail, it is to be understood that the present disclosure is not limited in its application to the construction details and component arrangements set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or carried out in various ways. Additionally, it is to be understood that the terminology and phraseology used herein is for the purpose of description and should not be regarded as limiting. The use of “comprising,” “including,” or “having” and variations thereof herein is intended to cover the items listed thereafter and equivalents thereof, as well as additional items. Furthermore, the present disclosure may use examples to illustrate one or more aspects thereof. Unless otherwise expressly stated, the use or listing of one or more examples (which may be indicated by “for example,” “by way of example,” “such as,” or similar language) is not intended and does not limit the scope of the present disclosure.

[0052] Embodiments of the present disclosure advantageously provide an objective means of assessing the effect of a decompression protocol or other spinal surgery on spinal stability, specifically including for assessing the risk that a given decompression or other spinal surgery will result in iatrogenic instability. Thus, embodiments of the present disclosure beneficially augment the treating physician's prior experience, knowledge, and judgment with objective data. Embodiments of the present disclosure may also beneficially enable the treating physician to evaluate the impact of one or more possible decompression or other spinal surgery protocols on spinal integrity, select the protocol that has the least negative impact on spinal integrity, and / or prepare for spinal fusion or other stability-enhancing protocols prior to a surgical protocol that is expected to negatively impact spinal integrity.

[0053] In addition, embodiments of the present disclosure provide objective techniques for determining the suitability of spinal implants in a preoperative setting, where such spinal implants may correspond to screws and / or intervertebral implants for spinal fusion procedures. The systems and methods according to at least one embodiment perform an evaluation of screw performance and / or perform an evaluation of intervertebral performance based on patient-specific measurements of bone anatomy (e.g., stiffness and / or bone density measurements derived from image data of a patient's spinal region).

[0054] More specifically, example embodiments use the patient's image data to virtually place the implant and then perform one or more loading simulations that simulate loading on one or more points on the implant. The loading simulations can be performed during a simulated motion of the spine (e.g., simulating a patient walking or spinal flexion), and can involve comparing the properties of the implant at one or more points with the properties of the bone anatomy at one or more corresponding points. In one specific non-limiting example, these properties correspond to the stiffness of the bone anatomy and the implant. If the loading simulation indicates that the implant is not suitable, the method according to the example embodiment can change the parameters of the simulation (e.g., screw position, screw size, screw type, screw material, intervertebral size, intervertebral position, and / or intervertebral material, etc.). When the loading simulation indicates that the properties of the implant at a particular point do not properly match the properties of the bone anatomy at the corresponding point (e.g., outside an acceptable range), the implant can be determined to be unsuitable. In one example, if the stiffness of the bone anatomy is exceeded during loading, the implant can be determined to be unsuitable for the spinal fusion procedure. If the loading simulation indicates that the implant is suitable (e.g., the properties of the implant and the properties of the bone anatomy sufficiently match and / or the mechanical stress on the bone anatomy and / or the implant does not exceed the corresponding thresholds), a surgical plan for implanting the implant can be automatically generated and, in some cases, displayed on a display for viewing by a surgeon or other user. As can be appreciated, embodiments of the present disclosure provide a patient-specific evaluation of whether a spinal implant is suitable for a spinal fusion procedure.

[0055] Turning first to Figure 1, showing a block diagram of a system 100 according to at least one embodiment of the present disclosure. The system 100 can be used to process images, detect spinal stenosis, perform one or more virtual simulations, evaluate spinal integrity, evaluate spinal implant suitability with simulated loading, generate a decompression plan, generate a fusion plan, and / or perform other aspects of one or more of the methods disclosed herein. The system 100 includes a computing device 102, an imaging device 112, a database 114, and / or a cloud or other network 116. The computing device 102 includes a processor 104, a memory 106, a communication interface 108, and a user interface 110. Systems according to other embodiments of the present disclosure (such as the system 100) may include more or fewer components than the system 100.

[0056] The processor 104 of the computing device 102 can be any processor described herein or any similar processor. The processor can be configured to execute instructions stored in the memory 106, which can cause the processor to perform one or more computational steps using or based on data received from the imaging device 112, the database 114, and / or the cloud 116.

[0057] The memory 106 can be or include RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other non-transitory memory for storing computer-readable data and / or instructions. The memory 106 can store information or data that can be used to complete any step of any of the methods described herein. The memory can store, for example, image processing instructions 118, stenosis detection instructions 120, simulation instructions 122, and / or stability evaluation instructions 124. In some embodiments, such instructions can be organized into one or more applications, modules, packages, layers, or engines. The instructions can cause the processor 104 to manipulate data stored in the memory 106 and / or data received from the imaging device 112, the database 114, and / or the cloud 116 to perform any step of any of the methods described herein.

[0058] The computing device 102 may also include a communication interface 108. The communication interface 108 may be used to receive image data or other information from external sources such as an imaging device 112, a database 114, and / or the cloud 116, and / or to transmit simulation results, decompression plans, fusion plans, images, or other information to external sources (e.g., the database 114, the cloud 116, another computing device 102). The communication interface 108 may include one or more wired interfaces (e.g., USB ports, Ethernet ports, FireWire ports) and / or one or more wireless interfaces (e.g., configured to transmit information via one or more wireless communication protocols such as 802.11a / b / g / n, Bluetooth, NFC, ZigBee, etc.). In some embodiments, the communication interface 108 may be used to enable the device 102 to communicate with one or more other processors 104 or computing devices 102, either to reduce the time required to complete computationally intensive tasks or for any other reason.

[0059] The computing device 102 may also include one or more user interfaces 110. The user interface 110 may be or include a keyboard, a mouse, a trackball, a display, a television, a touch screen, and / or any other device for receiving information from a user and / or for providing information to a user. The user interface 110 may be used, for example: to receive user selections or other user inputs regarding a decompression procedure for simulation and / or planning; to receive user inputs regarding an evaluation of an implant under simulated loading; to receive user selections or other user inputs regarding the type of approach to be used to perform a decompression procedure; to receive user inputs regarding the portion of the bone anatomy and / or soft tissue to be removed to achieve decompression; to display a recommended decompression plan to a surgeon or other user; to display simulation results to a surgeon or other user; to display information corresponding to an evaluation of an implant, a stability evaluation, an activity evaluation, or another spinal integrity evaluation to a surgeon or other user; to display information regarding the risk of iatrogenic instability to a surgeon or other user; to display a decompression plan to a surgeon or other user; and / or to display a fusion plan to a surgeon or other user. In some embodiments, the user interface 110 may be used to allow a surgeon or other user to modify a decompression plan, a fusion plan, or other displayed information.

[0060] Although the user interface 110 is shown as part of the computing device 102, in some embodiments, the computing device 102 may utilize a user interface 110 that is separately housed from one or more of the remaining components of the computing device 102. In some embodiments, the user interface 110 may be located proximal to one or more of the other components of the computing device 102, while in other embodiments, the user interface 110 may be located remotely from one or more of the other components of the computing device 102.

[0061] The imaging device 112 is operable to image a patient's anatomical structure (e.g., the spinal region) to generate image data (e.g., image data depicting the patient's spine). The image data may correspond to the entire spine of the patient or a portion of the patient's spine. The imaging device 802 may be, but is not limited to, a magnetic resonance imaging (MRI) scanner, a CT scanner, or other X-ray machine, an ultrasound scanner, an optical computed tomography scanner, or any other imaging device suitable for obtaining an image of the patient's spine.

[0062] The database 114 may store one or more images taken by one or more imaging devices 112 and may be configured to provide one or more such images (electronically, in the form of image data) to a computing device such as the computing device 102. The database 114 may be configured to provide the image data directly to the computing device 102 (e.g., when the computing device 102 and the database 114 are co-located and / or connected to the same local area network) and / or to provide the image data to the computing device via the cloud 116 (e.g., when the computing device 102 and the database 114 are not co-located or otherwise connected to the same local area network). In some embodiments, the database 114 may be or include a part of a hospital image storage system (such as a Picture Archiving and Communication System (PACS), a Health Information System (HIS)) and / or another system for collecting, storing, managing, and / or transmitting electronic medical records including image data).

[0063] The cloud 116 may be or represent the Internet or any other wide area network. The computing device 108 may be connected to the cloud 116 via a wired connection or a wireless connection through the communication interface 108. In some embodiments, the computing device 102 may communicate with the imaging device 112, the database 114, one or more other computing devices 102, and / or one or more other components of the computing device 102 (such as a display or other user interface 110) via the cloud 116.

[0064] Turning now to Figures 2 to 5B , the method 200 according to an embodiment of the present disclosure may be performed in whole or in part on the computing device 102.

[0065] The method 200 includes: receiving and processing preoperative image data (step 202). The preoperative image data may include or correspond to, for example, a three-dimensional image of the patient's spine and may include data corresponding to a plurality of individual incisions, slices, or segments of the patient's spine, which together constitute the three-dimensional image of the spine. Additionally or alternatively, the preoperative image data may include or correspond to one or more two-dimensional images of the patient's spine. For example, the preoperative image data may correspond to an image 300 such as Figure 3A and / or Figure 3BImages such as image 302. In cases where the image data includes or corresponds to multiple two-dimensional images of a patient's spine, the multiple two-dimensional images may be sufficient to construct or reconstruct a three-dimensional image or model of the spine. The preoperative image data may correspond to preoperative images of the patient's spine taken using an imaging device 112 (such as an MRI scanner, a CT scanner, or another imaging device). The preoperative image data may contain data for the entire spine of the patient or data for a part of the patient's spine. The preoperative image data may be received from the imaging device 112, the database 114, the cloud 114, or any other source, and may be received via the communication interface 108.

