Patient-specific artificial discs, implants and associated systems and methods

Abstract

To provide patient-specific artificial discs, implants and associated systems and methods.SOLUTION: Systems and methods for designing patient-specific medical devices are described herein. In some embodiments, a method includes obtaining patient data that includes image data and kinematic data of a patient's spine. A virtual model of the patient's spine is generated and can be manipulated until a target anatomical configuration is achieved. A patient-specific implant can then be designed based at least in part on the target anatomical configuration and the kinematics such that, when the patient-specific implant is implanted in the patient, the patient-specific implant provides the target anatomical correction while maintaining or improving the kinematics of the patient's spine.SELECTED DRAWING: Figure 1C

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Patent Application No. 16 / 987,113, filed August 6, 2020, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to orthopedic implants, and more particularly to systems and methods for designing and implementing patient-specific orthopedic implants. [Background technology]

[0003] Orthopedic implants are used to correct numerous different ailments in a variety of settings, including spinal surgery, hand surgery, shoulder and elbow surgery, total joint replacement (arthroplasty), cranial reconstruction, pediatric orthopedics, foot and ankle surgery, musculoskeletal oncology, surgical sports medicine, and orthopedic trauma. Spinal surgery itself can involve a variety of procedures and targets, such as one or more of the cervical, thoracic, lumbar, or sacrum, and is performed to treat spinal deformity or degeneration and / or associated back pain, leg pain, or other bodily pain. Common spinal deformities that can be treated using orthopedic implants include irregular spinal curvatures, such as scoliosis, lordosis, and kyphosis (high or low), as well as irregular spinal displacements (e.g., spondylolisthesis). Other spinal disorders that can be treated using orthopedic implants include osteoarthritis, lumbar or cervical degenerative disc disease, lumbar spinal stenosis, and cervical spinal stenosis. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] U.S. Patent Application Serial No. 16 / 735,222 Summary of the Invention [Means for solving the problem]

[0005] In some cases, orthopedic implants (e.g., artificial discs) are implanted into a patient's spine to restore spinal alignment while preserving spinal mobility. Disc replacement procedures can be performed on lumbar, thoracic, and cervical discs. Artificial cervical, thoracic, and lumbar discs can be surgically implanted to improve disc height, alignment, or mobility. For example, artificial discs can be used to improve or restore the relative position of vertebrae, establish proper foraminal height, provide nerve decompression, and allow relative motion between spinal segments. Unlike traditional implants used in spinal fusion procedures, artificial discs mimic the function of a patient's natural disc, allowing adjacent vertebrae to "move" relative to each other, maintaining a natural range of motion. To implant an artificial disc into a patient's spine, a physician can remove some or all of the patient's degenerated natural disc tissue. The physician can then insert the artificial disc in place of the removed natural disc tissue and affix it to the vertebrae using known techniques.

[0006] The accompanying drawings illustrate various embodiments of the systems, methods, and various other aspects of the present disclosure. Those skilled in the art will understand that the illustrated element boundaries (e.g., boxes, box groups, or other shapes) represent exemplary boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component of another element, and vice versa. A non-limiting and non-exhaustive description is provided with reference to the following drawings. The components in the figures are not necessarily to scale, with emphasis instead being placed on illustrating the principles. [Brief explanation of the drawings]

[0007] [Figure 1A] FIG. 10 is a schematic illustration of a patient-specific spinal implant positioned between vertebral bodies in a first configuration in accordance with an alternative embodiment of the present technology; [Figure 1B]FIG. 1B is a schematic diagram of the spinal implant of FIG. 1A in a second configuration. [Figure 1C] FIG. 10 is a schematic illustration of another patient-specific spinal implant positioned between vertebral bodies in a first configuration in accordance with an alternative embodiment of the present technology; [Figure 1D] FIG. 1D is a schematic diagram of the spinal implant of FIG. 1C in a second configuration. [Figure 2] FIG. 1 is a network connectivity diagram illustrating a system for providing patient-specific medical care, configured in accordance with an alternative embodiment of the present technology. [Figure 3] 2 illustrates a computing device suitable for use in connection with the system of FIG. 1, in accordance with an alternative embodiment of the present technology. [Figure 4] FIG. 10 is a flow diagram illustrating a method for designing a patient-specific implant, in accordance with an alternative embodiment of the present technology. [Figure 5] FIG. 10 is a flow diagram illustrating another method for designing a patient-specific implant in accordance with an alternative embodiment of the present technology. DETAILED DESCRIPTION OF THE INVENTION

[0008] Technology Overview The present technology relates to patient-specific medical device implants. For example, in many of the embodiments disclosed herein, the present technology provides systems and methods for designing, manufacturing, and / or providing patient-specific artificial discs (e.g., disc replacement devices, disc prostheses, spinal arthroplasty devices, etc.) for use during disc replacement procedures. The patient-specific artificial discs described herein can be specifically tailored to achieve one or more desired patient outcomes after implantation in a patient. For example, the patient-specific artificial disc can have a size and shape that corrects the patient's anatomy while also maintaining or improving the patient's motion characteristics. Thus, in some embodiments, the patient-specific artificial disc can improve or restore the relative position of adjacent vertebrae while also allowing a desired range of motion between adjacent vertebrae. Furthermore, the patient-specific artificial disc can have design features (e.g., shape, topography, etc.) configured to mate with the patient's particular anatomy to reduce the risk of migration and further improve patient outcomes.

[0009] In some embodiments, the patient-specific prosthetic implants described herein are designed using patient data to enhance implant performance. Patient data can include image data (e.g., anatomical data), kinematic data (e.g., motion data), medical history, patient information, etc. Anatomical data can include the shape and / or topography of anatomical features, spacing between adjacent anatomical features, and characteristics (e.g., tissue properties), etc. Kinematic data can include range of motion data (e.g., target range of motion data, pre-operative range of motion data, etc.) and other kinematic characteristics. Kinematic data can be collected by performing motion studies, modeling joint motion using software modules, or other techniques. Kinematic data can be associated with a subject joint or motion segment.

[0010] In some embodiments, the patient-specific prosthetic implants described herein are designed using one or more design criteria in addition to or in place of patient data. Design criteria may include, but are not limited to, target range of motion, target vertebral spacing (e.g., minimum vertebral body spacing), vertebral endplate topography, implantation procedure (e.g., access route or procedure), expected service life, patient-specific needs, regulatory requirements, etc. For example, the patient-specific prosthetic disc can be configured to match the intervertebral space, vertebral endplate topology, subject joint kinematics, or a combination thereof. In some procedures, the patient-specific prosthetic disc can be configured to preserve spinal motion and reduce the risk of complications. In other procedures, the patient-specific prosthetic disc can be configured to increase spinal motion. In some embodiments, the technology can incorporate predictive analytics, machine learning, neural networks, and / or artificial intelligence (AI) to determine improved or optimal surgical interventions and / or implant designs to achieve desired efficacy. For example, patient data can be used to generate a patient-specific artificial disc design that provides one or more joint characteristics (eg, range of motion, disc height, etc.).

[0011] In some embodiments, the present technology provides a method for providing a patient-specific implant. In certain embodiments, the method includes acquiring image data of one or more regions of a patient's spine, the image data representing the native anatomical configuration of the one or more regions of the patient's spine. The method further includes acquiring kinematic data associated with the one or more regions of the patient's spine. The kinematic data may include values ​​of one or more kinematic parameters, such as range of motion, flexion angle, rotation angle, translation, flexion, extension, flexion / extension arc, lateral bending, left / right flexion arc, and / or axial rotation. The method further includes determining a target anatomical configuration that differs from the native anatomical configuration. Thereafter, the patient-specific implant is designed based on the target anatomical configuration and the kinematic parameter values. Specifically, the patient-specific implant is designed to provide the target anatomical configuration while maintaining or improving the kinematic parameters when implanted in the patient.

[0012] In another specific embodiment, a computer-implemented method in accordance with the present technology includes receiving image data of one or more regions of a patient's spine, the image data representing a native anatomical configuration of the one or more regions. The method further includes measuring one or more kinematic parameters associated with the one or more regions and determining a target anatomical configuration that differs from the native anatomical configuration. A patient-specific implant is then designed based on the target anatomical configuration and the measured kinematic parameters. Specifically, the patient-specific implant is designed to provide the target anatomical configuration when implanted in the patient.

[0013] In some embodiments, a computer-implemented method for designing a patient-specific implant uses acquired patient data. The patient data can include one or more images, kinematic data, physician-entered data, etc. The images can depict the patient's native anatomical features. The kinematic data can relate to one or more regions and can include one or more specific values ​​of various kinematic parameters. The kinematic parameters can include range of motion, flexion angle, rotation angle, flexion / extension arc, left / right flexion arc, lateral bending, displacement, and other parameters related to flexion, extension, flexion, axial rotation, etc. under various conditions (e.g., weight-bearing, non-weight-bearing, etc.). The values ​​of the kinematic parameters can be determined based on images of the patient in different positions, or by measuring body position / movement, etc. In some embodiments, the values ​​of the kinematic parameters can be compared to target values ​​of the kinematic parameters. The target values ​​can be target range of motion, flexion angle, rotation angle, displacement, and / or other parameters related to flexion, extension, flexion, axial rotation, etc. A target anatomical configuration for one or more regions of the patient can also be determined. The target anatomical configuration can include adjustments to the native anatomical configuration of one or more anatomical features, including, but not limited to, interbody spacing, vertebral body orientation, alignment of two or more vertebral bodies, lumbar lordosis, Cobb angle(s), pelvic incidence, disc height, segmental flexibility, and rotational displacement, etc. At least a portion of the patient-specific implant can be designed based at least in part on the target anatomical configuration and the kinematic parameter values.

[0014] The computer-implemented method can include identifying anatomical features that impair physical motion. The computer-implemented method can generate a kinematic algorithm based on the identified features, and design an implant based on the kinematic algorithm to maintain a threshold amount of physical motion, maintain pre-treatment physical motion, and / or improve physical motion. In some embodiments, one or more predictive models can be used to determine predicted kinematics. The designer can update the predictive models. Identified anatomical features (e.g., stenosis, enlarged facet joints, bony overgrowth, cartilage loss, etc.) can be addressed with secondary procedures to further enhance or influence physical motion. The kinematic algorithm can model one or more spinal segments as a kinematic chain of links using constraints and boundary conditions to model segmental configuration, motion, range of motion, degrees of freedom, etc. For example, fixed links can represent fused vertebrae along the segment. Images of the patient's body in different positions and other patient data (including current and / or previous patients) can be used to automatically generate a virtual model for two-dimensional or three-dimensional analysis.

[0015] The patient-specific artificial discs described herein are expected to offer many advantages over conventional artificial discs. For example, the patient-specific artificial discs described herein may reduce the number of surgical steps required during implantation procedures. Conventional spinal implants, including artificial disc implants, are manufactured in standard shapes and sizes with standard flexion. Minimal consideration of implant size and other characteristics occurs prior to implantation procedures. Instead, surgeons select a particular implant from a surgical kit containing various sizes and shapes, with the patient's spine exposed during implantation procedures. Typically, surgeons select implant size through a technique known as "trialing," which uses a series of incrementally sized implant proxies or subcomponents to determine the appropriate implant size and shape. Trialing can be a time-consuming process, and surgeons typically focus only on the posterior height and sagittal angle of the implant, largely ignoring the lateral height and coronal angle of the implant. Because the patient-specific artificial discs described herein are already properly sized for the patient, the trialing process can be eliminated using this technique.

[0016] Patient-specific artificial discs can also eliminate the need for surgical kits containing a series of differently sized implants. As mentioned above, surgeons select traditional implants from a stock or kit of implants during surgery. Such kits require the shipping and delivery of enough implants to cover a variety of patients and their specific interbody spaces. Shipping, sterilizing, processing, and transporting a sufficient number of implants for a single procedure to the operating room is logistically laborious and expensive. For example, it is not uncommon for more than 50 implants to be provided for a procedure that requires only one. In addition to the logistical burden posed by these kits, the implants ultimately selected by the surgeon are still limited to those available in the surgical kits in the operating room. Therefore, by selecting stock implants from an assortment of prescribed implant sizes during surgery, surgeons are unable to provide patients with an optimal solution for correcting the specific spinal deformity or pathological misalignment that is causing their pain. Because patient-specific artificial implants are specifically designed to fit the patient, this technology can eliminate the need for surgical kits containing numerous implants.

