The field of the invention generally relates to orthopedic implants, including spinal implants, and methods for designing and producing them.
Orthopedic implants are used to correct a variety of different maladies. Orthopedic surgery utilizing orthopedic implants may include one of a number of specialties, including: spine surgery, hand surgery, shoulder and elbow surgery, total joint reconstruction (arthroplasty), skull reconstruction, pediatric orthopedics, foot and ankle surgery, musculoskeletal oncology, surgical sports medicine, and orthopedic trauma. Spine surgery may encompass one or more of the cervical, thoracic, lumbar spine, or the sacrum, and may treat a deformity or degeneration of the spine, or related back pain, leg pain, or other body pain. Irregular spinal curvature may include scoliosis, lordosis, or kyphosis (hyper- or hypo-), and irregular spinal displacement may include spondylolisthesis. Other spinal disorders include osteoarthritis, lumbar degenerative disc disease or cervical degenerative disc disease, lumbar spinal stenosis or cervical spinal stenosis.
Spinal fusion surgery may be performed to set and hold purposeful changes imparted on the spine during surgery. Spinal fusion procedures include PLIF (posterior lumbar interbody fusion), ALIF (anterior lumbar interbody fusion), TLIF (transverse or transforaminal lumbar interbody fusion), or LLIF (lateral lumbar interbody fusion), including DLIF (direct lateral lumbar interbody fusion) or XLIF (extreme lateral lumbar interbody fusion).
The goal of interbody fusion is to grow bone between vertebra in order to seize the spatial relationships in a position that provides enough room for neural elements, including exiting nerve roots. An interbody implant device (or interbody implant, interbody cage, or fusion cage, or spine cage) is a prosthesis used in spinal fusion procedures to maintain relative position of vertebra and establish appropriate foraminal height and decompression of exiting nerves. Each patient may have individual or unique disease characteristics, but most implant solutions include implants (e.g. interbody implants) having standard sizes or shapes (stock implants).
A patient-specific medical device and an efficient method of producing a patient-specific interbody implant is described in the embodiments herein. Devices according to embodiments described herein may include interbody implants, fusion cages, or other implants. The interbody implants are typically intended to be placed in the space (created by surgical intervention) between two vertebrae. In fusion surgeries, the intervertebral disc may be surgically removed prior to the placement of the interbody implant. The lower (inferior) side of an interbody implant is intended to abut at least a portion of an upper (superior) side of a first vertebrae and the superior endplate of the interbody implant is intended to abut at least a portion of an inferior endplate of a second vertebrae.
Insufficient contact and load transfer between the vertebrae (anatomy) and the interbody implant (device) can produce inadequate fixation. Inadequate fixation can allow the cage to move relative to the vertebrae. Furthermore, insufficient contact area or fixation between the interbody implant and the vertebrae can result in micro- and/or macro-motions that can reduce the opportunity for bone growth and fusion to occur. If enough motion occurs, expulsion of the interbody implant or subsidence of the interbody implant into the adjacent vertebrae can result.
Traditional implants are selected intraoperatively from a surgical kit containing likely sizes and shapes depending on the surgical approach and patient anatomy. Selection of implant size is performed by the surgeon during the surgery while the patient's spine is exposed. Often, minimal consideration is paid to implant size prior to the surgery. The method for selecting implant size is “trialing,” whereby the surgeon uses a series of incrementally sized implant proxies to determine the appropriate implant size and shape. This method presents several opportunities for improvements.
Significant intra-operative attention is paid to the posterior height and sagittal angle of the interbody implants; however, minimal attention is paid to the lateral heights and coronal angle of the interbody implants. Even with the attention paid to the sagittal height, the implants available in surgery only come in stock sizes that are unlikely to provide optimal solutions for the particular patient or particular interbody space. Additionally, traditional stock implants do not provide any options for variable coronal angles. By selecting stock implants intraoperatively from a fixed assortment of implant sizes, the surgeon is unable to provide to the patient an optimal solution for correction of the particular spinal deformity or pathological malalignment causing patient pain.