[0066] The processing of the preoperative image data may include: applying one or more filters to the image data to prepare the image data for further processing. The processing of the preoperative image data may also include: segmenting the preoperative image data to identify, for example, one or more vertebrae 310, one or more intervertebral discs 312, the spinal cord 304, and / or one or more nerve exits 306 represented by the image data. The segmentation may utilize feature recognition, machine learning, or any other segmentation method. Additionally or alternatively, the processing may include: measuring, inferring, calculating, or otherwise identifying one or more properties of the bony anatomy or soft tissue represented in the image data. For example, the processing may include: determining a measurement of one or more vertebrae 310 or portions thereof in Hounsfield units and converting the measurement into a bone mineral density value or another value representative of the strength or stiffness of the bony anatomy under consideration. Any other measurement or algorithm may be utilized to measure, infer, calculate, or otherwise determine the strength or stiffness of one or more anatomical elements represented in the image data. In some embodiments, the processing produces a determination of the material strength or stiffness for the multiple vertebrae 310 represented in the image data or for each vertebra 310 represented in the image data. Also in some embodiments, the modeling and processing performed on a computer produces a determination of the material strength or stiffness for the multiple intervertebral discs 312 represented in the image data or for each intervertebral disc represented in the image data.

[0067] Method 200 further includes: identifying spinal stenosis based on the preoperative image data (step 204). Stenosis may be identified in the central canal and / or the lateral recesses of the spine represented by the image data. In some embodiments, stenosis may be identified by comparing one or more properties of the spinal cord 304 at one location of the spine represented in the image data with the corresponding one or more properties of the spinal cord 304 at another location of the spine represented in the image data. For example, stenosis may be identified by comparing the diameter of the spinal cord at a first segment of the spine with the diameter of the spinal cord at one or more other segments of the spine, which one or more other segments may be adjacent to the first segment. A sudden or rapid change in the diameter of the spine between adjacent or contiguous segments may indicate stenosis.

[0068] In other embodiments, stenosis may be identified by applying a predetermined algorithm to the image data. The algorithm may be or may have been generated, for example, by a machine learning engine based on training data.

[0069] In yet other embodiments, a surgeon or other user may identify stenosis by providing one or more inputs via the user interface 110. In such embodiments, the identification of stenosis may be based on the image data and / or additional information obtained in other ways known to the surgeon or other user, such as information provided by the patient or information revealed during a neurological examination.

[0070] Method 200 further includes: determining one or more symptomatic segments of spinal stenosis (step 206). In some embodiments, the identified stenosis may be located at only one segment of the spine represented in the image data. In other embodiments, the identified stenosis may be located at multiple segments of the spine. Based on the processing of the image data in step 202 and / or the identification of stenosis in step 204, the segments where stenosis is present may be determined and, in some embodiments, recorded (e.g., in the memory 106) and / or reported (e.g., via the user interface 110). The segments where stenosis is present are the target segments for the purposes of steps 208 and 210.

[0071] Method 200 further includes: identifying the bony anatomy and / or soft tissue to be removed at the target segment (step 208). Correction of spinal stenosis generally involves decompression or removal of the bony anatomy and / or soft tissue that is compressing the spinal cord. In some embodiments, an algorithm is used to identify the bony anatomy and / or soft tissue at the target segment that needs to be removed to create sufficient space for the spinal cord 304 to return to its normal diameter (or otherwise be released from compression). For example, in Figures 4A to 4B , markers 402, 404, and 406 identify the bony anatomy and / or soft tissue that needs to be removed to release the spinal cord 304 from compression. The bony anatomy may be, for example, a part of one or more vertebrae 310, and the soft tissue may be, for example, all or a part of the intervertebral disc 312.

[0072] In some embodiments, identifying the bone anatomy and / or soft tissue to be removed at the target segment may include: receiving user input via the user interface 110 (e.g., from a surgeon or other user), the user interface identifying the bone anatomy and / or soft tissue to be removed. For example, the user input may include a user selection of one of a laminectomy, a laminotomy, a foraminotomy, a discectomy, or other procedures. Based on the user selection, the recommended portion of the bone anatomy and / or soft tissue may be automatically identified for removal, or the surgeon or other user may identify the portion of the bone anatomy and / or soft tissue to be removed. For example, if the user input is a laminectomy, the lamina proximate the identified stenotic vertebra 310 may be automatically identified for removal, or the user may select the lamina of the vertebra 310 for removal.

[0073] Typically, the bone anatomy or soft tissue that needs to be removed during a decompression procedure is not easily accessible to the surgeon. Thus, the surgeon must remove additional bone anatomy and / or soft tissue from the spine only to access the bone anatomy and / or soft tissue causing the stenosis. Then, in some embodiments, the identification of the bone anatomy and / or soft tissue to be removed includes not only identifying the bone anatomy and / or soft tissue that needs to be removed to correct the stenosis, but also identifying the optimal trajectory for accessing the bone anatomy and / or soft tissue causing the stenosis and / or the bone anatomy and / or soft tissue that needs to be removed to access the bone anatomy and / or soft tissue causing the stenosis. The bone anatomy and / or soft tissue that must be removed to access the bone anatomy and / or soft tissue causing the stenosis may be automatically identified (e.g., by applying an algorithm selected based on the procedure to be performed and / or an algorithm predefined by a machine learning engine or otherwise) or identified via additional user input through the user interface 110.

[0074] The identifying step 208 may further include: marking the identified bone anatomy and / or soft tissue in the image data.

[0075] The method 200 further includes: virtually removing the bone anatomy and / or soft tissue at the target segment in a simulated decompression procedure to produce modified preoperative image data (step 210). The virtual removal of the bone anatomy and / or soft tissue may include: modifying the image data to replace the identified bone anatomy and / or soft tissue with a virtual material having little or no intensity or stiffness. Alternatively, the virtual removal of the bone anatomy and / or soft tissue may include: simply deleting the portion of the image data representing the bone anatomy and / or soft tissue to be removed. As another alternative, the virtual removal of the bone anatomy and / or soft tissue may include: assigning a Hounsfield unit measurement of zero to the portion of the image data representing the bone anatomy and / or soft tissue to be removed. Figure 5A and Figure 5BShows portions 502, 504, and 506 of bony anatomy and / or soft tissue removed from an imaged spine.

[0076] Method 200 further includes: evaluating the stability of the spine based on the modified preoperative image data (step 212). Evaluating the stability of the spine can include, for example: running a virtual 6-degree-of-motion analysis based on the modified preoperative image data to evaluate the predicted spinal stability after the planned procedure. This analysis can evaluate the stress that will be present in one or more vertebrae or vertebral segments of the spine represented in the modified preoperative image data when the spine is in a neutral position and / or when the spine is in one or more flexion positions. In some embodiments, a virtual 6-degree-of-motion analysis can be performed using the original preoperative image data, and the results can be used as a reference for the virtual 6-degree-of-motion analysis performed using the modified preoperative image data to evaluate how the planned procedure may affect spinal stability relative to the preoperative stability level of the spine. In some embodiments, evaluating the stability of the spine can be the same as or similar to performing a finite element analysis of the spine (as represented in the modified preoperative image data).

[0077] The result of the evaluation (whether using a virtual 6-degree-of-motion analysis or otherwise) can be: a calculated level of predicted stability or instability of the spine (e.g., in millimeters of translation or angulation, in N / m 2 or otherwise measured); an indication of the predicted maximum stress that one or more vertebrae of the spine will experience; an indication that the predicted stress level of one or more vertebrae of the spine exceeds or is close to a pre-determined threshold; or any other indication related to the predicted stability or instability of the spine represented in the modified preoperative image data in the case of performing the planned procedure. In some embodiments, the result of the virtual 6-degree-of-motion analysis can be an indication of whether the risk of iatrogenic instability is high, medium, or low, which indication can be based on comparing the calculated or otherwise predicted iatrogenic instability with one or more pre-determined thresholds.

[0078] The spinal stability evaluation of step 212 can also evaluate whether and / or how removing bony anatomy and / or soft tissue from the spine during the planned procedure affects spinal movement. For example, removing bony anatomy and / or soft tissue from the spine can cause at least a portion of the spine to be less constrained and able to move more freely. This, in turn, can affect the stress applied to one or more elements of the spine.

[0079] Method 200 also includes: determining a decompression plan and a possible implant placement plan (step 214). The decompression plan can be a plan for performing a simulation protocol for modifying preoperative data prior to performing the spinal stability assessment in step 212. The decompression plan can include information about which bony anatomy and / or soft tissue is to be removed to correct the identified stenosis. The decompression plan can also include information about which bony anatomy and / or soft tissue is to be removed to access the bony anatomy and / or soft tissue that is causing the stenosis condition. The decompression plan can include an identification of which instruments or types of instruments are to be used for one or more steps of the planned decompression, the trajectory of the decompression, and / or the sequence of steps for performing the planned decompression. The decompression plan can be automatically generated and then presented to the surgeon or other user for review, modification, and / or approval. Alternatively, the decompression plan can be generated through a combination of automatically generated recommendations and user input regarding, for example, the desired decompression protocol, the desired approach for performing the decompression protocol, and / or which part or parts of the bony anatomy and / or soft tissue are to be removed to correct the identified stenosis. As yet another alternative, the decompression plan can be generated based solely on user input.

[0080] The decompression plan can be a plan intended to be performed manually, such as by a surgeon using manually operated tools. Alternatively, the decompression plan can be a plan intended to be performed by a surgical robot or with the assistance of a surgical robot.