[0017] Patient-specific artificial discs can be designed to further facilitate proper placement and reduce the number of implant failures by optimizing the implant's fit, mobility, flexibility, and / or other characteristics. Improper placement or sizing of a spinal implant can result in implant failure. For example, an improperly placed artificial disc can cause problems in other joints of the motion segment. In one instance, if an artificial disc is not properly positioned or sized, the associated facet joints can be overstressed and degenerate. Furthermore, insufficient contact and load transfer between the vertebrae and the implant can result in insufficient fixation between the implant and the anatomy. Insufficient fixation can result in movement of the implant relative to the vertebrae, leading to improper placement of the implant. Furthermore, insufficient contact area or fixation between the interbody implant and the vertebrae can result in micro- and / or macro-motion, reducing the chance of bone ingrowth and implant fusion. Sufficient movement can result in expulsion of the interbody implant or subsidence into the adjacent vertebrae. Thus, the patient-specific artificial discs described herein can be configured to limit stress (e.g., limit stress on vertebral bodies, facet joints, etc.), enhance fixation, provide a relatively large contact area, or facilitate placement to meet other design criteria. As will be appreciated by those skilled in the art from this disclosure, patient-specific artificial implants can provide additional advantages over conventional implants and implant procedures, regardless of whether such issues are addressed herein.

[0018] Thus, the present technology provides systems and methods for designing "patient-specific" or "personalized" medical devices, such as artificial intervertebral discs, that are expected to alleviate at least some of the drawbacks of conventional stock implants discussed above. Specifically, the present technology provides systems and methods for designing patient-specific implants optimized for a patient's specific characteristics (e.g., condition, anatomy, pathology, medical history, etc.). For example, a patient-specific medical device can be designed and manufactured specifically for a particular patient, rather than an off-the-shelf device. However, it should be understood that a patient-specific or personalized medical device can also include one or more components that are not patient-specific and / or can be used with instruments or tools that are not patient-specific. The personalized implant design can be used to manufacture or select patient-specific technology, including medical devices, instruments, and / or surgical kits. For example, a personalized surgical kit can include one or more patient-specific devices, patient-specific instruments, non-patient-specific technology (e.g., standard instruments, devices, etc.), instructions for use, patient-specific treatment planning information, or a combination thereof.

[0019] Embodiments of the present disclosure will now be described more fully with reference to the accompanying drawings, in which like numerals represent like elements throughout the several views and illustrate example embodiments. However, the claimed embodiments may be embodied in many different forms and should not be construed as limited to the embodiments described herein. The example described herein is a non-limiting example and is merely one example among many possible examples.

[0020] The words "comprising, having, containing, and including," and other forms of these words, are intended to be semantically equivalent and open-ended in that they do not imply that the item or items following any one of these words is an exhaustive list of such item or items, or that the item or items are limited to only the listed item or items.

[0021] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include their plural references unless the context clearly indicates otherwise.

[0022] While the disclosure herein primarily describes systems and methods for treatment planning in the context of orthopedic surgery, the technology is equally applicable to medical procedures and devices in other fields (e.g., other types of surgical practices). Also, while many embodiments herein describe systems and methods related to implanted devices, the technology is equally applicable to other types of medical devices (e.g., non-implanted devices).

[0023] Patient-specific implants 1A is a schematic illustration of an exemplary patient-specific artificial disc implant 80 (referred to as "implant 80") positioned between vertebral bodies 50, 60 (shown in cross section). Implant 80 includes a first (e.g., upper) endplate 82 and a second (e.g., lower) endplate 86. First endplate 82 can have an outward-facing surface 83 and an inward-facing surface 84. Similarly, second endplate 86 can have an outward-facing surface 87 and an inward-facing surface 88. Implant 80 also includes a core 90 (e.g., nucleus) positioned between first endplate 82 and second endplate 86 (e.g., extending between and coupled to inner surface 84 of first endplate 82 and inner surface 88 of second endplate 86).

[0024] When implant 80 is implanted in a patient, outer surface 83 of first end plate 82 engages the underside (e.g., inferior surface) 52 of first vertebral body 50, and outer surface 87 of second end plate 86 engages the upper side (e.g., superior surface) 62 of second vertebral body 60 below / caudal to first vertebral body 50. In some embodiments, outer surface 83 of first end plate 82 has a topology specifically tailored to mate with the topology of surface 52 of first vertebral body 50, and outer surface 87 of second end plate 86 has a topology specifically tailored to mate with the topology of surface 62 of second vertebral body 60. As used herein, the term "mating" can refer to two surfaces engaging with a reduced and / or minimized gap between them. For example, the outer surface 83 of the first end plate 82 can form a tight or nearly tight interface with the surface 52 of the first vertebral body 50, and the outer surface 87 of the second end plate 86 can form a tight or nearly tight interface with the surface 62 of the second vertebral body 60. Thus, the shapes of the first end plate 82 and the second end plate 86 can be designed based on the topology, shape, and features (e.g., ring apophysis, cortical rim, etc.) of the vertebral bodies that will interact during implantation. In the illustrated embodiment, for example, the peripheral edge of the second end plate 86 has a curved contour that matches the curvature of the ring apophysis 65, and the central region of the first end plate 82 is generally convex to match the concave portion of the central region of the first vertebral body 50. This provides a relatively large contact area that limits stress on the first and second vertebral bodies 50, 60, facilitating seating of the implant 80 and / or limiting or inhibiting migration of the implant 80. Thus, in some embodiments, the first endplate 82 and the second endplate 86 have different geometries and / or topographies to correspond to the different geometries and / or topographies of the first and second vertebral bodies. Without being bound by theory, it is expected that improving the fit between the endplates and the vertebral bodies will prevent and / or reduce instances of dynamic failure of the implant (e.g., by reducing and / or preventing micromotion of the implant) and / or increase the effectiveness of the implant.

[0025] As best seen in FIG. 1B , core 90 allows first endplate 82 and / or second endplate 86 to pivot or otherwise rotate relative to one another to accommodate movement between first vertebral body 50 and second vertebral body 60. Accordingly, core 90 may also be referred to as a “motion segment.” As described in further detail below with respect to FIGS. 4 and 5 , core 90 can be designed with appropriate orientation, rotation, flexion, and / or translation to allow first vertebral body 50 to move relative to second vertebral body 60 according to one or more desired kinematic parameters. The degree and type of movement permitted by core 90 can be based on a number of factors, including, but not limited to, the composition of the core, the interface between the mating surfaces, and / or the geometry of the core (e.g., contour, shape, diameter, etc.). Core 90 can be formed of any suitable material, including, but not limited to, elastomeric polymers, rigid polymers, hybrid materials having elastomeric and rigid properties, ceramics, metals, and combinations thereof. The core 90 can also include multiple mating surfaces that provide the determined kinematics. For example, the core 90 can be a ball-and-socket joint, a dome-cup joint, or the like. As another example, the core 90 can include one or more biasing members, springs, sliding members / interfaces, or other resilient feature(s). As will be appreciated by those skilled in the art, in some embodiments, the core 90 can be omitted, and the first endplate 82 and / or the second endplate 86 can be configured to impart motion to the implant 80. For example, the first endplate 82 can form an interface with the second endplate 86 that at least partially defines the motion segment of the implant (e.g., an articulating interface). In such embodiments, the interface between the first endplate 82 and the second endplate 86 can be any suitable interface that allows motion between the two components, including, but not limited to, a ball-and-socket interface, a dome-cup interface, a sliding interface, a rolling interface, or the like. In some embodiments, the inner surface 84 of the first endplate 82 directly engages the inner surface 88 of the second endplate 86 to form an interface that defines the motion segment.

[0026] In some embodiments, a motion segment (e.g., core 90 or the interface between first endplate 82 and second endplate 86) can include one or more patient-specific features that provide patient-specific kinematics. Patient-specific features can be selected based on the desired kinematics (e.g., degrees of freedom, type of motion, etc.) and can include joint types, number of core members / layers, interface characteristics (e.g., between core members / layers), mating surface contours, limiting elements (e.g., limiting struts), etc. As described in detail below, patient data can be analyzed to configure selected patient-specific features for the desired kinematics. If the desired range of motion is not achieved, additional or different patient-specific features can be selected until the target kinematics (e.g., range of motion, type of motion, etc.) are achieved. Thus, different components of implant 80 can be designed based on different selected design criteria.

[0027] 1C and 1D show another patient-specific artificial disc implant 80a (referred to as "implant 80a") configured in accordance with embodiments of the present technology. Similar to implant 80, implant 80a can be configured to be positioned between a first vertebral body 50a and a second vertebral body 60a. Implant 80a can include a first end plate 82a and a second end plate 86a. First end plate 82a can include an outer surface 83a designed to mate with surface 52a of the first vertebral body 50a. Second end plate 86a can include an outer surface 87a designed to mate with surface 62a of the second vertebral body 60a. Core 90a can allow first end plate 82a and / or second end plate 86a to pivot or otherwise rotate relative to one another to accommodate movement between first vertebral body 50a and second vertebral body 60a.

[0028] As those skilled in the art will appreciate from the disclosure herein, implants 80, 80a are provided as simple, schematic examples of patient-specific artificial discs. The patient-specific implants described herein are designed to fit the individual patient's anatomy, and the size, shape, and geometry of the patient-specific implant will vary according to the individual patient's anatomy. Thus, the present technology is not limited to any particular artificial disc design or configuration, and thus may include artificial disc implants other than those shown or described herein, including other disc or joint replacements not expressly described herein.

[0029] Artificial disc implant design and manufacturing system 2 is a network connectivity diagram illustrating a computer system 200 for providing a patient-specific device in accordance with embodiments of the present technology. System 200 can include, among other things, a computer device 202, a communication network 204, a server 206, a display 222, and a manufacturing system 224. As described in further detail below, system 200 can be used to design patient-specific medical devices, such as patient-specific artificial discs, that conform to the native patient anatomy and / or target anatomical configuration while also replicating and / or approximating healthy or "normal" joint kinematics. Thus, in at least some embodiments, system 200 can be used as part of a treatment plan to address degenerative disc disease or another disorder requiring disc replacement.

[0030] Computing device 202 may be a user device such as a smartphone, mobile device, laptop, desktop, personal computer, tablet, phablet, or other such device known in the art. As further described herein, computing device 202 may include one or more processors and memory that stores instructions executable by the one or more processors to perform the methods described herein. Computing device 202 may be associated with a healthcare provider treating a patient. While FIG. 1 depicts a single computing device 202, in another embodiment, computing device 202 may be implemented as a client computing system including multiple computing devices, such that the operations described herein with respect to computing device 202 may instead be performed by a computing system and / or multiple computing devices.

[0031] The computing device 202 is configured to acquire (e.g., receive, determine, etc.) a patient dataset 208 related to a patient to be treated. The patient dataset 208 may include image data and / or kinematic data of the patient's spine. The image data may include, for example, magnetic resonance imaging (MRI) images, ultrasound images, computer-aided tomography (CAT) images, positron emission tomography (PET) images, x-ray images (e.g., biplanar x-rays), camera images, etc. The image data may be indicative of patient anatomy, such as the geometry, orientation, and topography of various anatomical features. For example, in some embodiments, the image data may be indicative of (and / or used to determine) vertebral spacing, vertebral orientation, vertebral translation, abnormal bone growth, abnormal joint growth, arthritis, joint degeneration, tissue degeneration, stenosis, scar tissue, lumbar lordosis, Cobb angle(s), pelvic intrinsic angle, disc height, segmental flexibility, rotational displacement, and other spinal tissue characteristics. For example, the kinematic data may include specific values ​​or other data corresponding to one or more kinematic parameters, such as specific values ​​or other data corresponding to a range of motion in three dimensions (e.g., including flexion, extension, bending, etc.), a flexion / extension arc, a left / right flexion arc, a lateral bending, a flexion angle, a rotation angle, and a displacement, etc. The kinematic data may be acquired under various conditions (e.g., weight-bearing, etc.). The values ​​of the kinematic parameters may be determined based on images of the patient in different positions, measurements of body position / body motion, etc. For example, characteristics of bone kinematic relationships may be determined by imaging the patient during motion (e.g., X-rays, MRIs, CAT scans, etc.) and analyzing the patient's morphology based on the images. In some embodiments, the range of motion may be defined as a spherical range of motion, in which one vertebra moves spherically relative to another vertebra. In other embodiments, the range of motion may be defined as a more complex range of motion defined by a three-dimensional curve through space. In some embodiments, as described in further detail below, the system 200 is configured to determine the kinematic data based on image data.In such an embodiment, the patient data set 208 received by the computing device 202 does not necessarily include kinematic data.