Furthermore, intraoperative selection of stock implants requires shipment and delivery of sufficient implants to cover the wide variety of patients and their unique interbody spaces. The shipping, sterilization, processing, and delivery of enough implants to surgery can be characterized as logistically burdensome and expensive. It is not uncommon for more than fifty implants to be delivered to a surgery that requires only one implant.
In one typical fusion procedure, posterior fixation devices (pedicle screws, spinal rods) are used to stabilize the spine. Additionally, anterior interbody implants provide spacing and decompression of neural elements and a location for interbody fusion (bone growth between two vertebra).
Improper or sub-optimal sizing of interbody implants can result in implant failures. If the interbody space is not sufficiently filled, posterior implants (including rods and plates) are required to carry more dynamic loads prior to fusion. The typical failure mode of spinal rods include fracture due to dynamic loads; the increased magnitude of the movement due to an undersized interbody implant only exacerbates the condition, leading to more implant failures.
Patient-specific interbody implants can be designed for optimal fit in the negative space created by removal of the disc and adjustment of the relative position of vertebrae. Surgical planning software can be used to adjust the relative positions of vertebrae and define the negative space between the vertebrae. Modifying the spatial relationship between adjacent vertebrae within a virtual design space can provide a definition of the 3D negative space into which an interbody can be delivered. Software can further be used to compare the original pathology to the corrected positions of the vertebrae. The optimal size and shape of patient-specific implants can prevent or reduce instances of dynamic failure of posterior implants.
Presently, intraoperative imaging often requires radiation. Exposure to radiation should be reduced as low as reasonably possible. Surgeries using stock interbody implants require trialing to inform the selection of the stock implant. Patient-specific implants do not require trialing, as the size and shape of the implant has been determined prior to the surgery using preoperative imaging and planning software.
The imaging tools available to the surgeon during surgery typically only include mobile radiography (bedside x-ray, c-arm, o-arm). The use of mobile radiography exposes surgeons, staff, and patients to intraoperative radiation. The operating room environment does not provide the same radiation shielding capabilities that a standard dedicated radiology room provides (leaded walls, leaded glass, etc.). Because of the desire to reconcile radiographic images with visible (and invisible) anatomy, avoid sensitive anatomy, and understand relative anatomical positions, surgeons are often in close proximity to or within the field of radiation during intraoperative imaging. It is advantageous to reduce or eliminate radiation exposure to the participants of surgery.
One method of designing patient-specific interbody implants includes capturing important anatomical geometry and relative positioning using computed tomography (CT) or another imaging modality (MRI, simultaneous bi-planar radiography, etc.). The image data can be reconstructed into volumetric data containing voxels that are representative of anatomy. Following the scan, the collected data can be ported to a workstation with software to enable segmentation of relevant anatomy. A process called segmentation separates voxels representing bony anatomy from the other anatomy. Isolation of individual bony structures enables a user to appreciate each bony structure independently. Furthermore, following isolation, the relationships between individual vertebrae (distances, angles, constraints, etc.) can be manipulated. Together with a surgeon, an engineer can manipulate the vertebrae thereby changing the spacing between the virtual anatomical structures. Manipulations can include translations along an axis or curve, rotation about an axis or centroid, or rotation about the center of mass, among other movements. Consideration is to be paid to the virtual manipulations to ensure they are representative of anatomical constraints and manipulations that can be achieved in a surgical setting. After the virtual manipulations of select vertebrae, the newly created negative space between the vertebrae can be mapped and characterized using design software. One way of mapping the negative 3D space is to (1) select a bounding anatomical feature, such as a vertebral endplate, (2) create a best-fit plane through the surface, (3) define a perimeter of the anatomical feature, and (4) extrude a volume defined by the perimeter and perpendicular to the best-fit plane to the interface of another anatomical feature.