[0081] In some embodiments, step 214 can also include: determining a fusion plan based on the results of the assessment in step 212. For example, if the assessment determines that the planned decompression protocol will have little or no effect on the stability of the spine or on the stresses applied to the vertebrae of the spine, and / or if the assessment determines that the risk of iatrogenic instability is low, then no fusion plan may be prepared. Alternatively, if the assessment determines that the planned decompression protocol will have a material effect on the stability of the spine or on the stresses applied to the vertebrae of the spine, and / or if the assessment determines that the risk of iatrogenic instability is high, then a fusion plan may be prepared.

[0082] The fusion plan can be a plan for improving the predicted postoperative stability of the spine, whether by using bone grafts and / or spinal implants (whether plates for fixing two or more vertebrae together, rods fixed to pedicle screws inserted into the vertebrae to be fused, artificial discs, or discs or others). Like the decompression plan, the fusion plan can be automatically generated and then presented to the surgeon or other user for review, modification, and / or approval. Alternatively, the fusion plan can be generated through a combination of automatically generated recommendations and user input. As yet another alternative, the fusion plan can be generated based solely on user input.

[0083] Method 200 further includes: causing a decompression plan to be displayed on a monitor or other user interface in the operating room (step 216). The decompression plan can be displayed on a monitor, using an augmented reality overlay on a lens, or on other user interfaces in the operating room to facilitate the surgeon's execution of the decompression plan. The displayed decompression plan can be enhanced with surgical navigation and / or other systems to assist the surgeon in removing the correct bony anatomy and / or soft tissue from the patient's spine to correct stenosis, and / or accessing the bony anatomy and / or soft tissue causing the stenosis. In some embodiments, the displayed decompression plan will be manually executed, while in other embodiments, the displayed decompression plan will be automatically executed (e.g., by a surgical robot). In still other embodiments, the displayed decompression plan can be executed with robotic assistance.

[0084] In one or more embodiments, the decompression plan and / or fusion plan is determined according to the description provided in U.S. Patent Application No. 16 / 842,38, filed on April 7, 2020 (now U.S. Patent No. 11,450,435, issued on September 20, 2022), the entire content of which is incorporated herein by reference.

[0085] Method 200 further includes: receiving and processing postoperative image data (step 218). The received postoperative image data can be obtained from an imaging device 112 in the operating room or elsewhere used to image the patient's spine after the decompression procedure and / or from a database 114 in which such images are stored, and / or via the cloud 116. The received postoperative image data can be processed in any of the same or similar ways as described above for the processing of image data in step 202. The receiving and processing of the postoperative image data can occur while the patient is still in the operating room and can occur after the bony anatomy and / or soft tissue causing the identified stenosis has been removed.

[0086] Method 200 further includes: evaluating the stability of the spine based on the postoperative image data (step 220). The evaluation can be completed in any of the same or similar ways as described above for evaluating the stability of the spine based on the modified postoperative image data in step 212. Additionally, the result of the evaluation can be of the same or similar type as described above in connection with step 212. The evaluation can be performed while the patient is still undergoing surgery.

[0087] Method 200 also includes: displaying the surgical plan on a monitor or other user interface in the operating room (step 222). In the case where the result of the assessment in step 220 indicates a high risk of iatrogenic instability, or otherwise indicates that the decompression procedure has compromised spinal integrity in a manner that requires surgical implant placement or other corrective measures, method 200 can display the surgical or operative plan to the surgeon in the operating room to facilitate the surgeon's execution of the plan. The displayed surgical plan can be the surgical plan prepared in step 214, or a surgical plan prepared based on the result of the assessment in step 220. In the former case, the surgical plan can be modified by the surgeon or another user based on the result of the assessment in step 220. In the latter case, the surgical plan can be automatically generated, prepared using a combination of automated recommendations and user input, and / or prepared based solely on user input.

[0088] Method 200 advantageously allows the surgeon or other physician to respond in real-time or near real-time to the determined risk of iatrogenic instability caused by the decompression procedure. Method 200 thus advantageously avoids the situation where the patient undergoes a first decompression operation and then has to return to the operating room and / or hospital to undergo a second fusion operation. Method 200 also advantageously provides an objective measure of the risk of iatrogenic instability, thus helping to relieve the treating physician of the burden of making a subjective determination as to whether fusion is needed (whether at the time of the decompression procedure or immediately following the decompression procedure, or based on a subsequent diagnosis of iatrogenic instability).

[0089] Although described with respect to correcting compression of the spinal cord 304, method 200 can also be used in combination with correcting compression of the nerves 306 passing through or exiting.

[0090] Now turning to Figure 6 , a method 600 for assessing spinal integrity includes: receiving image data corresponding to the spine (step 602). Receiving image data corresponding to the spine can be done in the same or a similar manner as step 202 of method 600. For example, the image data can include or correspond to, for example, a three-dimensional image of the patient's spine, and can include data corresponding to a plurality of individual incisions, slices, or segments of the patient's spine, which together constitute the three-dimensional image of the spine. Additionally or alternatively, the image data can include or correspond to one or more two-dimensional images of the patient's spine. For example, preoperative image data can correspond to an image 300 such as Figure 3A and / or Figure 3BImages such as image 302. In cases where the image data comprises or corresponds to a plurality of two-dimensional images of a patient's spine, the plurality of two-dimensional images may be sufficient to construct or reconstruct a three-dimensional image or model of the spine. The preoperative image data may correspond to preoperative images of the patient's spine taken using an imaging device 112 (such as an MRI scanner, a CT scanner, or another imaging device). The preoperative image data may contain data for the entire spine of the patient or data for a portion of the patient's spine. The preoperative image data may be received from the imaging device 112, the database 114, the cloud 114, or any other source, and may be received via the communication interface 108.

[0091] Method 600 further includes: determining the material strength or stiffness of the bone anatomy in at least a portion of the spine (step 604). The material strength or stiffness may be determined by processing the image data (e.g., in one or more of the manners described above with respect to step 202 of method 200) and / or by analyzing metadata included with the image data. In cases where the image data corresponds to a plurality of slices (where the plurality of slices yield, for example, a 3D image and together produce a 3D image), determining the material strength or stiffness of the bone anatomy may include: determining the material strength or stiffness of the bone anatomy in each of the plurality of slices. The material strength or stiffness of a given portion of the bone anatomy in the image data may be determined by measuring the Hounsfield unit of that portion of the bone anatomy in the image data and / or by performing a bone mineral density test or a similar analysis. In some embodiments, the image data may include data corresponding to an imaging phantom having a known material strength or stiffness (or having different portions, each having a known material strength or stiffness), and the material strength or stiffness of a given portion of the bone anatomy may be determined by comparing the pixel intensity or other characteristics of the given portion of the bone anatomy with the pixel intensity or other corresponding characteristics of the image data corresponding to the phantom (or a portion thereof).

[0092] Method 600 further includes: completing a first stability assessment of the spine (step 606). This stability assessment can be completed in the same or a similar manner as the stability assessment completed in step 212 of method 200. For example, assessing the stability of the spine can include: running a virtual 6-degree-of-freedom analysis based on image data to evaluate the preoperative stability of the spine. This analysis can evaluate the stress applied to one or more vertebrae of the spine represented in the image data when the spine is in a neutral position and / or when the spine is in one or more flexion positions. The result of the virtual 6-degree-of-freedom analysis can be: a calculated level of spinal stability; an indication of the maximum stress applied to one or more vertebrae of the spine; an indication that the stress level of one or more vertebrae of the spine (even before any decompression procedure or other operation) exceeds a predetermined threshold; or any other indication related to the preoperative stability or instability of the spine represented in the image data.

[0093] In some embodiments, step 606 can include: generating a virtual three-dimensional model of the spine based on image data. The various anatomical elements of the spine included in the virtual three-dimensional model (or a portion thereof) can be assigned the material strength or stiffness determined for that anatomical element (or a portion thereof) in step 604. Then, a virtual 6-degree-of-freedom analysis can be performed on and / or using the virtual 3D model, with the same results as described above.

[0094] Method 600 further includes: modifying the image data to simulate the removal of bony anatomical structures and / or soft tissues from the spine (step 608). This modification can include: identifying the bony anatomical structures and / or soft tissues to be removed from the spine to correct the identified stenosis or other condition, which can be performed in the same or a similar manner as described above in connection with step 208 of method 200. For example, in some embodiments, an algorithm can be used to identify the bony anatomical structures and / or soft tissues that need to be removed to correct the stenosis condition. The bony anatomical structure can be, for example, a part of one or more vertebrae, and the soft tissue can be, for example, all or a part of an intervertebral disc, ligament, or other soft tissue.

[0095] In some embodiments, identifying the bone anatomy and / or soft tissue to be removed may include: receiving user input via a user interface (e.g., from a surgeon or other user), the user interface identifying the bone anatomy and / or soft tissue to be removed. For example, the user input may include a user selection of one of a laminectomy, laminotomy, foraminotomy, discectomy, or other procedure. Based on the user selection, a recommended portion of the bone anatomy and / or soft tissue may be automatically identified for removal, or the surgeon or other user may identify the portion of the bone anatomy and / or soft tissue to be removed. For example, if the user input is a laminectomy, the lamina proximate the identified stenotic vertebra may be automatically identified for removal, or the user may select the lamina of the vertebra for removal.

[0096] Typically, the bone anatomy or soft tissue that needs to be removed in a decompression procedure is not easily accessible to the surgeon. Thus, the surgeon must remove additional bone anatomy and / or soft tissue from the spine only to access the bone anatomy and / or soft tissue causing the stenosis. Then, in some embodiments, the identification of the bone anatomy and / or soft tissue to be removed includes not only identifying the bone anatomy and / or soft tissue that needs to be removed to correct the stenosis, but also identifying the bone anatomy and / or soft tissue that needs to be removed to access the bone anatomy and / or soft tissue causing the stenosis. The bone anatomy and / or soft tissue that must be removed to access the bone anatomy and / or soft tissue causing the stenosis may be automatically identified (e.g., by applying an algorithm selected based on the procedure to be completed and / or an algorithm predefined by a machine learning engine or otherwise) or identified via additional user input.