[0032] In addition to image data and / or kinematic data, the patient dataset 208 may also include, but is not limited to, medical history, surgical intervention data, treatment outcome data, progress data (e.g., physician's notes), patient feedback (e.g., quality of life questionnaires, feedback obtained using surveys), clinical data, provider information (e.g., physician, hospital, surgical team), patient information (demographics, gender, age, height, weight, pathology type, occupation, activity level, tissue information, health assessment, comorbidities, health-related quality of life (HRQL)), vital signs, diagnosis results, medication information, allergies, diagnostic equipment information (e.g., manufacturer, model number, specifications, user-selected settings / configuration, etc.), or any combination thereof. In some embodiments, the patient dataset 208 includes data representing one or more of a patient identification number (ID), age, gender, body mass index (BMI), lumbar lordosis, Cobb angle(s), pelvic intrinsic angle, disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level.

[0033] The computing device 202 may include or be operably coupled to a display 222 for providing output to a user (e.g., a clinician, surgeon, healthcare provider, patient). In some embodiments, the display 222 may include a graphical user interface (GUI) for visually presenting a virtual model 230 of one or more regions of the patient's anatomy based on the patient dataset 208. The virtual model 230 may be a 2D model, a 3D model, a CAD model, or other suitable model that provides a virtual representation of the patient's anatomy. The one or more regions may include, but are not limited to, the patient's spinal region (e.g., cervical, thoracic, lumbar, and / or sacrum). For example, in one embodiment, the target site may be a segment of the patient's spine between C6 and C3. In such an embodiment, the virtual model 230 may include the individual vertebrae between C6 and C3 and other associated anatomical structures, such as the intervertebral discs between the vertebrae. In other embodiments, the virtual model may include a model of the patient's entire spine (or generally the entire spine), rather than just a specific segment. In some embodiments, generating the virtual model 230 from the image data includes reconstructing two-dimensional image data including pixels into three-dimensional volumetric data including voxels representing patient anatomy. In some embodiments, the image data and / or the virtual model can be segmented to better visualize individual anatomical features. Segmentable anatomical features can be any anatomy of interest, such as bones, discs, organs, etc. For example, in some embodiments, bony anatomy (e.g., vertebrae) is segmented from other anatomy so that individual bony structures (e.g., vertebrae) are visible in isolation. In some embodiments, the display 222 includes a touch screen or other input module that allows a user to optionally manipulate the virtual model 230.

[0034] The computing device 202 may be operatively connected to a server 206 via a communications network 204, thus enabling data transfer between the computing device 202 and the server 206. The communications network 204 may be a wired and / or wireless network. If the communications network 204 is wireless, it may be implemented using communications technologies such as visible light communications (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), wireless local area network (WLAN), infrared (IR) communications, public switched telephone network (PSTN), radio waves, and / or other communications technologies known in the art.

[0035] Server 206, which may also be referred to as a "treatment assistance network" or a "prescriptive analytics network," may include one or more computer devices and / or systems. As further described herein, server 206 may include one or more processors and memory that stores instructions executable by the one or more processors to perform the methods described herein. In some embodiments, server 206 is implemented as a distributed "cloud" computer system or facility across any suitable combination of hardware and / or virtual computing resources.

[0036] The computing device 202 and the server 206 may individually or collectively perform various methods for providing patient-specific medical care described herein. For example, some or all of the steps of the methods described herein may be performed by the computing device 202 alone, by the server 206 alone, or by a combination of the computing device 202 and the server 206. Thus, while some operations are described herein with respect to the server 206, it should be understood that these operations may also be performed by the computing device 202, and vice versa.

[0037] The server 206 includes at least one database 210 configured to store reference data useful for the treatment planning methods described herein. The reference data may include historical and / or clinical data from the same or other patients, data collected from previous surgeries and / or other treatments of the patient by the same or other healthcare providers, data related to medical device designs, data collected from research or survey groups, data from clinical databases, data from academic institutions, data from implant or other medical device manufacturers, data from imaging studies, data from simulations, clinical trials, demographic data, treatment data, outcome data, or mortality rates, etc.

[0038] In some embodiments, database 210 includes multiple reference patient data sets, each associated with a corresponding reference patient. For example, the reference patients can be patients who have previously been treated or patients currently undergoing treatment. Each reference patient data set can include data representing the corresponding reference patient's condition, anatomy, pathology, kinematics, medical history, preferences, and / or any other information or parameters associated with the reference patient, such as any of the data described herein with respect to patient data sets 208. In some embodiments, a reference patient data set includes pre-operative data, intra-operative data, and / or post-operative data. For example, a reference patient data set can include data representing one or more of anatomical data, kinematic data, motion data, patient ID, age, sex, BMI, lumbar lordosis, Cobb angle(s), pelvic intrinsic angle, disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level.

[0039] In some embodiments, the server 206 receives at least some of the reference patient datasets from multiple healthcare provider computer systems. Each healthcare provider computer system can include at least one reference patient dataset (e.g., multiple reference patient datasets) associated with reference patients treated by the corresponding healthcare provider. The reference patient datasets can include, for example, kinematic records, electronic medical records, electronic health records, biomedical datasets, etc.

[0040] As described in further detail herein, the server 206 can be configured with one or more algorithms to generate patient-specific treatment planning data (e.g., patient-specific treatment procedures, patient-specific implants) based on the baseline data. In some embodiments, the patient-specific data is generated based on a correlation between the patient dataset 208 and the baseline data. Optionally, the server 206 can predict outcomes including recovery time, efficacy based on clinical endpoints, likelihood of success, predicted mortality, predicted relevant follow-up surgeries, etc. In some embodiments, the server 206 can continuously or periodically analyze patient data (including patient data obtained during the patient stay) to determine near-real-time or real-time risk scores, mortality predictions, etc.

[0041] In some embodiments, the server 206 includes one or more modules for performing one or more steps of the patient-specific treatment planning methods described herein. For example, in the illustrated embodiment, the server 206 includes a data analysis module 216 and a treatment planning or implant design module 218. In other embodiments, one or more of these modules may be combined with one another or omitted. Thus, although some operations are described herein with respect to a particular module or modules, this is not intended to be limiting, and in other embodiments, such operations may be performed by a different module or modules.

[0042] The data analysis module 216 is configured using one or more algorithms to identify a subset of reference data from the database 210 that is likely to be useful in developing a patient-specific treatment plan. For example, the data analysis module 216 can compare patient-specific data (e.g., the patient dataset 208 received from the computing device 202) with reference data (e.g., a reference patient dataset) from the database 210 to identify similar data (e.g., one or more similar patient datasets within the reference patient dataset). This comparison can be based on one or more parameters, such as age, gender, BMI, pathology, kinematics, lumbar lordosis, pelvic intrinsic angle, and / or treatment level. These parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient dataset 208 and the reference patient dataset. Thus, similar patients can be identified based on whether the similarity score is above, below, or equal to a specified threshold. For example, as described in more detail below, the comparison can be performed by assigning a value to each parameter to determine an overall difference between the patient of interest and each reference patient. Reference patients whose overall difference is below a threshold can be considered similar patients.

[0043] The data analysis module 216 may be further configured with one or more algorithms to select a subset of the reference patient dataset based on, for example, similarity to the patient dataset 208 and / or the treatment outcomes of the corresponding reference patients. For example, after identifying one or more similar patient datasets within the reference patient dataset, the data analysis module 216 may select the subset of similar patient datasets based on whether the similar patient datasets contain data indicative of a favorable or desirable treatment outcome. The outcome data may include data indicative of one or more outcome parameters, such as corrected anatomical metrics, range of motion, kinematic data, HRQL, activity level, complications, recovery time, efficacy, mortality, or follow-up surgery. As described in more detail below, in some embodiments, the data analysis module 216 calculates an outcome score by assigning a value to each outcome parameter. A patient may be considered to have had a favorable outcome if the outcome score is above, below, or equal to a specified threshold.

[0044] In some embodiments, the data analysis module 216 selects a subset of the reference patient dataset based at least in part on user input (e.g., from a clinician, surgeon, physician, or healthcare provider). For example, the user input can be used in identifying similar patient datasets. In some embodiments, the healthcare provider or physician can select weightings for the similarity and / or outcome parameters to adjust the similarity and / or outcome score based on the clinician input. In further embodiments, the healthcare provider or physician can select the similarity and / or outcome parameter sets (or define new similarity and / or outcome parameters) used to generate the similarity and / or outcome score, respectively.

[0045] In some embodiments, the data analysis module 216 includes one or more algorithms used to select sets or subsets of reference patient datasets based on criteria other than patient parameters. For example, these one or more algorithms can be used to select subsets based on provider parameters (e.g., based on provider rankings / scores, such as hospital / physician expertise, number of procedures performed, hospital rankings, etc.) and / or medical resource parameters (e.g., diagnostic equipment, facility, surgical equipment, such as surgical robots), or other non-patient-related information that can be used to predict outcomes and risk profiles for current provider procedures. For example, reference patient datasets containing images captured from similar diagnostic equipment can be aggregated to reduce or limit irregularities due to diagnostic equipment variability. Data from similar providers (e.g., providers with traditionally similar outcomes, physician expertise, surgical teams, etc.) can also be used to create patient-specific treatment plans for a particular provider. In some embodiments, reference provider datasets, hospital datasets, physician datasets, surgical team datasets, post-treatment datasets, and other datasets can be utilized. As one example, a patient-specific treatment plan for performing a battlefield surgery can be based on reference patient data from similar battlefield surgeries and / or datasets related to battlefield surgeries. In another example, a patient-specific treatment plan can be generated based on available robotic surgical systems. The reference patient dataset can be selected based on patients operated on using comparable robotic surgical systems under similar conditions (e.g., surgical team size and capabilities, hospital resources, etc.).

[0046] The implant design module 218 is configured using one or more algorithms to generate at least one treatment plan (e.g., pre-operative plan, surgical plan, post-operative plan, etc.) and / or implant design based on, for example, output from the data analysis module 216. In some embodiments, the implant design module 218 is configured to build and / or implement at least one predictive model for generating a patient-specific treatment plan, also known as a "prescriptive model." The predictive model(s) can be built using clinical knowledge, statistics, machine learning, AI, neural networks, or the like. In some embodiments, the output from the data analysis module 216 is analyzed (e.g., using statistics, machine learning, neural networks, AI, etc.) to identify correlations between data sets, patient parameters, healthcare provider parameters, healthcare resource parameters, treatment protocols, medical device designs, and / or treatment outcomes. These correlations can be used to build at least one predictive model that predicts the likelihood that a treatment plan will result in a favorable outcome for a particular patient. The predictive model(s) can be validated, for example, by inputting data into the model(s) and comparing the model's output to an expected output.

[0047] In some embodiments, the implant design module 218 is configured to generate an implant design based on previous treatment data from a reference patient. For example, the implant design module 218 can receive a selected subset of the reference patient dataset and / or similar patient dataset from the data analysis module 216 and determine or identify treatment data from the selected subset. The treatment data can include, for example, range of motion and / or other kinematic data associated with a favorable or desired treatment outcome for the corresponding patient, treatment procedure data (e.g., surgical procedure or intervention data), and / or medical device design data (e.g., implant design data). The implant design module 218 can analyze the treatment procedure data and / or medical device design data to determine an optimal treatment protocol for the patient being treated. For example, values ​​can be assigned to the treatment procedures and / or medical device designs, which can be aggregated to generate a treatment score. A patient-specific treatment plan can be determined by selecting a treatment plan(s) based on a score (e.g., higher or highest score, lower or lowest score, score above, below, or the same as a specified threshold). The personalized patient-specific treatment plan can be based at least in part on a patient-specific technique or a patient-specific selected technique.

[0048] Alternatively or in combination, the implant design module 218 may generate an implant design based on correlations between data sets. For example, the implant design module 218 may correlate an implant design with medical device design data from implant designs for similar patients with favorable outcomes (e.g., identified by the data analysis module 216). The correlation analysis may include converting the correlation coefficient values ​​into values ​​or scores. These values / scores may be aggregated, filtered, or otherwise analyzed to determine one or more statistical significances. These correlations may be used to determine treatment procedure(s) and / or medical device design(s) likely to result in optimal or favorable outcomes for the treated patient.

[0049] Alternatively or in combination, the implant design module 218 may generate designs using one or more AI techniques. AI techniques can be used to build computer systems that can simulate aspects of human intelligence, such as learning, reasoning, planning, problem-solving, and decision-making. AI techniques may include, but are not limited to, case-based reasoning, rule-based systems, artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks (e.g., naive Bayes classifiers), genetic algorithms, cellular automata, fuzzy logic systems, multi-agent systems, swarm intelligence, data mining, machine learning (e.g., supervised learning, unsupervised learning, reinforcement learning), and hybrid systems.

[0050] In some embodiments, the implant design module 218 generates the treatment plan using one or more trained machine learning models. Various types of machine learning models, algorithms, and techniques are suitable for use with the present technology. In some embodiments, the machine learning model is first trained based on a training dataset, which is a set of examples used to adapt the model's parameters (e.g., the weights of the connections between "neurons" in an artificial neural network). For example, the training dataset may include any of the reference data stored in the database 210, such as multiple reference patient datasets or a selected subset thereof (e.g., multiple similar patient datasets).

[0051] In some embodiments, a machine learning model (e.g., a neural network or a naive Bayes classifier) ​​can be trained based on a training dataset using supervised learning methods (e.g., gradient descent or stochastic gradient descent). The training dataset can include pairs of generated "input vectors" and associated corresponding "answer vectors" (commonly represented as targets). A current model is run on the training dataset to generate results, which are compared to the targets for each input vector in the training dataset. Model parameters are adjusted based on the results of the comparison and the particular learning algorithm used. Model fitting can include both variable selection and parameter estimation. The fitted model can be used to predict responses for observations in a second dataset, called the validation dataset. The validation dataset can provide an unbiased evaluation of a model fitted to the training dataset while tuning the model parameters. The validation dataset can be used for early stopping regularization, for example, by stopping training when the error on the validation dataset increases, which is considered a sign of overfitting to the training dataset. In some embodiments, the error in the validation dataset can fluctuate during training, and therefore ad hoc rules can be used to determine when overfitting actually begins. Finally, a test dataset can be used to unbiasedly evaluate the fit of the final model to the training dataset.

[0052] To generate a treatment plan, the patient dataset 208 can be input to the trained machine learning model(s). The trained machine learning model(s) can also be input with additional data, such as a selected subset of the reference patient dataset and / or similar patient dataset, and / or treatment data from the selected subset. The trained machine learning model(s) can then calculate whether various candidate treatment procedures and / or candidate medical device designs are likely to result in a favorable outcome for the patient. Based on these calculations, the trained machine learning model(s) can select at least one treatment plan for the patient. In embodiments using multiple trained machine learning models, the models can be run sequentially or simultaneously and the results compared, and the models can be periodically updated using the training dataset. The implant design module 218 can use one or more of the machine learning models based on the models' predictive accuracy scores.

[0053] The patient-specific treatment plan generated by the implant design module 218 may include at least one patient-specific treatment procedure (e.g., surgical procedure or intervention) and / or at least one patient-specific medical device (e.g., implant or implant delivery instrument). The patient-specific treatment plan may include an entire surgical procedure or a portion thereof. Additionally, one or more patient-specific medical devices may be specifically selected or designed for the corresponding surgical procedure, thereby enabling the treatment of the patient using various components of patient-specific technology in combination. In some embodiments, the patient-specific medical device design includes the design of an orthopedic implant and / or the design of an instrument for delivering the orthopedic implant. Examples of such implants include, but are not limited to, screws (e.g., bone screws, spinal screws, pedicle screws, facet screws), interbody implant devices (e.g., intervertebral implants), cages, plates, rods, discs, fusion devices, spacers, rods, expandable devices, stents, brackets, ties, scaffolding, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, joint replacements, hip implants, etc. Examples of instruments include, but are not limited to, screw guides, cannulas, ports, catheters, insertion tools, and the like.

[0054] A patient-specific medical device design may include data representing one or more of the physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and / or biological properties (e.g., osteointegration, cell adhesion, antibacterial properties, antiviral properties) of the corresponding medical device. For example, a design for an orthopedic implant may include the shape, size, material, and / or effective stiffness of the implant (e.g., lattice density, number of struts, location of struts, etc.). In some embodiments, the patient-specific medical device design generated is a design for the entire device. Alternatively, the design generated may be for one or more components of the device rather than the entire device.

[0055] In some embodiments, the design is for one or more patient-specific device components that can be used with standard, off-the-shelf components. For example, in spinal surgery, a pedicle screw kit can include both standard and patient-specific, customized components. In some embodiments, the design generated is for a patient-specific medical device that can be used with standard, off-the-shelf delivery instruments. For example, an implant (screw, screw holder, rod) can be designed and manufactured for the patient, while the instrument for delivering the implant can be a standard instrument. In this approach, implanted components can be designed and manufactured based on the patient's anatomy and / or surgeon's preferences to enhance treatment. The patient-specific instruments described herein are expected to improve delivery into the patient's body, placement at the treatment site, and / or interaction with the patient's anatomy.

[0056] In embodiments where the patient-specific treatment plan includes a surgical procedure to implant a medical device, the implant design module 218 may also store various types of implant procedure information, such as implant parameters (e.g., type, size), implant availability, aspects of pre-operative planning (e.g., initial implant configuration, detection and measurements of the patient's anatomy, etc.), or FDA requirements for the implant (e.g., specific implant parameters and / or characteristics that comply with FDA regulations). In some embodiments, the implant design module 218 may convert the implant procedure information into a format usable for machine learning-based models and algorithms. For example, the implant procedure information may be tagged with a specific identifier for official use or converted into a numerical representation suitable for feeding into a trained machine learning model(s). The implant design module 218 may also store information about the patient's anatomy, such as two-dimensional or three-dimensional images or models of the anatomy, and / or information regarding the biology, geometry, and / or mechanical properties of the anatomy. This anatomy information may be used to inform the design and / or placement of the implant.

[0057] The treatment plan(s) generated by the implant design module 218 can be transmitted to the computing device 202 via the communications network 204 for output to a user (e.g., a clinician, surgeon, healthcare provider, patient) via the display 222. As described above, the display 222 can include a graphical user interface (GUI) that visually depicts various aspects of the treatment plan(s). For example, the display 222 can depict various aspects of a surgical procedure to be performed on a patient, such as the surgical approach, treatment levels, corrective maneuvers, tissue resection, and / or implant placement. In addition to the virtual model 230 described above, the display 222 can also display patient-specific implant designs or renderings 235, such as two-dimensional or three-dimensional models of the implant. The display 222 can also display patient information, such as two-dimensional or three-dimensional images or models of the patient's anatomy on which the surgical procedure is to be performed and / or the device is to be implanted. The computing device 202 may further include one or more user input devices (not shown) that allow a user to modify, select, accept, and / or reject the displayed treatment plan(s).

[0058] In some embodiments, the medical device design(s) generated by the implant design module 218 can be transmitted from the computing device 202 and / or server 206 to a manufacturing system 224 for manufacturing the corresponding medical device. The manufacturing system 224 can be located on-site or off-site. On-site manufacturing reduces the number of patient sessions and / or the time to be able to perform a procedure, while off-site manufacturing is useful for creating complex devices. Off-site manufacturing facilities can have specialized manufacturing equipment. In some embodiments, more complex device components can be manufactured off-site and simpler device components can be manufactured on-site.

[0059] Various types of manufacturing systems are suitable for use with embodiments herein. For example, the manufacturing system 224 can be configured for additive manufacturing, such as three-dimensional (3D) printing, stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), selective thermal sintering (SHM), electron beam melting (EBM), laminate manufacturing (LOM), powder bed printing (PP), thermoplastic printing, direct material deposition (DMD), inkjet photo-resin printing, or similar techniques, or a combination thereof. Alternatively or in combination, the manufacturing system 224 can be configured for (traditional) subtractive manufacturing, such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, lathing / turning), or similar techniques, or a combination thereof. The manufacturing system 224 can manufacture one or more patient-specific medical devices based on fabrication instructions or data (e.g., CAD data, 3D data, digital blueprints, stereolithography data, or other data suitable for the various manufacturing techniques described herein). In some embodiments, to simplify manufacturing, the patient-specific medical devices can include features, materials, and designs that are shared across designs. For example, implants for different patients can have similar internal deployment mechanisms but different deployment configurations. In some embodiments, components of the patient-specific medical devices can be selected from a set of available pre-fabricated components, and the selected pre-fabricated components can be modified based on the fabrication instructions or data.

[0060] The treatment plans described herein may be performed by a surgeon, a surgical robot, or a combination thereof, thus allowing for treatment flexibility. In some embodiments, a surgical procedure may be performed entirely by a surgeon, entirely by a surgical robot, or a combination thereof. For example, one step of a surgical procedure may be performed manually by a surgeon, and another step of the procedure may be performed by a surgical robot. In some embodiments, the implant design module 218 generates control instructions configured to cause a surgical robot (e.g., a robotic surgical system, a navigation system, etc.) to partially or completely perform the surgical procedure. The control instructions may be transmitted to the robotic device by the computing device 202 and / or the server 206.

[0061] After treating a patient according to a treatment plan, the progress of treatment can be monitored over one or more time periods to update the data analysis module 216 and / or implant design module 218. Post-treatment data can be added to the baseline data stored in the database 210. This post-treatment data can be used to train machine learning models to develop a patient-specific treatment plan, a patient-specific medical device, or a combination thereof.

[0062] It should be understood that the components of system 200 can be configured in many different ways. For example, in another embodiment, database 210, data analysis module 216, and / or implant design module 218 can be components of computing device 202 rather than server 206. As another example, database 210, data analysis module 216, and / or implant design module 218 can be located across multiple different servers, computer systems, or other types of cloud computing resources rather than on a single server 206 or computing device 202.

[0063] Additionally, in some embodiments, system 200 can operate with numerous other computer system environments or configurations. Examples of computer systems, environments, and / or configurations that may be suitable for use with the present technology include, but are not limited to, personal computers, server computers, handheld or laptop devices, mobile phones, wearable electronics, tablet devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, or distributed computing environments that include any of these systems or devices. In some embodiments, system 200 can include additional features and / or capabilities, such as any of those described in U.S. Patent Application No. 16 / 735,222, filed January 6, 2020, the disclosure of which is incorporated herein by reference in its entirety.

[0064] FIG. 3 illustrates a computing device 300 suitable for use in connection with system 200 of FIG. 2 , according to one embodiment. Computing device 300 may be incorporated into various components of system 200 of FIG. 2 , such as computing device 202 or server 206. Computing device 300 includes one or more processors 310 (e.g., CPU(s), GPU(s), HPU(s), etc.). Processor(s) 310 may be a single processing unit or multiple processing units within a device, or may be distributed across multiple devices. Processor(s) 310 may be coupled to other hardware devices using a bus, such as a PCI bus or a SCSI bus. Processor(s) 310 may be configured to execute one or more computer-readable program instructions, such as program instructions for performing any of the methods described herein.

[0065] Computing device 300 may include one or more input devices 320 that provide input to processor(s) 310 that, for example, signals actions from a user regarding one or more aspects of computing device 300. These actions may be mediated by a hardware controller that interprets signals received from input device(s) 320 and communicates the information to processor(s) 310 using a communication protocol. Input device(s) 320 may include, for example, a mouse, keyboard, touch screen, infrared sensor, touchpad, wearable input device, camera or image-based input device, microphone, or other user input device.

[0066] The computing device 300 may include a display 330 used to display various types of output, such as text, models, virtual procedures, surgical plans, implants, graphics, and / or images (e.g., images including voxels representing radioactive units or Hounsfield units representing tissue density at a location). For example, in some embodiments, the display 330 provides a two-dimensional or three-dimensional virtual model of the patient's spine. In some embodiments, the display 330 provides graphical and textual visual feedback to the user. The processor(s) 310 may communicate with the display 330 through a hardware controller of the device. In some embodiments, the display 330 includes the input device(s) as part of the display 330, such as when the input device(s) 320 include a touch screen or are equipped with an eye gaze monitoring system. In other embodiments, the display 330 is separate from the input device(s) 320. Examples of display devices include LCD display screens, LED display screens, projected, holographic, or augmented reality displays (e.g., head-up display devices or head-mounted devices), etc. In some embodiments, display 330 is configured to display a virtual model of the patient's spine that is generated based on received patient data (eg, image data), as described above with respect to display 222.