[0097] The identifying step 208 may further include: marking the identified bone anatomy and / or soft tissue in the image data (or, in the case where a virtual 3D model of the spine is being used, marking the identified bone anatomy and / or soft tissue in the virtual 3D model).

[0098] Once the bone anatomy and / or soft tissue to be removed has been identified, it may be modified in the same or a similar manner as step 210 of method 200. For example, modifying the image data to simulate the removal of the bone anatomy and / or soft tissue may include: replacing the identified bone anatomy and / or soft tissue in the image data with a virtual material having little or no material strength or stiffness. Alternatively, modifying the image data to simulate the removal of the bone anatomy and / or soft tissue may include: simply deleting the portion of the image data representing the bone anatomy and / or soft tissue to be removed. As another alternative, the virtual removal of the bone anatomy and / or soft tissue may include: assigning a Hounsfield unit measurement of zero to the portion of the image data representing the bone anatomy and / or soft tissue to be removed.

[0099] In an implementation where the virtual 3D model is used for the first stability assessment in step 606, step 608 may include: modifying the virtual 3D model (instead of the image data) to simulate the removal of bony anatomical structures and / or soft tissues from the spine. Simulating the removal of bony anatomical structures and / or soft tissues from the spine in the virtual 3D model may include: deleting only the portions of each vertebra, intervertebral disc, ligament, or other bony anatomical structure and / or soft tissue in the model that correspond to the bony anatomical structure and / or soft tissue to be removed.

[0100] Method 600 further includes: using the modified image data to complete a second stability assessment of the spine (step 610). The second stability assessment may be completed in the same or a similar manner as the first stability assessment in step 606, except that the second stability assessment is based on the modified image data (or, in an implementation where a virtual 3D model is being used, based on the modified virtual 3D model). The type of result of the second stability assessment may also be the same or similar to the type of result described above in connection with the first stability assessment.

[0101] Method 600 further includes: comparing the second stability assessment with the first stability assessment or a pre-determined threshold (step 612). The comparison may include: comparing the calculated preoperative stability level of the spine with the calculated or predicted postoperative stability level of the spine; comparing an indication of the preoperative maximum stress applied to one or more vertebrae of the spine with an indication of the predicted postoperative maximum stress to be applied to one or more vertebrae of the spine; or comparing any other indication related to the preoperative and postoperative stability of the spine represented in the image data and / or the virtual 3D model. In an implementation where the results of the first stability assessment and the second stability assessment each include an indication based on one or more pre-determined thresholds, the comparison may include: assessing whether the indication has remained the same (e.g., the stability of the spine is expected not to have changed), has changed to better, or has changed to worse. In other implementations where the results of the first stability assessment and the second stability assessment each include an indication based on one or more pre-determined thresholds, step 612 may not be required.

[0102] Method 600 also includes: displaying the result of the comparison via a user interface (step 614). The result of the comparison can be displayed via a user interface such as user interface 110. The result can be displayed as a number, a range, a color-coded indication (e.g., green for low risk of postoperative iatrogenic instability, yellow for medium risk of postoperative iatrogenic instability, and red for high risk of postoperative iatrogenic instability), displayed as text (e.g., indicating whether the postoperative risk of iatrogenic instability is high, medium, or low), displayed in graphical form (e.g., as a gauge showing multiple possible results, with an arrow or other marker indicating the actual result), displayed in any other way, and / or displayed in any combination of the foregoing ways. In embodiments where the second stability assessment includes comparing a calculated or measured value to a pre-determined threshold (e.g., to determine whether the postoperative risk of iatrogenic instability is high, medium, or low), information corresponding to the comparison (such as, for example, the result of the comparison and / or the information used for the comparison) can be displayed via the user interface.

[0103] Turning now to Figure 7 , a method 700 for assessing spinal integrity includes: receiving image data corresponding to the spine (step 702). Receiving image data corresponding to the spine can be done in the same or a similar manner as step 602 of method 600 and / or step 202 of method 200. In some embodiments, the image data can include a virtual three-dimensional model of the spine generated based on an actual image of the patient's spine. In other embodiments, step 702 can include: generating a virtual three-dimensional model of the spine based on the image data.

[0104] Method 700 also includes: automatically analyzing the image data to identify stenosis in the spine (step 704), or in embodiments where a virtual 3D model of the spine is generated based on the image data, automatically analyzing the virtual 3D model to identify stenosis in the spine. Stenosis can be identified in the central canal and / or the lateral recesses of the spine represented by the image data. Automatically analyzing the image data to identify stenosis in the spine can be done in the same or a similar manner as in step 204 of method 200. For example, in some embodiments, stenosis can be identified by comparing image data corresponding to a first portion of the spine to image data corresponding to a second portion of the spine, where the second portion of the spine is different from the first portion of the spine. As a more specific example, stenosis can be identified by comparing the spinal cord diameter at a first segment of the spine to the spinal cord diameter at one or more other segments of the spine, where the one or more other segments can be adjacent to the first segment. A sudden or rapid change in the diameter of the spine between adjacent or contiguous segments can indicate stenosis.

[0105] In other embodiments, stenosis can be identified by applying a predefined algorithm to the image data. The algorithm can be or can have been, for example, generated by a machine learning engine based on training data.

[0106] Method 700 further includes: receiving mobility data corresponding to an initial mobility assessment of the spine (step 706). The mobility data can be received together with or separately from the image data received in step 702. The mobility data can be based on a mobility assessment performed independently of the image data or on a mobility assessment of the image data. The mobility data can correspond to a 6-degree-of-freedom assessment and / or to an assessment of the maximum flexion / extension of the spine in the lateral plane and / or the anterior-posterior plane and / or about the vertical axis.

[0107] Method 700 further includes: modifying the image data to simulate the removal of bony anatomy and / or soft tissue from the spine (step 708). Modifying the image data can be done in the same or a similar manner as step 608 of method 600 and / or step 210 of method 200. Simulating the removal of bony anatomy and / or soft tissue from the spine can be based on the stenosis identified in step 704 and can involve removing the bony anatomy and / or soft tissue that is causing the identified stenosis. Simulating the removal of the bony anatomy and / or soft tissue can also involve removing the bony anatomy and / or soft tissue to enable access to the bony anatomy and / or soft tissue that is causing the identified stenosis.

[0108] In some embodiments, simulating the removal of bony anatomy and / or soft tissue from the spine can be based on user input received via a user interface (such as user interface 110) (e.g., from a surgeon or other user), the user interface identifying the bony anatomy and / or soft tissue to be removed. For example, the user input can include a user selection of one of a laminectomy, laminotomy, foraminotomy, discectomy, or other procedure. Based on the user selection, a recommended portion of the bony anatomy and / or soft tissue can be automatically identified for removal, or the surgeon or other user can identify the portion of the bony anatomy and / or soft tissue to be removed. For example, if the user input is a laminectomy, the lamina of the vertebrae proximate to the identified stenosis can be automatically identified for removal, or the user can select the lamina of the vertebrae for removal.

[0109] In some embodiments (e.g., where the image data does not include a virtual 3D model of the spine and step 702 does not include generating such a virtual 3D model), step 706 can include: generating a virtual three-dimensional model of the spine based on the image data. The various anatomical elements of the spine included in the virtual three-dimensional model (or a portion thereof) can be modified to simulate the removal of bony anatomy and / or soft tissue from the spine.

[0110] Method 700 further includes: generating an updated mobility assessment of the spine (step 710). The updated mobility assessment can be accomplished based on the modified image data (or modified virtual 3D model) generated from step 708. The updated mobility assessment can correspond to a six degrees of freedom assessment and / or an assessment of the maximum flexion / extension of the spine in the lateral plane and / or the anterior-posterior plane and / or about the vertical axis.

[0111] Based on the results of the updated mobility assessment of the spine that can be displayed or otherwise reported to the surgeon or other user, it can be determined whether the predicted risk of postoperative iatrogenic instability of the spine is high enough to justify planning and / or performing a spinal fusion procedure or other procedure to improve the stability of the spine. Thus, method 700 advantageously facilitates an objective evaluation of whether a decompression or other spinal procedure will likely result in iatrogenic instability and whether a spinal fusion or other procedure is needed or likely needed after the initial procedure to improve spinal stability.

[0112] Although the foregoing disclosure has focused primarily on correcting spinal cord compression and assessment, the systems and methods disclosed herein can also be used to correct compression of nerves passing through or exiting. In addition, the systems and methods disclosed herein can be used to assess the risk of iatrogenic instability in conjunction with any spinal procedure (not just decompression procedures designed to correct stenosis conditions).

[0113] Figure 8 Method 800 is illustrated for determining a plan for inserting an implant (e.g., a spinal implant) into a patient and then evaluating whether a particular spinal implant is suitable for use with that plan.

[0114] Initially, method 800 includes: receiving image data of the patient's spine (step 804). Receiving the image data in step 804 can be performed in the same or similar manner as described above with reference to Figures 1 to 7 (see, for example, steps 202, 602, and / or 702). As described herein, the image data can include one or more 2D images and / or one or more 3D images of the patient's spine. These images can be preoperative images.