[0067] Optionally, the processor(s) 310 may also be coupled to other I / O devices 340, such as a network card, video card, audio card, USB, Firewire or other external device, camera, printer, speaker, CD-ROM drive, DVD drive, disk drive, or Blu-Ray device. The other I / O devices 340 may also include input ports for information from directly connected medical equipment, such as imaging devices including MRI machines, X-ray machines, CT machines, etc. The other I / O devices 340 may further include input ports for receiving data from these types of machines, from other sources, such as over a network, or from previously captured data stored, for example, in a database.

[0068] In some embodiments, computing device 300 also includes a communications device (not shown) capable of wireless or wired communications with network nodes. The communications device may communicate with another device or server over a network using, for example, the TCP / IP protocol. Computing device 300 may utilize the communications device to distribute operations across multiple network devices, including imaging facilities, manufacturing facilities, etc.

[0069] The computing device 300 may include memory 350, which may reside within a single device or be distributed across multiple devices. Memory 350 may include one or more of various hardware devices for volatile and non-volatile storage, including both read-only and writable memory. For example, memory may include random access memory (RAM), various caches, CPU registers, read-only memory (ROM), and writable, non-volatile memory such as flash memory, hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives, and device buffers. Memory is not a propagating signal independent of the underlying hardware and is therefore non-transitory. In some embodiments, memory 350 is a non-transitory computer-readable storage medium that stores, for example, programs, software, or data. In some embodiments, memory 350 may include program memory 360 that stores programs and software, such as an operating system 362, one or more implant design modules 364, and other application programs 366. The implant design module(s) 364 may include one or more modules configured to perform the various methods described herein. Memory 350 may also include data memory 370 which may contain, for example, reference data, configuration data, settings, user options or preferences, etc. which may be provided to program memory 360 or any other element of computing device 300 .

[0070] How to design a patient-specific device 4 is a flowchart of a method 400 for designing a patient-specific implant in accordance with an optional embodiment of the present technology. For example, method 400 can be used to design a patient-specific artificial disc implant. Method 400 can begin by acquiring patient data in step 402. The patient data can include image data and kinematic data of the patient's spine. The image data can include, for example, magnetic resonance imaging (MRI) images, ultrasound images, computer-aided tomography (CAT) images, positron emission tomography (PET) images, x-ray images (e.g., biplanar x-rays), camera images, etc. The image data can represent the patient's native anatomical configuration (e.g., pre-operative anatomy), such as the geometry, orientation, and topography of various anatomical features. For example, in some embodiments, the image data may be indicative of (and / or be used to determine) vertebral spacing, vertebral orientation, vertebral translation, abnormal bone growth, abnormal joint growth, arthritis, joint degeneration, tissue degeneration, stenosis, scar tissue, lumbar lordosis, Cobb angle(s), pelvic intrinsic angle, disc height, segmental flexibility, rotational displacement, and other spinal tissue characteristics.

[0071] For example, the kinematic data may include specific values ​​or other data corresponding to one or more kinematic parameters, such as specific values ​​or other data corresponding to range of motion in three dimensions (e.g., including flexion, extension, and bending), flexion / extension arc, left / right flexion arc, lateral bending, flexion angle, rotation angle, translation, and axial rotation. Kinematic data may be acquired under various conditions (e.g., weight-bearing, non-weight-bearing, etc.). In some embodiments, range of motion may be defined as a spherical range of motion, in which one vertebra moves spherically relative to another vertebra. In other embodiments, range of motion may be defined as a more complex range of motion defined by a three-dimensional curve through space. In addition to image data and kinematic data, other patient data may also be acquired at step 402. Additional patient data may include, but is not limited to, medical history, surgical intervention data, treatment outcome data, progress data (e.g., physician's notes), patient feedback (e.g., quality of life questionnaires, feedback obtained using surveys), clinical data, provider information (e.g., physician, hospital, surgical team), patient information (demographics, gender, age, height, weight, pathology type, occupation, activity level, tissue information, health assessment, comorbidities, health-related quality of life (HRQL)), vital signs, diagnostic results, medication information, allergies, and / or any combination thereof.

[0072] In some embodiments, acquiring kinematic data in step 402 includes determining values ​​for one or more kinematic parameters using one or more software modules (e.g., implant design module 218 of FIG. 2 and / or implant design module 364 of FIG. 3 ). The software modules may perform a kinematic assessment of the patient based on the image data and / or other patient data to estimate various kinematic parameters of the patient. For example, the software modules may analyze one or more anatomical features / measurements in the image data and / or virtual model to define kinematic parameters, determine kinematic relationships, and / or estimate various kinematic parameters. Anatomical features / measurements may include, but are not limited to, distances between anatomical landmarks, fiducials, vertebral spacing, vertebral orientation, abnormal bone growth, abnormal joint growth, arthritis, joint degeneration, tissue degeneration, stenosis, scar tissue, and combinations thereof. Other patient data that the software modules may use to estimate kinematic parameters may include, but are not limited to, medical history, gender, age, height, and weight. In some embodiments, the software module may incorporate one or more artificial intelligence architectures that determine various kinematic parameters based on the image data.

[0073] The artificial intelligence architecture may be similar to those described herein above and may include, for example, a trained neural network (e.g., a trained convolutional neural network, etc.) for analyzing two-dimensional images and / or three-dimensional models. Without being bound by theory, performing the kinematic assessment using one or more software modules may reduce and / or eliminate the need for manual assessment of the patient's kinematics prior to implant surgery. However, in at least some embodiments, the kinematic data may also be obtained through one or more standard kinematic studies. Thus, in at least some embodiments, obtaining the kinematic data in step 402 includes receiving values ​​for one or more kinematic parameters. For example, the values ​​for the one or more kinematic parameters may be obtained from a motion study or through a joint morphology study and input to a system performing method 400 (e.g., input to computer device 300 via input device(s) 320 shown in FIG. 3 ).

[0074] Method 400 further includes generating a virtual model of one or more regions of the patient's anatomy at step 404 based at least in part on the image data acquired at step 402. The virtual model may be a 2D model, a 3D model, a CAD model, or other suitable model that provides a virtual representation of the patient's natural anatomy. The one or more regions may include, but are not limited to, a region of the patient's spine (e.g., cervical, thoracic, lumbar, and / or sacrum). For example, in one embodiment, the target site may be a segment of the patient's spine between C6 and C3. In such an embodiment, the virtual representation may include the individual vertebrae between C6 and C3 and other associated anatomical structures, such as discs between the vertebrae. In some embodiments, the virtual model may include a model of the patient's entire spine, not just a specific segment. In some embodiments, generating the virtual model from the image data includes reconstructing the two-dimensional image data, including pixels, into three-dimensional volumetric data, including voxels, representing the patient's anatomy. In some embodiments, the image data and / or the virtual model may be segmented to better visualize individual anatomical features. The segmentable anatomical feature may be any anatomical structure of interest, such as a bone, a disc, an organ, etc. For example, in some embodiments, bony anatomy (e.g., vertebrae) is segmented from other anatomical structures such that individual bony structures (e.g., vertebrae) are visible in isolation. The virtual model may optionally be displayed to the physician, such as via display 222 shown in FIG. 2. In some embodiments, step 404 is omitted, and method 400 proceeds from step 402 directly to step 406.

[0075] In step 406, a user (e.g., a surgeon or other physician) and / or a software module (e.g., implant design module 218 and / or implant design module 364) determines a target anatomical configuration for one or more regions of the patient's anatomy. The target anatomical configuration can differ from the native anatomical configuration represented in the image data. The target anatomical configuration can include adjustments to the native anatomical configuration of one or more anatomical features, including, but not limited to, interbody spacing, vertebral body orientation, alignment of two or more vertebral bodies, lumbar lordosis, Cobb angle(s), pelvic intrinsic angle, disc height, segmental flexibility, and rotational displacement. For example, in an embodiment in which a patient has disc degeneration between two vertebrae, the image data may indicate that the native anatomical configuration has a reduced or suboptimal distance between the lower border of a first vertebra and the upper border of a second vertebra. Thus, the target anatomical configuration can include an increased distance between the first and second vertebrae, reflecting "healthy" or "normal" anatomy. In another example, the image data may indicate that a first vertebra is misaligned with a second vertebra, and thus, in such an embodiment, the target anatomical configuration may include realigning the first vertebra and the second vertebra.

[0076] In embodiments in which a user determines a target anatomical configuration, the user can use the virtual model to manipulate one or more relationships (e.g., distances, angles, constraints) between individual vertebrae to set the target anatomical configuration. Manipulations can include, but are not limited to, translation along an axis or curve, rotation about an axis or center of gravity, and / or rotation about a center of mass. In some embodiments, manipulations can be performed until the virtual model shows the anatomy in the "desired" anatomical arrangement. The user can then provide input to set the shown desired anatomical configuration as the target anatomical configuration.

[0077] In embodiments in which a software module determines the target anatomical configuration, the software module can automatically manipulate the virtual model to provide a recommended target anatomical configuration based on one or more design criteria and / or a reference patient data set. For example, suitable design criteria can include target values ​​related to various anatomical features, such as target values ​​related to vertebral spacing (e.g., minimum vertebral body spacing, maximum vertebral spacing, etc.), vertebral orientation, vertebral alignment, vertebral translation, lumbar lordosis, Cobb angle(s), pelvic intrinsic angle, disc height, segmental flexibility, rotational displacement, or kinematics, etc. For example, a suitable reference patient data set can be identified using the data analysis module 216 described above with reference to FIG. 2. The implant design module can further perform one or more simulations or analyses (e.g., stress analysis, fatigue analysis, etc.), etc., to provide feedback (e.g., identified high stress areas), design recommendations, or treatment recommendations (e.g., steps to prepare the implant site), etc. The software module used to manipulate the virtual model to provide the recommended target anatomical configuration in step 406 can be the same as or different from the software module used to perform the kinematic assessment in step 402. In some embodiments, determining the target anatomical configuration includes, after providing the recommended target anatomical configuration using a software module, optionally allowing the physician to further modify the target anatomical configuration.

[0078] Next, method 400 designs a patient-specific implant at step 408. The patient-specific implant can be designed using a software module, which can be the same or different from the software modules optionally used in steps 402 and 408. Among other things, the software module designs the patient-specific implant to fit into the target anatomical configuration when implanted in a patient. Thus, the patient-specific implant should fit into the negative space (e.g., "implant envelope") of the target anatomical configuration. The negative space can be used to determine various geometric parameters of the patient-specific implant. The geometric parameters include, but are not limited to, dimensions, height, surface, footprint, etc. In some embodiments, a virtual patient-specific implant can be created and displayed within the negative space of the virtual representation of the patient anatomy.

[0079] The software module can also design the patient-specific implant to fit the anatomical topography of the target site. For artificial disc implants, this design involves matching the topography of the disc endplate to the topography of the adjacent vertebrae. For example, referring to FIG. 1A , the outer surface 83 of the first endplate 82 is designed to mate with the topography of the inferior surface 52 of the relatively upper vertebra 50, and the outer surface 87 of the second endplate 86 is designed to mate with the topography of the superior surface 62 of the relatively lower vertebra 60. For example, if the inferior surface of the relatively upper vertebra is slightly convex, the outer surface of the first endplate is designed to be slightly concave to "mate" with the slightly convex vertebral surface. Without being bound by theory, it is expected that increasing the fit between the implant endplates and the vertebrae (e.g., forming a gap-free or nearly gap-free interface) will prevent and / or reduce instances of dynamic failure of the implant (e.g., by reducing and / or preventing micromotion of the implant) and / or increase the effectiveness of the implant.

[0080] In some embodiments, the software module can further design the patient-specific implant to improve one or more kinematic parameter values ​​obtained in step 402. For example, as described in further detail with reference to FIG. 5, the obtained kinematic parameter values ​​may indicate that the patient's kinematics are limited by a pathological joint that is being replaced by the patient-specific implant. Thus, rather than designing the patient-specific implant to maintain suboptimal kinematics associated with the pathological condition, the software module can design the patient-specific implant to provide improved kinematics that meet one or more predetermined kinematic criteria (e.g., baseline or "target" kinematic values) when implanted between the target vertebrae.

[0081] Specifically, in some embodiments, the kinematic parameter values ​​obtained in step 402 are compared to one or more predetermined kinematic criteria, which may include reference kinematic values. The reference kinematic values ​​may include specific values ​​of various kinematic parameters, such as specific values ​​related to range of motion, flexion angle, rotation angle, displacement, flexion, extension, flexion / extension arc, lateral bending, left / right flexion arc, and axial rotation. The reference kinematic values ​​may include minimum thresholds, maximum thresholds, and / or ranges depending on the specific parameters. For example, the reference kinematic values ​​may include flexion / extension arcs of 60-80 degrees, 70-80 degrees, etc., and / or flexion / extension arcs of greater than 60 degrees, greater than 70 degrees, greater than 80 degrees, etc. Additionally or alternatively, the reference kinematic values ​​may include a minimum threshold for lateral bending of 25 degrees, 30 degrees, 35 degrees, etc. The reference kinematic values ​​may be selected based on the kinematics of normal or healthy patients of similar age, weight, height, etc. In some embodiments, the baseline kinematic values ​​may also be selected based on one or more patient characteristics, such as the patient's desired range of motion, overall spinal health, or activity level. In some embodiments, the baseline kinematic values ​​may be determined using baseline patient data stored in database 210 on server 206 (FIG. 2) and / or may be selected by a surgeon or other physician.

[0082] If one or more of the acquired kinematic parameter values ​​do not meet one or more of the corresponding reference kinematic parameter values ​​(and / or are not within a threshold deviation, such as within 5%, 10%, etc., of the corresponding reference kinematic parameter values), the software module can automatically design a patient-specific implant to improve one or more of the kinematic parameter values. For example, the software module can design a patient-specific implant that, when implanted in a patient, increases flexion / extension arc, left / right flexion arc, or other kinematic parameter value by at least 5%, 10%, 20%, 30%, or other suitable amount (e.g., based on the difference between the acquired kinematic parameter value and the reference kinematic parameter value). In some procedures, the patient-specific prosthetic implant can increase the patient's flexion / extension arc by 50-60 degrees, 60-70 degrees, 60-80 degrees, or other suitable amount. In some procedures, the patient-specific prosthetic implant can increase the patient's lateral bending by 10 to 20 degrees, 20 to 30 degrees, 20 to 40 degrees, or any other suitable amount.

[0083] In a particular example, if the acquired kinematic parameter values ​​indicate a patient's flexion / extension arc of 50 degrees, but the baseline kinematic parameter value for the flexion / extension arc is between 60 degrees and 80 degrees, the software module can design a patient-specific implant such that, when implanted in the patient, the patient has a flexion / extension arc of at least 60 degrees. However, in other embodiments, the software module designs the patient-specific implant to improve the flexion / extension arc, but not necessarily to the baseline kinematic parameter value (e.g., a 50-55 degree improvement in the previous example). In another example, if the acquired kinematic parameter values ​​indicate a patient's lateral bending (e.g., left bending) of 20 degrees, but the minimum threshold for the baseline kinematic parameter value for lateral bending is 30 degrees, the software module can design a patient-specific implant such that, when implanted in the patient, the patient has at least 30 degrees of lateral bending.

[0084] In some embodiments, the software module designs the patient-specific implant to maintain the kinematic parameter values ​​obtained in step 402 (e.g., be generally similar and / or identical, such as within 10% of the kinematic parameter values). For example, the obtained kinematic parameter values ​​may indicate that the patient's kinematics are limited by a pathological joint being replaced by the patient-specific implant. This may be determined, for example, by comparing the obtained kinematic parameter values ​​to one or more predetermined kinematic criteria (e.g., the reference kinematic values ​​described above). If the obtained kinematic parameter values ​​meet one or more predetermined kinematic criteria (and / or are within a threshold deviation, such as within 5%, 10%, etc., of the reference kinematic parameter values), the software module may design the patient-specific implant such that, when implanted between the target vertebrae, the target vertebrae have kinematics similar and / or identical to the kinematics obtained in step 402. Thus, in some procedures, the patient-specific artificial disc may be configured to maintain spinal motion (e.g., to maintain healthy kinematics, reduce the risk of complications, etc.). For example, a patient-specific artificial disc can maintain a standard lumbar flexion arc (e.g., measured in an upright and / or supine position), a flexion / extension arc or left / right flexion arc measured using digital measurement techniques and / or via image analysis, etc.

[0085] In a particular example, if the acquired kinematic parameter values ​​indicate a patient's flexion / extension arc is 65 degrees and the reference kinematic parameter value for the flexion / extension arc is between 60 degrees and 80 degrees, the software module can design a patient-specific implant that, when implanted in the patient, will maintain the patient in a flexion / extension arc of approximately 62 degrees. In another example, if the acquired kinematic parameter values ​​indicate a patient's lateral bending (e.g., left bending) is 35 degrees and the reference kinematic parameter value for lateral bending has a minimum threshold of 30 degrees, the software module can design a patient-specific implant that, when implanted in the patient, will maintain the patient in approximately 35 degrees of lateral bending.

[0086] Designing a patient-specific implant to maintain or improve kinematics involves designing the interior of the patient-specific implant to have specific kinematic characteristics. Specifically, referring again to FIGS. 1A and 1B , the motion segment or core 90 of the patient-specific implant can be designed to have the appropriate orientation, rotation, flexion, and / or translation to allow the target vertebrae to move relative to one another according to the desired kinematics after the implant 80 is implanted in the patient. This design can include selecting one or more suitable combinations of materials that provide the target kinematics. Suitable materials include, but are not limited to, elastomeric polymers, rigid polymers, hybrid materials with elastomeric and rigid properties, ceramics, metals, and combinations thereof. The motion segment can also include multiple mating surfaces that provide the determined kinematic characteristics. Thus, in some embodiments, the desired kinematics can be obtained by selecting the geometry or other characteristics of multiple mating surfaces that provide the desired kinematics. Thus, the motion segment or core 90 can be designed to provide any of the above-mentioned corrections, such as improving the flexion / extension arc, left / right flexion arc, or other kinematic parameter value by at least 5%, 10%, 20%, 30%, or other suitable amount.

[0087] In some embodiments, the patient may have other conditions (e.g., nerve compression, curved spine, lordosis, arthritis, etc.) that may limit the kinematic parameter values. Thus, in some embodiments, the software module can recommend secondary procedures to be performed on the identified anatomical features (e.g., stenosis, enlarged facet joints, bony overgrowth, cartilage loss, etc.) to further enhance or influence physical motion. The predicted results of these secondary procedure(s) can be input into the software module to determine a modified / optimized patient-specific implant. The patient-specific implant can thus be modified / optimized for the spine in which the secondary procedure(s) is performed simultaneously with or after implantation of the patient-specific implant. In some embodiments, the patient-specific artificial disc can also be designed to compensate for, alleviate, or otherwise affect these other conditions. For example, the patient-specific artificial disc can be designed to reduce or limit pain associated with nerve compression or to correct lordosis while providing a threshold amount of motion. In some embodiments, a patient-specific prosthetic implant can be designed to enable a particular motion(s) suitable for performing selected tasks such as walking, running, swinging a golf club, jumping, etc. The selected tasks can be input into a software module, which designs the implant to enable the patient to perform these tasks. Design criteria can be selected by the software module, the designer, and / or the physician.

[0088] The software module may also be designed to optimize the patient-specific implant in other ways. For example, the software module may analyze a virtual model of the patient-specific implant implanted in the patient anatomy to identify one or more load-bearing or other high-stress areas of the implant. If the fatigue properties associated with the load-bearing or high-stress areas exceed a maximum threshold, the processing module may automatically redesign the patient-specific implant to avoid, modify, adapt, or otherwise account for stresses such that the fatigue properties no longer exceed the maximum threshold. The patient-specific implant may also be optimized for the patient in other ways not explicitly described herein.

[0089] As mentioned above, in at least some embodiments, the patient-specific implant is designed in step 408 using one or more software modules, such as implant design module 218 described above with respect to Figure 2 and / or implant design module 364 described above with respect to Figure 3. The software modules used in step 408 can optionally be the same or different from the software modules used in steps 402 and 406. Thus, in at least some embodiments, the patient-specific implant can be automatically designed in step 408 using system 200 and / or computing device 300. In other embodiments, step 408 is only partially automated and can include one or more user steps / inputs.

[0090] After the patient-specific implant is designed, method 400 can continue at step 410, manufacturing the patient-specific implant. In some embodiments, the patient-specific implant design(s) can be transmitted from the software module to a manufacturing system that manufactures the patient-specific implant. For example, the method can include generating computer-executable manufacturing instructions that, when executed by the manufacturing system, instruct the manufacturing system to manufacture the patient-specific implant. The manufacturing instructions can be transmitted to the manufacturing system using any suitable means. The manufacturing system can be located on-site or off-site. On-site manufacturing reduces the number of sessions with the patient and / or the time to be able to perform a procedure, whereas off-site manufacturing is useful for forming complex devices and may have specialized manufacturing equipment. In some embodiments, more complex device components can be manufactured off-site and simpler device components can be manufactured on-site.

[0091] Various types of manufacturing systems are suitable for use with embodiments herein. For example, the manufacturing system can be configured for additive manufacturing, such as three-dimensional (3D) printing, stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), selective thermal sintering (SHM), electron beam melting (EBM), laminate manufacturing (LOM), powder bed printing (PP), thermoplastic printing, direct material deposition (DMD), inkjet photoresist printing, or similar techniques, or a combination thereof. Alternatively or in combination, the manufacturing system can also be configured for (traditional) subtractive manufacturing, such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, lathe / turning), or similar techniques, or a combination thereof. The manufacturing system can manufacture one or more patient-specific medical devices based on manufacturing instructions or data (e.g., CAD data, 3D data, digital blueprints, stereolithography data, or other data suitable for the various manufacturing techniques described herein). In some embodiments, to simplify manufacturing, patient-specific implants can include features, materials, and designs that are shared across designs. For example, patient-specific implants deployable for different patients can have similar internal deployment mechanisms but different deployment configurations. In some embodiments, components of the patient-specific implant can be selected from a set of available prefabricated components, and the selected prefabricated components can be modified based on manufacturing instructions or data.

[0092] 5 is a flowchart of another method 500 of designing a patient-specific implant, in accordance with select embodiments of the present technology. Specifically, method 500 includes determining, analyzing, and / or evaluating the kinematics of a natural joint using one or more software modules (e.g., implant design module 218 and / or implant design module 364) and designing a patient-specific implant based on the determined kinematics. Thus, in some embodiments, method 500 can be used to design a patient-specific artificial disc implant. Some aspects of method 500 are generally similar to some aspects of method 400 described above. Thus, the following description of method 500 will focus on aspects of method 500 not described with respect to method 400, with the understanding that descriptions of similar steps in method 400 also apply to similar steps in method 500.

[0093] Similar to method 400, method 500 may begin by acquiring patient data in step 502. The patient data may include, for example, image data of the patient's spine. In some embodiments, the patient data may also include other patient data and / or data from one or more kinematic studies performed on the patient. Method 500 further includes, in step 504, generating a virtual model of one or more regions of the patient's anatomy based at least in part on the image data. The virtual model may be similar to the virtual model described in detail in step 404 of method 400. Similar to method 400, in some embodiments, step 504 may be omitted, and method 500 may proceed directly from step 502 to step 506.

[0094] Next, method 500 may analyze the kinematics of one or more joints of the patient's spine based on the patient data at step 506. As described in detail in step 402 of method 400, analyzing the kinematics of one or more joints may include evaluating one or more kinematic parameters using a software module. In some embodiments, step 506 may be performed automatically or partially automated using a software module, or may be performed manually by manipulating the virtual model generated at step 504. In embodiments in which kinematic data is received along with the patient data at step 502, step 506 may optionally be omitted and method 500 may proceed directly to step 508.

[0095] In some embodiments, as described in detail with respect to step 408 of method 400, analyzing the kinematics in step 506 includes comparing the determined kinematic parameter values ​​to one or more kinematic criteria associated with the kinematic parameters (e.g., the reference kinematic parameter values ​​described above). If one or more of the determined kinematic parameter values ​​do not meet the corresponding reference kinematic parameter value (and / or are not within a threshold deviation, such as within 5%, 10%, 20%, etc., of the corresponding reference kinematic parameter value), method 500 can provide a notification to a user (e.g., a physician) that the kinematics should be modified / optimized when designing the patient-specific implant. In some embodiments, as described above, the software module can further recommend secondary procedures (e.g., decompression procedures, scar tissue removal, etc.) to be performed before, during, or after implanting the patient-specific implant to ensure improved patient kinematics after the implant procedure. The predicted results of these secondary procedures can be input into the software module to determine a modified / optimized patient-specific implant. Thus, the patient-specific implant can be modified / optimized for the spine in which the secondary procedure(s) are performed either simultaneously with or after implantation of the patient-specific implant.