[0115] Method 800 may include: determining a property of a bone anatomy in at least a first portion of a spine based on image data (step 808). Step 808 may include: processing the image data from step 804 according to the methods described herein to determine the property of the bone anatomy (see, e.g., steps 202, 604). For example, such processing may include: measuring, inferring, calculating, or otherwise identifying one or more properties of the bone anatomy and / or soft tissue represented in the image data. For example, the processing may include: determining a measurement of one or more vertebrae or portions thereof in Hounsfield units and converting the measurement into a bone mineral density value or other value representative of the strength or stiffness of the bone anatomy under consideration. As can be understood, the Hounsfield unit is a quantification of individual pixels in an image (e.g., a CT image). This quantification is compared to "air", where a higher Hounsfield unit number corresponds to a higher bone density. A higher bone density corresponds to a higher rigidity. Other suitable measurements or algorithms may be utilized to measure, infer, calculate, or otherwise determine the strength or stiffness of one or more anatomical elements represented in the image data. In some embodiments, the processing yields a determination of the material strength or stiffness for a plurality of vertebrae represented in the image data or for each vertebra represented in the image data. Also in some embodiments, modeling and processing performed on a computer yields a determination of the material strength or stiffness for a plurality of intervertebral discs represented in the image data or for each intervertebral disc represented in the image data. One or more properties of the bone anatomy may be measured at a plurality of points (e.g., within a vertebral body, within a pedicle, and / or on the surface of a vertebral endplate, etc.). In at least one embodiment, the property includes the rigidity of one or more portions of a vertebra. The rigidity of a portion of the bone anatomy, such as a vertebra, may have a corresponding Young's modulus value.

[0116] Method 800 may include: determining an initial plan for inserting an implant into the spine based on the property of the bone anatomy from step 808 (step 812). As described below with reference to Figures 9A to 10BMore particularly, the initial plan may include a screw trajectory for a screw implant and / or a location for placement of an intervertebral implant (also referred to as an interbody fusion device). The initial plan in step 812 may additionally or alternatively be determined based on one or more of the stability assessments described herein. For example, the aforementioned pre-operative stability assessment and predicted post-operative stability assessment may be used to determine characteristics of the screw implant (e.g., screw type, screw length, screw diameter, screw material, entry location, trajectory angle, etc.), and may assist in determining characteristics of the intervertebral implant (e.g., intervertebral implant type, material, and dimensions (length, width, height, surface angle) and / or intervertebral implant location, etc.). The stability assessment may further assist in determining the location of the implant in a manner predicted to result in a positive stability outcome. It is noted that the screw implant may be used with a rod connected to one or more other screws, and / or the screw implant may be used in conjunction with securing the intervertebral implant between two vertebral endplates.

[0117] Method 800 may include: virtually inserting an implant according to the initial plan using the image data to generate modified image data (step 816). As described in more detail below Figures 9A to 10B virtually inserting the implant may include: virtually inserting a screw along the determined screw trajectory and / or virtually inserting an intervertebral implant at the determined location. In either case, step 816 may further include: virtually removing spinal anatomy to make room for the implant (e.g., virtually removing bone anatomy and / or soft tissue in the vicinity of the location of the intervertebral implant and / or screw).

[0118] Then, method 800 may perform a first loading simulation on the virtual inserted implant using the modified image data (step 820). Generally speaking, the loading simulation performed in step 820 generates an output that can be used to determine whether the initial plan identified in step 812 is suitable for addressing the spinal condition being treated for a particular patient (see step 824). The loading simulation in step 820 may include using the modified image data from step 816 to simulate movement within a predefined cycle. For example, the modified image data containing information about at least one characteristic of the bone anatomy may be transformed into a computerized model of the spine with the virtual inserted implant, the virtual inserted implant having at least one known property, such as known rigidity, dimensions (length, width, height, surface angle), or other properties mentioned herein. Thereafter, the model may be subjected to virtual loading. In some examples, the virtual loading includes: simulating movement that may correspond to simulating a patient walking (e.g., a previously recorded gait cycle of the patient) and / or corresponding to a simulated bend of the spine in one or more directions. The loading simulation may simulate loading according to one or more degrees of freedom (e.g., loading with six degrees of freedom). In at least one example, the loading simulation in step 820 is performed according to the standards set by the American Society for Testing and Materials (ASTM) for spinal fusion products and procedures (such as the ASTM F-1717-04 standard). In some examples, the model is displayed on the display of the user interface 110 when subjected to virtual loading, and may be presented in a manner that highlights or otherwise visually indicates areas of concern and / or non-concern areas for the user to view. The model may also be interactive, enabling the user to adjust the model and / or the loading parameters in real time through user input to the user interface 110.

[0119] Method 800 includes: determining the suitability of the initial plan for implanting the implant based on the first loading simulation (steps 824 and 828). Generally speaking, steps 824 and 828 are performed to predict whether the implant inserted according to the initial plan in the final spinal procedure will achieve the stated or expected goals while reducing or minimizing the risk of negative effects. For example, steps 824 and 828 may be performed to predict whether inserting the implant according to the initial plan will have the expected consequences (e.g., relieve spinal stenosis or other conditions), while also preventing or minimizing the risk of unexpected implant displacement, preventing or minimizing the risk of the implant sinking into the bone anatomy or surrounding tissue, and / or preventing or minimizing the risk of the implant having an adverse effect on some other part of the spinal anatomy, etc.

[0120] In at least one example, steps 824 and 828 may include: comparing the properties of the bone anatomy at a particular point or location with the corresponding points or locations of the implant in contact with or close to the bone anatomy for the same properties. As described below with reference toFigures 9A to 10B As discussed, the loading simulations in steps 824 and 828 may include: comparing one or more properties of the bone anatomy from step 808 with one or more corresponding properties of the implant one or more times within a predefined period (e.g., a period defined by ASTM, a patient gait cycle, and / or a patient spinal curvature cycle, etc.). In an example where the property corresponds to rigidity, steps 824 and 828 may include: comparing the rigidity of points on the bone anatomy with the corresponding points of the implant in contact with or near the bone anatomy to determine whether the rigidity at these points sufficiently matches or is within the corresponding tolerance range. If the rigidity sufficiently matches the corresponding tolerance range and / or is within the corresponding tolerance range, the initial plan and the implant are determined to be suitable, and if not, the initial plan and the implant are determined to be unsuitable. The corresponding rigidity may be determined by the Young's modulus of the material being examined (e.g., the Young's modulus of the implant and / or the bone anatomy at a specific point). In another example, steps 824 and 828 include: determining whether any one or more limit values of the properties of the bone anatomy are exceeded at any time during the loading simulation. If the limit values are exceeded, the method determines in step 828 that the initial plan is unsuitable, and if the limit values are not exceeded, the initial plan and the implant are determined to be suitable. The evaluation of the loading simulation in steps 824 and 828 may be performed with the help of a machine learning engine that uses data from previous loading and / or stability simulations and / or real-world data to predict how the implant and / or the bone anatomy will respond to various mechanical stresses (e.g., compression, shear, rotation, tension, and / or similar stresses).

[0121] After determining in operations 824 and 828 that an implant inserted according to a specific plan (e.g., the initial plan) is suitable, method 800 generates a surgical plan for implanting the implant according to the suitable plan (step 832). The surgical plan may include a decompression plan and / or a fusion plan, and may be related to what was referred to above Figures 1 to 7are generated and displayed in the same or a similar manner (see, e.g., step 222). In steps 824 and 828, if the plan under consideration is determined to be unsuitable, method 800 determines an updated plan for inserting the implant into the spine (step 836). As can be appreciated, the updated plan includes at least one parameter that has been changed (updated) compared to the previously unsuitable plan (e.g., the initial plan). In the case where the implant includes screws and / or rods, the changed or updated parameters relate to screw type, screw diameter, screw position, implant reinforcement (e.g., further securing the implant with cement or other adhesives), the angle of the screw trajectory, rod size (length, diameter), rod material, rod angle, and / or the connection position of the screw on the rod, etc. In the case where the implant includes screws and / or rods, the changed or updated parameters relate to intervertebral implant type, material, and size (length, width, height, surface angle), and / or intervertebral implant position, etc.

[0122] Then, method 800 virtually inserts the implant according to the updated plan in the same or a similar manner as described above in step 816 (step 840), for example, before continuing with the updated loading simulation (step 844). The updated loading simulation can be performed in the same or a similar manner as in step 824, except that the updated loading simulation takes into account the parameters changed in step 836. Thereafter, method 800 returns to step 828 to determine whether the updated plan and the associated implant are suitable. The method iterates through steps 828, 836, 840, and 844 until a suitable plan is determined or until a stop condition is reached (e.g., time limit, a threshold number of iterations, and / or a threshold amount of processing power exceeded due to running the simulation, etc.).

[0123] Figures 9A to 10B relates to a specific implementation of method 800. As described in method 800 above, an implant for insertion into a patient's spine can include one or more screws inserted into the spine (e.g., pedicles and vertebral bodies) and connected to one or more rods and / or an intervertebral implant inserted and anchored (e.g., with screws) between two endplates. Figure 9A and Figure 9B illustrates method 900 for evaluating the suitability of a spinal implant including screws, while Figure 10A and Figure 10B illustrates method 1000 for evaluating the suitability of a spinal implant including an intervertebral implant (with or without screw anchoring). Figure 9B and Figure 10B respectively include Figure 9A and Figure 10AVisual representations of certain steps therein. Notably, methods 900 and 1000 can be performed independently of each other, in parallel with each other, or sequentially. Additionally, the steps of method 900 can be used in method 1000, and the steps of method 1000 can be used in method 900.