[0096] In step 508, a user and / or a software module may determine a target anatomical configuration, as described above with respect to step 406 of method 400. After determining the target anatomical configuration, method 500 may perform a second kinematic analysis in step 510 to determine the range of motion of the joint under the target anatomical configuration determined in step 508. The second kinematic analysis may be generally similar to the kinematic analysis performed in step 506 and may be performed in addition to or instead of the kinematic analysis of step 506. In some embodiments, the second kinematic analysis may serve as a “check” on the target anatomical configuration. For example, in some embodiments, the second kinematic analysis must show that the kinematics associated with the target anatomical configuration meet one or more predetermined kinematic criteria (e.g., particular kinematic parameter thresholds and / or ranges typical of a “healthy” joint). The predetermined kinematic criteria used in step 410 may be the same as or different from the kinematic criteria used in step 506. For example, if the kinematic analysis indicates that the target anatomical configuration does not achieve one or more predetermined kinematic criteria, such as when the target anatomical configuration prevents the virtual model from meeting a threshold range of motion, the user may be prompted to further manipulate or otherwise adjust the target anatomical configuration using the virtual model (e.g., as performed in step 506). After further manipulation of the target anatomical configuration, a second kinematic analysis may be repeated to confirm that the kinematics associated with the modified target anatomical configuration achieves one or more predetermined kinematic criteria. Once the kinematic criteria are achieved, method 500 may proceed.

[0097] Next, method 500 designs a patient-specific implant in step 512. The design of the patient-specific implant in step 512 can be generally similar to the design of the patient-specific implant in step 408 of method 400. In some embodiments, the patient-specific implant is designed to provide the kinematics determined in step 506 when implanted in a patient. In other embodiments, such as those in which the kinematics determined in step 506 did not meet one or more predetermined kinematic criteria, the patient-specific implant is designed to provide kinematics according to one or more predetermined criteria (e.g., to mimic a "healthy" joint). In some embodiments, a virtual model of the designed patient-specific implant can be generated and combined with a virtual model of the patient's spine to create a combined virtual model showing the patient-specific implant in the patient's spine.

[0098] In some embodiments, method 500 may then perform a third kinematic analysis in step 514. The third kinematic analysis may include evaluating one or more kinematic parameters of the patient's spine including the patient-specific implant and comparing the evaluated kinematic parameters to one or more kinematic criteria, which may be the same or different from the kinematic criteria used in steps 506 and 510. If the evaluated kinematic parameters do not match the one or more criteria, method 500 may (i) prompt the user to further adjust the target anatomical configuration and / or (ii) make recommended adjustments to the target anatomical configuration and / or the patient-specific implant. In some embodiments, method 500 may prevent the patient-specific implant from being manufactured until predetermined kinematic criteria are met (e.g., design data for the patient-specific implant may be sent to a manufacturing system only after the patient-specific implant has been shown to provide one or more kinematic criteria). If the third kinematic analysis confirms that the patient-specific implant achieves the kinematic criteria, the patient-specific implant may be manufactured in step 516, which may be generally similar to step 410 of method 400. The manufactured implant may then be transported to the operating room and implanted in the patient.

[0099] The systems and methods described herein can also be combined with any of the above methods to generate a patient treatment plan in addition to designing a patient-specific implant. The treatment plan can include surgical information, a surgical plan, and technical recommendations (e.g., device and / or instrumentation recommendations) in addition to a medical device design. For example, the treatment plan can include at least one treatment procedure (e.g., a surgical procedure or intervention) for implanting a patient-specific implant. The systems described herein can be configured to generate treatment plans for patients suffering from orthopedic or spinal diseases or disorders such as trauma (e.g., fractures), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), irregular spinal curvature (e.g., scoliosis, lordosis, kyphosis), irregular spinal displacement (e.g., spondylolisthesis, lateral displacement, axial displacement), osteoarthritis, lumbar degenerative disc disease, cervical degenerative disc disease, lumbar spinal stenosis, cervical spinal stenosis, or a combination thereof.

[0100] The foregoing detailed description has presented various embodiments of devices and / or processes via block diagrams, flowcharts, and / or examples. Those skilled in the art will appreciate that, to the extent such block diagrams, flowcharts, and / or examples include one or more functions and / or operations, each function and / or operation in such block diagrams, flowcharts, and / or examples can be individually and / or collectively implemented by a wide variety of hardware, software, firmware, or substantially any combination thereof. In some embodiments, portions of the subject matter described herein can be implemented by an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or other integrated format. However, those skilled in the art will recognize that certain aspects of the embodiments disclosed herein may equivalently be implemented in whole or in part within integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or substantially any combination thereof, and that designing circuitry and / or writing software and / or firmware code is well within the skill of those skilled in the art in light of this disclosure. Those skilled in the art will also recognize that the subject matter mechanisms described herein can be distributed as a program product in various forms, and that exemplary embodiments of the subject matter described herein apply regardless of the particular type of signal-bearing medium used to actually effect the distribution. Examples of signal bearing media include, but are not limited to, recordable-type media such as floppy disks, hard disk drives, CDs, DVDs, digital tape, computer memory, and transmission-type media such as digital and / or analog communications media (e.g., fiber optic cables, wave guides, wired communications links, wireless communications links, etc.).

[0101] Those skilled in the art will recognize that it is common in the art to describe devices and / or processes as described herein and then integrate such described devices and / or processes into a data processing system using good engineering practices. That is, at least a portion of the devices and / or processes described herein can be integrated into a data processing system through a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system generally includes a system unit housing, a video display device, memory such as volatile and non-volatile memory, a processor such as a microprocessor and a digital signal processor, computational entities such as an operating system, drivers, a graphical user interface, and application programs, one or more interaction devices such as a touchpad or screen, and / or a control system including feedback loops and control motors (e.g., feedback for sensing position and / or velocity, control motors for moving and / or adjusting components and / or quantities). A typical data processing system can be implemented utilizing any suitable commercially available components typically found in data computer / communication systems and / or network computer / communication systems.

[0102] The subject matter described herein may depict different components contained within or connected to other different components. It should be understood that such depicted architectures are merely exemplary, and that many other architectures that achieve the same functionality may in fact be implemented. In a conceptual sense, any configuration of components that achieve the same functionality is effectively "associated" with each other such that the desired functionality is achieved. Thus, any two components herein that combine to achieve a particular functionality may be considered to be "associated" with each other such that the desired functionality is achieved, regardless of the architecture or intervening components. Similarly, any two components that are associated in this manner may also be considered to be "operably connected" or "operably coupled" with each other such that the desired functionality is achieved, and any two components that may be associated in this manner may also be considered to be "operably couplable" with each other such that the desired functionality is achieved. Examples of operably couplable components include, but are not limited to, physically coupled and / or physically interacting components, wirelessly interacting and / or wirelessly interacting components, and / or logically interacting and / or logically interacting components.

[0103] In some embodiments, the embodiments, features, systems, devices, materials, methods, and techniques described herein may be similar to any one or more of the embodiments, features, systems, devices, materials, methods, and techniques described in the following documents: U.S. Patent Application No. 16 / 048,167, filed July 27, 2018, entitled "SYSTEMS AND METHODS FOR ASSISTING AND AUGMENTING SURGICAL PROCEDURES"; U.S. Patent Application No. 16 / 242,877, filed January 8, 2019, entitled "SYSTEMS AND METHODS OF ASSISTING A SURGEON WITH SCREW PLACEMENT DURING SPINAL SURGERY"; U.S. Patent Application No. 16 / 207,116, filed December 1, 2018, entitled "SYSTEMS AND METHODS FOR MULTI-PLANAR ORTHOPEDIC ALIGNMENT"; U.S. Patent Application No. 16 / 352,699, filed March 13, 2019, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION"; U.S. Patent Application No. 16 / 383,215, filed April 12, 2019, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION"; U.S. Patent Application No. 16 / 569,494, filed September 12, 2019, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS"; U.S. Patent Application No. 16 / 699,447, filed November 29, 2019, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS"; U.S. Patent Application No. 17 / 085,564, filed October 30, 2020, entitled "SYSTEMS AND METHODS FOR DESIGNING ORTHOPEDIC IMPLANTS BASED ON TISSUE CHARACTERISTICS"; U.S. Patent Application No. 16 / 735,222, filed January 6, 2020, entitled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 16 / 990,810, filed August 11, 2020, entitled "LINKING PATIENT-SPECIFIC MEDICAL DEVICES WITH PATIENT-SPECIFIC DATA, AND ASSOCIATED SYSTEMS, DEVICES, AND METHODS"; U.S. Patent Application No. 17 / 100,396, filed November 20, 2020, entitled "Patient-Specific Vertebral Implants with Positioning Features," and U.S. Patent Application No. 17 / 342,439, filed June 8, 2021, entitled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS."

[0104] All of the above patents and applications are incorporated herein by reference in their entirety, and in certain embodiments, the embodiments, features, systems, devices, materials, methods, and techniques described herein may be applied or used in connection with any one or more of these embodiments, features, systems, devices, or other items. [Explanation of symbols]

[0105] 50a First vertebra 52a Surface of the first vertebral body 60a Second vertebra 62a Surface of the second vertebra 80a Implant 82a First endplate 83a Outer surface of first endplate 86a Second endplate 88a Outer surface of second endplate 90a Core

Claims

1. 1. A computer-implemented method for designing a patient-specific implant, comprising: image data of one or more regions of the patient's spine, the image data representing the native anatomical configuration of the one or more regions; kinematic data including values ​​of one or more kinematic parameters associated with the one or more regions of the patient's spine; acquiring patient data including: determining a target anatomical configuration for the one or more regions that differs from the original anatomical configuration; designing a patient-specific implant based at least in part on the target anatomical configuration and the kinematic parameter values; wherein the patient-specific implant is configured, when implanted in the patient, to provide the target anatomical configuration while maintaining or improving the kinematic parameter values. A method characterized by:

2. obtaining the kinematic data includes determining the values ​​of the one or more kinematic parameters based on the image data; The method of claim 1.

3. determining the values ​​of the one or more kinematic parameters based on the image data includes analyzing the image data using one or more artificial intelligence architectures; The method of claim 2.

4. obtaining the kinematic data includes receiving the values ​​of the one or more kinematic parameters; The method of claim 1.

5. the one or more kinematic parameters comprise a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation, and the value of the one or more kinematic parameters comprises a value of a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation; The method of claim 1.

6. comparing the kinematic parameter values ​​to one or more kinematic criteria; If the kinematic parameter values ​​satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to maintain the kinematic parameter values ​​when implanted in the patient; if the kinematic parameter values ​​do not satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to improve the kinematic parameter values ​​when implanted in the patient; The method of claim 1 further comprising:

7. the kinematic criteria include reference kinematic parameter values; The method of claim 5.

8. The patient-specific implant is configured to fit between a first anatomical structure and a second anatomical structure, and designing the patient-specific implant based on the target anatomical configuration includes: analyzing a first topography of the first anatomical structure; designing a first endplate of the patient-specific implant to mate with the first topography of the first anatomy; analyzing a second topography of the second anatomical structure; designing a second endplate of the patient-specific implant to mate with a second topography of the second anatomic endplate; The method of claim 1 , comprising:

9. The patient-specific implant is an artificial intervertebral disc having a first end plate, a second end plate, and a core between the first end plate and the second end plate, and designing the patient-specific implant includes: designing the first endplate to mate with a topography of a first anatomy of the patient's spine; designing the second endplate to mate with the topography of a second anatomical structure of the patient's spine; designing the core to maintain or improve the kinematic parameter values ​​when the patient-specific implant is implanted in the patient; The method of claim 1 , comprising:

10. designing the core includes selecting a combination of one or more elastomeric polymers, one or more rigid polymers, one or more ceramic materials, and / or one or more metallic materials to form the core; 10. The method of claim 9.

11. Determining the target anatomical configuration includes: generating a virtual model of the original anatomical structure based on the image data; receiving one or more operations on the virtual model to position the virtual model in the target anatomical configuration; The method of claim 1 , comprising:

12. Determining the target anatomical configuration includes: identifying one or more reference patient data sets; determining the target anatomical configuration based on the one or more reference patient data sets; The method of claim 1 , comprising:

13. generating computer-executable manufacturing instructions that, when executed, instruct a manufacturing system to manufacture the patient-specific implant. The method of claim 1.