[0124] Now referring to Figure 9A and Figure 9B , method 900 includes: receiving first image data of a patient's spine (step 904). As can be understood, receiving the image data in step 904 can be performed in the same or a similar manner as described above with reference to Figures 1 to 8 . (See, for example, steps 202, 602, 702, and / or 804). For example, the image data can include one or more 2D images and / or one or more 3D images of the patient's spine. These images can be preoperative images of the patient and correspond to images obtained in a scan (such as a CT scan, an MRI scan, or other suitable scan). The first image data in step 904 can correspond to Figure 9B images 948 and / or 950 of a segment of the spinal cord in

[0125] Method 900 can include: determining the nature of a first bony anatomy in at least a first portion of the spine based on the first image data (step 908). The nature can be determined in the same or a similar manner as described with reference to Figures 1 to 8 . In one example, the nature includes a bone density measurement derived from the first image data, which can be transformed or indicative of the material strength and / or rigidity of the first bony anatomy. The bone density measurement can be in Hounsfield units (e.g., Figure 9B 124.0 units in Figure 9B ). Although only one measurement is shown in

[0126] Method 900 can include: determining an initial screw trajectory for inserting a screw into the spine based on the nature of the first bony anatomy (step 912). Generally speaking, step 912 selects a screw trajectory that may be suitable for the type of spinal correction surgery to be performed. When selecting the initial screw trajectory, the determination in step 912 can consider the rigidity (i.e., nature) of the first bony anatomy. For example, given the known or selected rigidity of a screw, step 912 can select a trajectory for the screw that avoids (or minimizes) regions of the first bony anatomy that have a lower rigidity than the screw.

[0127] The screw trajectory in step 812 can be additionally or alternatively determined based on one or more of the stability assessments described herein. For example, the aforementioned preoperative stability assessment and predicted postoperative stability assessment can help determine the specific locations of the screws that are predicted to result in positive postoperative stability outcomes.

[0128] Determining the screw trajectory can include: determining the position of the screw and characteristics of the screw other than the actual trajectory. Characteristics of the screw implant that can be determined or selected in step 912 include rigidity, screw type, screw length, screw diameter, screw material, entry location, and / or trajectory angle, etc. Step 912 can include: automatically or alternatively with user input selecting a screw trajectory from a plurality of possible screw trajectories based on historical information. For example, step 912 can select an initial screw trajectory with the help of a machine learning engine that uses data from previously loaded and / or stability simulations and / or real-world data to predict how the implant and / or bone anatomy will respond to various mechanical stresses (such as compression, shear, rotation, and / or tension, etc.) when inserted along the initial screw trajectory. As Figure 9B shown, in step 912, the screw trajectory can be superimposed on the image 950 (e.g., as the initial screw trajectory).

[0129] U.S. Application No. 17 / 671,117, filed on February 14, 2022 (published on September 8, 2022, as US2022 / 0280240), generally describes how a screw trajectory can be selected in step 912, which is hereby incorporated by reference in its entirety.

[0130] Method 900 can include: virtually inserting a screw along the initial screw trajectory using first image data to generate modified first image data (step 916), shown in Figure 9B as image 952 including the virtually placed screw.

[0131] Then, method 900 continues to perform an initial loading simulation on the virtually inserted screw using the modified first image data (step 920), and then determines the suitability of the initial screw trajectory for implanting the screw into the spine based on this initial loading simulation (steps 924 and 928). The loading simulation in step 920 can be performed in the same or a similar manner as described above with reference to Figure 8 For example, referring to Figure 9B , step 920 can construct a computerized model 954 (showing a schematic version) according to the modified first image data, which can include more images, screws, rods, etc. than depicted in image 952. In Figure 9BIn [the example], the model 954 includes screws 956 that are anchored at vertebral bodies 1 and 4 and are simultaneously connected to the rod 3. In some examples, the model 954 includes motion tracking markers 2 that can be virtually fixed to the screws 956 to facilitate tracking the movement of the screws during the loading simulation. Once the model 954 is constructed, step 920 can simulate the loading by inducing various types of stresses (compression, shear, tension, rotation, and / or similar stresses) on the screws, the rod, and / or the vertebrae. The model 954 illustrates an example with compressive forces, and in which vertebral bodies 1 and 4 can be tilted in the ventral and dorsal directions. However, the example implementation is not limited thereto, and the loading simulation can simulate other suitable forces on the screws, the rod, and / or the vertebrae.

[0132] Determining whether the initial screw trajectory is suitable in steps 924 and 928 can include steps that are the same as or similar to those described above with reference to Figure 8 the steps described in steps 824 and 828. Generally, steps 924 and 928 are performed to predict whether the implant inserted according to the initial screw trajectory in the final spinal procedure achieves the stated goals while avoiding or reducing or minimizing the risk of negative effects. For example, steps 924 and 928 can be performed to predict whether inserting an intervertebral implant according to the initial screw trajectory will have the expected consequences (e.g., play a role in relieving spinal stenosis or other conditions), while also preventing or minimizing the risk of unintended implant displacement, preventing or minimizing the risk of the implant sinking into the bone anatomy or surrounding tissue, and / or preventing or minimizing the risk that the implant has an adverse effect on some other part of the spinal anatomy, etc.

[0133] In at least one implementation, steps 924 and 928 can include comparing the properties of the bone anatomy at a specific point or location with the properties of the screw at the corresponding point or location that contacts or is close to the bone anatomy. In at least one example, the suitability determination includes performing the comparison during a simulated motion cycle of the spine (e.g., according to ASTM standards).

[0134] In some examples, the properties of the first bone anatomy are based on a bone density measurement of the first skeletal anatomy expressed in Hounsfield units. In one non-limiting example, the properties of the virtually inserted screw can include the rigidity of the virtually inserted screw (which is known for a given selected screw type), and the properties of the first bone anatomy can include the rigidity of the first bone anatomy derived or measured in step 908 (where the corresponding rigidity has a Young's modulus value). In an example where the properties being compared correspond to rigidity, steps 924 and 928 can include comparing the rigidity of a point on the bone anatomy with the corresponding rigidity of the implant at the point that contacts or is close to the bone anatomy to determine whether the rigidity at these points sufficiently matches and / or exceeds a limit value. Reference Figure 9B, a comparison can be performed between a set of points (points 1, 2, and 3) on the virtual inserted screw and a set of corresponding points on the first bone anatomy adjacent to points 1, 2, and 3 (e.g., points 1a, 2a, and 3a within 10 mm of points 1, 2, and 3, for example). Also as shown, the set of points includes at least one point 1a in the vertebral body and at least one point (points 2a and 3a) in the pedicle. More or fewer points can be evaluated as needed.

[0135] If the stiffness at each of the points 1, 2, 3 on the virtual inserted screw does not exceed the stiffness at each of the corresponding points 1a, 2a, 3a on the bone anatomy, the initial screw trajectory can be determined to be suitable. If the stiffness at one or more of the points 1, 2, and 3 exceeds the stiffness of the corresponding points 1a, 2a, 3a, the initial screw trajectory can be determined to be unsuitable. The corresponding stiffness can be determined by the Young's modulus of the material under consideration (e.g., the Young's modulus of the implant and / or the bone anatomy at points 1, 2, 3 and points 1a, 2a, and 3a). In another example, steps 924 and 928 include: determining whether one or more limit values of the properties of the bone anatomy are exceeded at any time during the loading simulation. If the limit values are exceeded, method 900 determines in step 928 that the initial plan and implantation are unsuitable. If the limit values are not exceeded, the initial plan and implantation are determined to be suitable. The evaluation of the loading simulation in steps 924 and 928 can be performed with the help of a machine learning engine that uses data from previous loading and / or stability simulations and / or real-world data to predict how the implant and / or the bone anatomy will respond to various mechanical stresses (e.g., compression, shear, rotation, and / or tension, etc.).

[0136] In at least one embodiment, steps 824 and 828 include: determining the location along the screw trajectory where the mechanical stress is highest. Stresses exceeding a threshold stress may be considered high stress points, and if the number of high stress points exceeds a threshold or if one of the high stress points exceeds a threshold, the screw trajectory may be determined to be unsuitable, otherwise the screw trajectory may be determined to be suitable. Steps 824 and 828 may also compare the mechanical stress along the screw trajectory to a standard benchmark of cancellous bone and / or cortical bone strength and determine that the screw trajectory is suitable when the mechanical stress does not exceed the benchmark, otherwise, the screw trajectory may be determined to be unsuitable. Further still, steps 824 and 828 may compare a dataset of similar patients to determine the risk of implant failure and determine the screw trajectory to be suitable when the risk is below a threshold and determine the screw trajectory to be unsuitable when the risk is above a threshold. In some examples, steps 824 and 828 consider multiple anchor points along a long malformed rod that acts as a level and increases the pullout force and determine the screw trajectory to be suitable or unsuitable based on this (e.g., a screw at S1 in a T2 to S1 fusion may have a large bending moment).

[0137] After determining in operations 924 and 928 that a screw inserted along a particular trajectory is suitable, method 900 generates a surgical plan for implanting the implant according to the suitable plan (step 932). The surgical plan may include a decompression plan and / or a fusion plan and may be generated and displayed in the same or a similar manner as described above with reference to Figures 1 to 8 (see for example step 222). In steps 924 and 828, if the screw trajectory under consideration is determined to be unsuitable, method 900 determines an updated screw trajectory for inserting the implant into the spine (step 936). As can be appreciated, the updated screw trajectory includes at least one parameter that has been changed (updated) compared to the previously unsuitable trajectory (e.g., the initial screw trajectory). The changed or updated parameter may relate to screw rigidity, screw type, screw diameter, screw position, implant reinforcement (e.g., further securing the implant with cement or other adhesive), the angle of the screw trajectory, and in the case of a rod, the rod size (length, diameter), rod material, rod angle, and / or the connection position of the screw on the rod, etc.

[0138] Then, method 900 continues to virtually insert the implant according to the updated screw trajectory in the same or a similar manner as described above in step 916 (step 940), for example, before proceeding with the updated loading simulation (step 944). The updated loading simulation can be performed in the same or a similar manner as in step 924, except that the updated loading simulation takes into account the parameters changed in step 936. Thereafter, method 900 returns to step 928 to determine whether the updated screw trajectory is suitable. The method iterates through steps 928, 936, 940, and 944 until a suitable screw trajectory is determined or until a stop condition (e.g., time limit, threshold number of iterations, and / or threshold amount of processing power, etc.) is reached.

[0139] Turning now to Figure 10A and Figure 10B , method 1000 involves evaluating the suitability of an intervertebral implant according to an example implementation. For purposes of explanation, the steps of method 1000 are described as if method 1000 is performed after method 900. However, the example implementation is not limited thereto, and methods 900 and 1000 can be performed independently of each other and / or in any order.

[0140] Method 1000 includes: receiving second image data of a patient's spine (step 1004). As can be understood, receiving the image data in step 1004 can be performed in the same or a similar manner as described above with reference to Figures 1 to 9B (see, for example, steps 202, 602, 702, 804, and / or 904). For example, the second image data may include one or more 2D images and / or one or more 3D images of the patient's spine. These images can be the patient's preoperative images and correspond to the images obtained in a scan (such as a CT scan, an MRI scan, or other suitable scan). The second image data in step 1004 may correspond to Figure 10B images 1050, 1052, and 1054 of segments of the spinal cord in

[0141] Method 1000 may include: determining the nature of a second bone anatomy in at least a second portion of the spine based on the second image data (step 1008). The nature of the second bone anatomy can be determined in step 1008 in the same or a similar manner as described with reference to Figure 1 through FIG. 9 (see, for example, steps 202, 604, 808, and / or 908). In one example, the nature includes a bone density measurement derived from the second image data, which can be transformed or indicative of the material strength and / or rigidity of the second bone anatomy. The bone density measurement can be expressed in Hounsfield units (e.g., Figure 10B 124.0 units in Figure 10BOnly one measurement is shown, but step 1008 may be performed multiple times on the region of the spine predicted to be affected by the intervertebral implant. In one example, the second bony anatomy corresponds to Figure 10B one or more portions of one or more vertebral endplates 1062.

[0142] Method 1000 may include: determining an initial position for inserting the intervertebral implant into the spine based on the properties of the second bony anatomy from step 1008 (step 1012). More specifically, step 1012 may consider the rigidity of the second bony anatomy when determining or selecting the initial position. For example, given a known or selected rigidity of an intervertebral implant, step 1012 may select an initial position of the intervertebral implant that avoids (or minimizes) regions of the second bony anatomy that have a lower rigidity than the intervertebral implant. Determining the initial position of the intervertebral implant may include determining the characteristics of the intervertebral implant in addition to determining the actual position of the intervertebral implant. Generally speaking, step 1012 selects the position and characteristics of an intervertebral implant that may be suitable for the type of spinal correction surgery to be performed. The aforementioned preoperative stability assessment and predicted postoperative stability assessment may also be used to determine the position of the intervertebral implant and / or the characteristics of the intervertebral implant. For example, the characteristics and / or position of the intervertebral implant are determined or selected in a manner predicted to result in positive postoperative stability results. Characteristics of the intervertebral implant that may be determined or selected in step 1012 include rigidity, intervertebral implant type, material, and / or dimensions (length, width, height, surface angle), etc.

[0143] Insofar as the intervertebral implant is to be fixed to the spine with one or more screws, step 1012 may further include: determining the screw trajectory according to Figure 8 the discussion of and FIG. 9 (e.g., steps 812 and 912), and determining the suitability of the screw trajectory for the intervertebral body implant by performing the same loading simulation described with reference to Figure 8 and FIG. 9, and generating a surgical plan.

[0144] Step 1012 may include: automatically or alternatively with user input selecting the position of the intervertebral implant from a plurality of possible positions based on historical information. For example, step 1012 may select the initial position of the intervertebral implant with the help of a machine learning engine that uses data from previous loading and / or stability simulations and / or real-world data to predict how the implant and / or bony anatomy will respond to various mechanical stresses (e.g., compression, shear, rotation, tension, and / or similar stresses) when inserted into the spine. As Figure 10B shown, in step 1012, the identified position may be superimposed on images 1050, 1052, and / or 1054 (shown only in Figure 10B image 1052 of ).

[0145] Method 1000 includes: virtually removing the spinal anatomy at the initial position using second image data to generate modified second image data (step 1016). For example, step 1016 is performed in the same or a similar manner as described above with reference to Figures 1 to 9B the manner described (see, for example, steps 208, 210, 608, 708, and / or Figures 4A to 5B ) to virtually remove the bone anatomy and / or soft tissue at the target segment corresponding to or near the initial position. In step 1016, the virtually removed spinal anatomy can be removed to create space for an implant (e.g., an intervertebral implant) and / or to assist in correcting a spinal condition (e.g., to relieve compression). Figure 10B The virtual removal of the anatomy (e.g., portions of the intervertebral disc and / or endplate) in images 1050 and 1054 is illustrated, and these images can correspond to the modified second image data.

[0146] Method 1000 can include: virtually inserting an intervertebral implant at the initial position using the modified second image data (step 1020). Figure 10B Image 1056 for virtually inserting an intervertebral implant is shown, and the position and characteristics of the intervertebral implant are determined in step 1012.

[0147] Then, method 1000 continues to perform a first loading simulation on the virtually inserted intervertebral implant (step 1024), and then determines the suitability of the intervertebral implant at the initial position based on this first loading simulation (steps 1028 and 1032).

[0148] The loading simulation in step 1024 can be performed in the same or a similar manner as described above with reference to, for example, Figure 8 the manner described. For example, referring to Figure 10B , step 1024 can construct a computerized model 1058 (showing a schematic version) based on the modified second image data with the virtually inserted intervertebral implant, and the modified second image data can include more images, intervertebral implants, screws, rods, etc. than depicted in image 1056. In Figure 10B , model 1058 includes an intervertebral implant (labeled "fuser") anchored between bone blocks with screws. Once model 1058 is constructed, step 1024 can simulate loading by inducing various types of stresses (compression, shear, tension, rotation, and / or similar stresses) on the intervertebral implant, screws, and / or vertebrae, where the response is detected by a virtual load cell or other suitable virtual load detector. As can be understood, the loading simulation can simulate any suitable forces on the intervertebral implant, screws, and / or vertebrae.

[0149] Determining whether the initial position is suitable in steps 1028 and 1032 may include steps that are the same as or similar to those described above with reference to Figure 8 steps such as those in steps 824 and 828. Generally speaking, steps 1028 and 1034 are performed to predict whether an implant inserted at a specific position and having selected characteristics in a final spinal procedure will achieve the stated goals while avoiding or reducing or minimizing the risk of negative effects. For example, steps 1028 and 1032 may be performed to predict whether inserting an intervertebral implant at the initial position will have the desired consequences (e.g., play a role in relieving spinal stenosis or other conditions), while also preventing or minimizing the risk of unintended implant displacement, preventing or minimizing the risk of the implant sinking into the bone anatomy or surrounding tissue, and / or preventing or minimizing the risk of the implant having an adverse effect on some other part of the spinal anatomy, etc.

[0150] In at least one embodiment, steps 1028 and 1032 may include: comparing the properties of the bone anatomy at a specific point or location with the properties of an intervertebral implant at a corresponding point or location that contacts or is close to the bone anatomy. In at least one example, the suitability determination includes performing the comparison within a simulated motion cycle of the spine (e.g., according to ASTM standards).

[0151] As described above, the properties of the second bone anatomy are based on bone density measurements of the second skeletal anatomy expressed in Hounsfield units. In one non-limiting example, the properties of the virtually inserted intervertebral implant may include the rigidity of the virtually inserted intervertebral implant (which is known or selected), and the properties of the second bone anatomy may include the rigidity of the second bone anatomy derived or measured in step 1008 (where the corresponding rigidity has a Young's modulus value). In an example where the properties being compared correspond to rigidity, steps 1028 and 1032 may include: comparing the rigidity of a point on the bone anatomy with the corresponding point of the implant at a point that contacts or is close to the bone anatomy to determine whether the rigidity at these points is sufficiently matched and / or exceeds a limit value. Reference Figure 10B, a comparison can be made between a set of points (points 1 and 2) on a virtual inserted intervertebral implant and a set of corresponding points on a second bone anatomy adjacent to points 1 and 2 (e.g., points 1a and 2a within, for example, 10 mm of points 1 and 2). Also as shown, the set of points includes at least one point 1a on the inferior vertebral endplate and at least one point 2a on the superior vertebral endplate. More or fewer points can be evaluated as needed. In at least one embodiment, steps 1028 and 1032 involve determining whether the angular change of the two vertebral bodies is within an acceptable range of the virtual inserted intervertebral implant, and if so, the method can proceed to step 1036, and if not, the method proceeds to step 1040. In some examples, steps 1028 and 1032 determine the primary force vector on the intervertebral implant during virtual insertion to evaluate the risk of implant expulsion, and if the risk is lower than a threshold risk, proceed to step 1036, or if the risk is higher than the threshold risk, proceed to step 1040.

[0152] If the rigidity at each of points 1 and 2 on the virtual inserted intervertebral implant does not exceed the rigidity of each corresponding point 1a and 2a on the bone anatomy, the initial position of the intervertebral implant can be determined to be suitable. If one or more of the rigidities at points 1 and 2 exceed the rigidities of the corresponding points 1a and 2a, the initial position can be determined to be unsuitable. The corresponding rigidities can be determined by the Young's modulus of the material being examined (e.g., the Young's modulus of the implant and / or the bone anatomy at points 1 and 2 and points 1a and 2a). In another example, steps 1028 and 1032 include: determining whether one or more limit values of the properties of the bone anatomy are exceeded at any time during the loading simulation. If the limit values are exceeded, method 1000 determines in steps 1028 and 1032 that the initial position is unsuitable. If the limit values are not exceeded, the initial position is determined to be suitable. The evaluation of the loading simulation in steps 1028 and 1032 can be carried out with the help of a machine learning engine that uses data from previous loading and / or stability simulations and / or real-world data to predict how the implant and / or the bone anatomy will respond to various mechanical stresses (e.g., compression, shear, rotation, and / or tension, etc.).