14. transmitting the computer-executable manufacturing instructions to the manufacturing system to manufacture the patient-specific implant. The method of claim 12.

15. 1. A system for designing a patient-specific implant, comprising: one or more processors; a memory for storing instructions; the instructions, when executed by the one or more processors, cause the system to: image data of one or more regions of the patient's spine, the image data representing the native anatomical configuration of the one or more regions; kinematic data including values ​​of one or more kinematic parameters associated with the one or more regions of the patient's spine; acquiring patient data including: determining a target anatomical configuration for the one or more regions that differs from the original anatomical configuration; designing a patient-specific implant based at least in part on the target anatomical configuration and kinematic parameter values; wherein the patient-specific implant, when implanted in the patient, is configured to provide the target anatomical correction while maintaining or improving the kinematic parameter values. A system characterized by:

16. obtaining the kinematic data includes determining the values ​​of the one or more kinematic parameters based on the image data; The system of claim 15.

17. determining the values ​​of the one or more kinematic parameters based on the image data includes analyzing the image data using one or more artificial intelligence architectures; 17. The system of claim 16.

18. obtaining the kinematic data includes receiving the values ​​of the one or more kinematic parameters; The system of claim 15.

19. the one or more kinematic parameters comprise a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation, and the value of the one or more kinematic parameters comprises a value of a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation; The system of claim 15.

20. The operation is comparing the kinematic parameter values ​​to one or more kinematic criteria; If the kinematic parameter values ​​satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to maintain the kinematic parameter values ​​when implanted in the patient; if the kinematic parameter values ​​do not satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to improve the kinematic parameter values ​​when implanted in the patient; The system of claim 15 further comprising:

21. the kinematic criteria include reference kinematic parameter values; 21. The system of claim 20.

22. The patient-specific implant is an artificial intervertebral disc having a first end plate, a second end plate, and a core between the first end plate and the second end plate, and the act of designing the patient-specific implant comprises: designing the first endplate to mate with a topography of a first anatomy of the patient's spine; designing the second endplate to mate with the topography of a second anatomical structure of the patient's spine; designing the core to maintain or improve the kinematic parameter values ​​when the patient-specific implant is implanted in the patient; The system of claim 15, comprising:

23. Determining the target anatomical configuration includes: identifying one or more reference patient data sets; determining the target anatomical configuration based on the one or more reference patient data sets; The system of claim 15, comprising:

24. the operations further include generating manufacturing data associated with the patient-specific implant, the manufacturing data configured to instruct a manufacturing system to manufacture the patient-specific implant. The system of claim 15.

25. A non-transitory computer-readable storage medium storing instructions that, when executed by a computer system, cause the computer system to: image data of one or more regions of the patient's spine, the image data representing the native anatomical configuration of the one or more regions; kinematic data including values ​​of one or more kinematic parameters associated with the one or more regions of the patient's spine; acquiring patient data including: determining a target anatomical configuration for the one or more regions that differs from the original anatomical configuration; designing a patient-specific implant based at least in part on the target anatomical configuration and the kinematic parameter values; wherein the patient-specific implant, when implanted in the patient, is configured to provide the target anatomical correction while maintaining or improving the kinematic parameter values. A non-transitory computer-readable storage medium comprising:

26. The patient-specific implant is an artificial intervertebral disc having a first end plate, a second end plate, and a core between the first end plate and the second end plate, and the act of designing the patient-specific implant comprises: designing the first endplate to mate with a topography of a first anatomy of the patient's spine; designing the second endplate to mate with the topography of a second anatomical structure of the patient's spine; designing the core to provide the kinematic parameter values ​​when the patient-specific implant is implanted in the patient; 26. The non-transitory computer-readable storage medium of claim 25, comprising:

27. obtaining the kinematic data includes determining the values ​​of the one or more kinematic parameters based on the image data using one or more artificial intelligence architectures; 26. The non-transitory computer-readable storage medium of claim 25.

28. the one or more kinematic parameters comprise a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation, and the value of the one or more kinematic parameters comprises a value of a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation; 26. The non-transitory computer-readable storage medium of claim 25.

29. The operation is comparing the kinematic parameter values ​​to one or more kinematic criteria; If the kinematic parameter values ​​satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to maintain the kinematic parameter values ​​when implanted in the patient; if the kinematic parameter values ​​do not satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to improve the kinematic parameter values ​​when implanted in the patient; 26. The non-transitory computer-readable storage medium of claim 25, further comprising:

30. the kinematic criteria include reference kinematic parameter values; 26. The non-transitory computer-readable storage medium of claim 25.

31. the operations further include generating manufacturing data associated with the patient-specific implant, the manufacturing data configured to instruct a manufacturing system to manufacture the patient-specific implant.

26. The non-transitory computer-readable storage medium of claim 25.

32. 1. A computer-implemented method for designing a patient-specific implant, comprising: receiving image data of one or more regions of a patient's spine, the image data representing the native anatomical configuration of the one or more regions; measuring one or more kinematic parameters associated with the one or more regions; determining a target anatomical configuration that is different from the original anatomical configuration; designing a patient-specific implant based at least in part on the target anatomical configuration and the kinematic parameter values; wherein the patient-specific implant is configured to provide the target anatomical correction when implanted in the patient. A method characterized by:

33. measuring the one or more kinematic parameters of the one or more regions includes measuring the one or more kinematic parameters based on the image data.

33. The method of claim 32.

34. after measuring the one or more kinematic parameters, comparing the measured kinematic parameters to one or more kinematic criteria; if the measured kinematic parameters do not satisfy the one or more kinematic criteria; adjusting the measured kinematic parameters to satisfy the one or more kinematic criteria; designing the patient-specific implant to provide the adjusted kinematic parameters that satisfy the one or more kinematic criteria; 34. The method of claim 33, further comprising:

35. assessing the one or more kinematic parameters of the patient's spine when in the target anatomical configuration after receiving the one or more manipulations; comparing the assessed kinematic parameters of the patient's spine in the target anatomical configuration to one or more kinematic criteria; If the evaluated kinematic parameters do not satisfy the one or more kinematic criteria, (i) providing a prompt requesting the user to adjust the target anatomical configuration, and / or (ii) providing recommended adjustments to the target anatomical configuration.

33. The method of claim 32, further comprising:

36. the virtual model is a first virtual model, and the method comprises: generating a second virtual model of the patient-specific implant; combining the first virtual model and the second virtual model to form a combined virtual model of the patient-specific implant implanted in the patient's spine; 33. The method of claim 32, further comprising:

37. using the combined virtual model to estimate at least one of the one or more kinematic parameters; comparing the estimated kinematic parameters determined using the combined virtual model to one or more kinematic criteria; suggesting adjustments to the target anatomical configuration and / or the patient-specific implant if the evaluated kinematic parameters determined using the combined virtual model do not satisfy the one or more kinematic criteria; 33. The method of claim 32, further comprising:

38. the one or more kinematic criteria include the measured kinematic parameters; 38. The method of claim 37.

39. 1. A computer-implemented method for designing a patient-specific implant, comprising: receiving image data of one or more regions of the patient's spine; measuring values ​​of one or more kinematic parameters associated with said one or more regions; comparing the measured kinematic parameter values ​​to one or more kinematic criteria; If the kinematic parameter values ​​satisfy the one or more kinematic criteria, designing a patient-specific implant configured to maintain the kinematic parameter values ​​when implanted in the patient; if the kinematic parameter values ​​do not satisfy the one or more kinematic criteria, designing the patient-specific implant such that the patient-specific implant is configured to improve the kinematic parameter values ​​when implanted in the patient; A method comprising:

40. the kinematic criteria include reference kinematic parameter values; 40. The method of claim 39.

41. the one or more kinematic parameters comprise a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation, and the value of the one or more kinematic parameters comprises a value of a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation; 40. The method of claim 39.

42. the kinematic criteria are determined based on kinematic values ​​contained in one or more reference patient data sets; 40. The method of claim 39.

43. the one or more reference patient data sets are identified using an artificial intelligence architecture; 43. The method of claim 42.

44. 1. A computer-implemented method for designing a patient-specific artificial intervertebral disc, comprising: image data of one or more regions of the patient's spine showing a first vertebral body having a first vertebral endplate surface and a second vertebral body having a second vertebral endplate surface; kinematic data including values ​​of one or more kinematic parameters associated with the one or more regions of the patient's spine; acquiring patient data including: designing a patient-specific artificial disc based at least in part on the image data and the kinematic data; the patient-specific artificial disc is configured to be implanted between the first vertebra and the second vertebra, and includes (i) a first intervertebral disc endplate designed to mate with a first topography of the first vertebral body endplate surface, (ii) a second intervertebral disc endplate designed to mate with a second topography of the second vertebral body endplate surface, and (iii) a core between the first intervertebral disc endplate and the second intervertebral disc endplate designed to maintain or improve the kinematic parameter value when the patient-specific artificial disc is implanted in the patient. A method characterized by:

45. the first topography of the first vertebral endplate surface is different from the second topography of the second vertebral endplate surface; 45. The method of claim 44.

46. the first intervertebral disc endplate has a different geometry than the second intervertebral disc endplate; 46. ​​The method of claim 45.

47. the first intervertebral disc endplate has a different topography than the second intervertebral disc endplate; 46. ​​The method of claim 45.

48. the first intervertebral disc endplate is designed to mate with the first topography of the first vertebral body endplate surface such that the first intervertebral disc endplate contacts the first vertebral body endplate surface without a gap therebetween when the patient-specific artificial disc is implanted between the first vertebra and the second vertebra; the second disc endplate is designed to mate with the second topography of the second vertebral body endplate surface such that the second disc endplate contacts the second vertebral body endplate surface without a gap therebetween when the patient-specific artificial disc is implanted between the first vertebra and the second vertebra.

45. The method of claim 44.

49. the core comprises a combination of one or more elastomeric polymers, one or more rigid polymers, one or more ceramic materials, and / or one or more metallic materials; 45. The method of claim 44.

50. the core includes at least two mating surfaces configured to move relative to one another; 45. The method of claim 44.

51. the one or more kinematic parameters comprise a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation, and the values ​​of the one or more kinematic parameters comprise patient-specific values ​​of a range of motion, a flexion angle, a rotation angle, a displacement, flexion, extension, a flexion / extension arc, a lateral bending, a left / right flexion arc, and / or an axial rotation.

45. The method of claim 44.

52. 1. A patient-specific artificial intervertebral disc for insertion between a first vertebra and a second vertebra of a patient, comprising: a first endplate having a first patient-specific topography configured to mate with a first vertebra to form a substantially tight interface with the first vertebra; a second endplate having a second patient-specific topography configured to mate with a second vertebra to form a substantially tight interface with the second vertebra; Including, At least one of the first endplate or the second endplate is configured to move relative to the other, and the patient-specific artificial disc is further configured to maintain or improve one or more predetermined patient-specific kinematic parameter values ​​when implanted between the first vertebra and the second vertebra. A patient-specific artificial intervertebral disc.

53. further comprising a motion segment between the first end plate and the second end plate, the motion segment configured to maintain or improve the one or more predetermined patient-specific motion parameter values ​​when the patient-specific artificial disc is implanted between the first vertebra and the second vertebra.

53. The patient-specific artificial disc of claim 52.

54. the motion segment includes a core; 54. The patient-specific artificial disc of claim 53.

55. the motion segment comprises a combination of one or more elastomeric polymers, one or more rigid polymers, one or more ceramic materials, and / or one or more metallic materials; 54. The patient-specific artificial disc of claim 53.

56. the motion segment includes at least two mating surfaces configured to move relative to one another; 54. The patient-specific artificial disc of claim 53.

57. the first endplate forms an articulation interface with the second endplate, and the motion segment is defined at least in part by the articulation interface between the first endplate and the second endplate.

54. The patient-specific artificial disc of claim 53.

58. the first endplate and / or the second endplate are configured to maintain or improve the one or more predetermined patient-specific kinematic parameter values.

53. The patient-specific artificial disc of claim 52.

59. the one or more predetermined patient-specific motion parameter values ​​include patient-specific values ​​for range of motion, flexion angle, rotation angle, displacement, flexion, extension, flexion / extension arc, lateral bending, left / right flexion arc, and / or axial rotation; 53. The patient-specific artificial disc of claim 52.

60. the first patient-specific topography is different from the second patient-specific topography; 53. The patient-specific artificial disc of claim 52.

Images