[0153] After determining in operations 1028 and 1032 that the position for inserting the intervertebral implant is suitable, method 1000 generates a surgical plan for implanting the implant according to the suitable position (step 1036). The surgical plan can include a decompression plan and / or a fusion plan, and can be generated and displayed in the same or a similar manner as described above with reference to Figures 1 to 9B (see, for example, step 222).

[0154] In steps 1028 and 1032, if the position of the intervertebral implant under consideration is determined to be unsuitable, method 1000 determines an updated position for inserting the intervertebral implant into the spine (step 1040). As can be appreciated, the updated position includes at least one parameter that has changed (been updated) compared to the previously unsuitable position (e.g., the initial position). The changed or updated parameter may relate to intervertebral implant rigidity, intervertebral implant type, material, and / or dimensions (length, width, height, surface angle), etc. In terms of screws involved in anchoring the intervertebral implant, the changed or updated parameter may additionally or alternatively relate to screw type, screw diameter, screw position, implant reinforcement (e.g., further securing the implant with cement or other adhesive), the angle of the screw trajectory, and, in the case of rods, rod dimensions (length, diameter), rod material, rod angle, and / or the connection position of the screw on the rod, etc.

[0155] Then, method 1000 virtually inserts the intervertebral implant according to the updated position in the same or a similar manner as described above in step 1016 (step 1044), for example, before proceeding with the updated loading simulation (step 1048). The updated loading simulation can be performed in the same or a similar manner as in step 1024, except that the updated loading simulation takes into account the parameters changed in step 1040. Thereafter, method 1000 returns to step 1032 to determine whether the updated screw trajectory is suitable. The method iterates through steps 1032, 1040, 1044, and 1048 until a suitable intervertebral implant position is determined or until a stop condition (e.g., time limit, threshold number of iterations, and / or threshold amount of processing power, etc.) is reached.

[0156] In view of the foregoing description, it should be understood that the example embodiments provide a preoperative assessment of implants such as screws and intervertebral implants to determine whether the implant is suitable for a particular procedure, thereby providing an automated and objective way to select implant characteristics, trajectories, and / or positions, which in turn improves surgical outcomes.

[0157] As can be understood based on the foregoing disclosure, the present disclosure encompasses methods having fewer steps than all of the steps identified in the drawings (and corresponding descriptions), as well as methods including steps from more than one of the drawings (and corresponding descriptions).

[0158] In some embodiments, one or more steps of any of the methods described herein may be repeated one or more times, for example, to allow a surgeon or other user to test the effects of multiple different procedures or specific implementations of a given procedure on predicted spinal integrity.

[0159] The foregoing discussion has been presented for purposes of illustration and description. The foregoing is not intended to limit the disclosure to one or more forms disclosed herein. In the foregoing detailed description, for example, for the purposes of simplifying the disclosure, various features of the disclosure are grouped together in one or more aspects, embodiments, and / or configurations. Features of the aspects, embodiments, and / or configurations of the disclosure may be combined in alternative aspects, embodiments, and / or configurations other than those discussed above. The methods of the disclosure should not be construed as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, aspects of the invention lie in less than all of the features of a single foregoing disclosed aspect, embodiment, and / or configuration. Accordingly, the following claims are hereby incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

[0160] In addition, although the description has included a description of one or more aspects, embodiments, and / or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure after understanding the disclosure, for example, as may be within the skill and knowledge of those in the art. It is intended to obtain rights to alternative aspects, embodiments, and / or configurations, including alternative, interchangeable, and / or equivalent structures, functions, scopes, or steps of those claimed, whether or not such alternative, interchangeable, and / or equivalent structures, functions, scopes, or steps are disclosed herein, and not to disclose any patentable subject matter.

Claims

1. A system for evaluating a spinal implant, the system comprising: at least one processor; and a memory storing instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: receive first image data of a patient's spine; determine a property of a first bone anatomy in at least a first portion of the spine based on the first image data; determine an initial screw trajectory for inserting a screw into the spine based on the property of the first bone anatomy; virtually insert the screw along the initial screw trajectory using the first image data to generate modified first image data; perform an initial loading simulation on the virtually inserted screw using the modified first image data; and determine the suitability of the initial screw trajectory for implanting the screw into the spine based on the initial loading simulation.

2. The system according to claim 1, wherein the suitability of the initial screw trajectory is determined based on a comparison that compares the property of the first bone anatomy with the property of the virtually inserted screw during a simulated motion cycle of the spine.

3. The system according to claim 2, wherein the property of the virtually inserted screw includes the rigidity of the virtually inserted screw, and wherein the property of the first bone anatomy includes the rigidity of the first bone anatomy.

4. The system according to claim 2, wherein the property of the first bone anatomy is based on a bone density measurement of the first skeletal anatomy expressed in Hounsfield units.

5. The system according to claim 2, wherein the comparison is performed between a set of points on the virtually inserted screw and a corresponding set of points on the first bone anatomy.

6. The system according to claim 5, wherein the set of points includes at least one point in a vertebral body of the spine and at least one point in a pedicle of the spine.

7. The system according to claim 1, wherein the memory stores additional instructions, the additional instructions when executed further causing the at least one processor to: when the initial screw trajectory is determined to be unsuitable, determine an updated screw trajectory for the screw, the updated screw trajectory having parameters changed compared to the initial screw trajectory; virtually insert the screw according to the updated screw trajectory; and perform an updated loading simulation on the virtually inserted screw inserted along the updated screw trajectory.

8. The system according to claim 7, wherein the changed parameters relate to screw type, screw diameter, screw position, implant reinforcement, or the angle of the screw trajectory.

9. The system according to claim 1, wherein the memory stores additional instructions, the additional instructions when executed further causing the at least one processor to: when the initial screw trajectory is determined to be suitable, generate a surgical plan for inserting the screw into the spine along the initial screw trajectory.

10. The system according to claim 1, wherein the memory stores additional instructions that, when executed, further cause the at least one processor to: Receive second image data of the spine of the patient; Determine the nature of a second bony anatomy in at least a second portion of the spine based on the second image data; Determine an initial position for inserting an intervertebral implant into the spine based on the nature of the second bony anatomy; Virtually remove a spinal anatomy at the initial position using the second image data to generate modified second image data; Virtually insert the intervertebral implant at the initial position using the modified second image data; Perform a first loading simulation on the virtually inserted intervertebral implant; And Determine the suitability of the intervertebral implant at the initial position based on the first loading simulation.

11. The system according to claim 10, wherein the memory stores additional instructions that, when executed, further cause the at least one processor to: Generate a surgical plan for implanting the intervertebral implant at the initial position when the initial position is determined to be suitable.

12. The system according to claim 10, wherein the nature of the second bony anatomy is based on a bone density measurement of the second skeletal anatomy expressed in Hounsfield units.

13. The system according to claim 10, wherein the first loading simulation includes performing a comparison that compares the nature of the second bony anatomy with the nature of the virtually inserted intervertebral implant during a simulated motion cycle of the spine.

14. The system according to claim 13, wherein the comparison is performed between a set of points on the virtually inserted intervertebral implant and a corresponding set of points on the second bony anatomy.

15. The system according to claim 14, wherein the second bony anatomy corresponds to an upper endplate, a lower endplate, or both, and wherein the spinal anatomy corresponds to at least a portion between the upper endplate and the lower endplate of the intervertebral disc.

16. A system for evaluating a spinal implant, the system Comprises: At least one processor; And A memory that stores instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: Receive image data of the spine of a patient; Determine the nature of a bony anatomy in at least a first portion of the spine based on the image data; Determine an initial plan for inserting an implant into the spine based on the nature of the bony anatomy; Virtually insert the implant according to the initial plan using the image data to generate modified image data; Perform a first loading simulation on the virtually inserted implant using the modified image data; And Determine the suitability of the initial plan for implanting the implant based on the first loading simulation.

17. The system according to claim 16, wherein the implant corresponds to an intervertebral implant, and wherein the intervertebral implant is virtually inserted between the upper endplate and the lower endplate.

18. The system according to claim 16, wherein the implant corresponds to a screw, and wherein the screw is virtually inserted into the pedicle and the vertebral body along a screw trajectory.

19. A system for evaluating a spinal implant, the system comprising: at least one processor; and a memory storing instructions for execution by the at least one processor, the instructions when executed causing the at least one processor to: receive first image data of a patient's spine; determine the nature of a first bone anatomy in at least a first portion of the spine based on the first image data; determine an initial position for inserting an intervertebral implant into the spine based on the nature of the first bone anatomy; virtually remove a spinal anatomy at the initial position using the first image data to generate modified first image data; virtually insert the intervertebral implant at the initial position using the modified first image data; perform a first loading simulation on the virtually inserted intervertebral implant; and determine the suitability of the intervertebral implant at the initial position based on the first loading simulation.

20. The system according to claim 19, wherein the memory stores additional instructions, the additional instructions when executed further causing the at least one processor to: receive second image data of the patient's spine; determine the nature of a second bone anatomy in at least a second portion of the spine based on the second image data; determine an initial screw trajectory for inserting a screw into the spine based on the nature of the second bone anatomy; virtually insert the screw along the initial screw trajectory using the second image data to generate modified second image data; perform a second loading simulation on the virtually inserted screw using the modified second image data; and determine the suitability of the initial screw trajectory for implanting the screw into the spine based on the second loading simulation.

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