The newly created negative space between virtual vertebrae can be used to determine geometric parameters (dimensions, angles, heights, surfaces, topographies, footprints, etc.) and external envelope for optimal interbody implants.
After the external envelope for the patient-specific interbody (PSIB) implant has been determined, internal features, including lattice, struts, and apertures, can be designed. The internal features will determine the strength and bone incorporation qualities. Internal features can be engineered to provide favorable conditions for osteo-integration, bony on-growth, bony in-growth, and bony through-growth. Internal features can also be designed to resist or allow deformation, resulting in an optimal structural stiffness or compliance according to the physiological demands. In some patients, reducing the strength (stiffness) of the implant may create less instances of implant subsidence into the neighboring bones. In other patients, a stronger or stiffer implant may be designed to handle larger anticipated anatomical loads.
In some embodiments, a system and computer-implemented method for manufacturing an orthopedic implant involves segmenting features in an image of anatomy. The features can be anatomy of interest, such as bone, organs, etc. Anatomic elements (e.g., vertebrae, vertebral disks, etc.) can be isolated. Spatial relationships between the isolated anatomic elements can be manipulated. Before and/or after manipulating the spatial relationships, a negative space between anatomic elements can be mapped. At least a portion of the negative space can be filled with a virtual implant. The virtual implant can be used to select, design, and/or manufacture a patient-specific implant.
Each stock implant has several dimensions that vary for a specific instance of an implant (length, width, height, curvatures, radii, etc.). Although these dimensions are infinitely variable, space, logistics and expense limit inclusion of all instances within a surgical kit 50.
As seen in
Vertebrae 120 can be moved along coordinate systems 122 as defined by the user. Manipulations can occur as (1) translations along predetermined or user-defined axis, (2) rotations about predetermined or user-defined axis, (3) translations along predetermined or user-generated curves, and (4) rotations about predetermined or user-generated curves.
In one embodiment, coordinate systems 122 based on the centroid for each vertebra is displayed in order to facilitate manipulation of each vertebrae. In another embodiment, curvatures representing a best-fit curve between centroids of adjacent vertebrae is created. Another curve representing the optimal curvature of vertebrae can be used to manipulate vertebrae. A ‘snap’ feature can cause the vertebrae aligned in pathological conditions to automatically be positioned on a desired curve that represents optimal alignment for a patient.
In another embodiment, intersections between virtual solid models can be calculated. Where intersections or overlap of bony anatomy is detected by the planning software, they can be resolved by an engineer, technician, or physician. Anatomical constraints, such as facet joint mobility, angles of facet articulating surfaces, and articulating surface size, must be considered during the alignment of virtual vertebrae. By manipulating the virtual models of vertebrae, the negative three-dimensional space between the vertebrae can be appreciated. After correction of the virtual vertebrae has occurred, the negative space that results from the correction can be described. The description of the negative space can be used to inform the design of the interbody implant.
In one embodiment, implant boundary 276 can be drawn on plane 270 to represent an external shape of implant 216. Boundary 274 can be projected from plane 270 to opposing anatomical endplates 264, 266 to define the 3D shape of implant 216.
Implant 216 can be manufactured using one or more additive manufacturing or subtractive (traditional) manufacturing methods. Additive manufacturing methods include, but are not limited to: three-dimensional printing, stereolithography (SLA), selective laser melting (SLM), powder bed printing (PP), selective laser sintering (SLS), selective heat sintering (SHM), fused deposition modeling (FDM), direct metal laser sintering (DMLS), laminated object manufacturing (LOM), thermoplastic printing, direct material deposition (DMD), digital light processing (DLP), inkjet photo resin machining, and electron beam melting (EBM). Subtractive (traditional) manufacturing methods include, but are not limited to: CNC machining, EDM (electrical discharge machining), grinding, laser cutting, water jet machining, and manual machining (milling, lathe/turning).
Three columns containing six panes 156, 158, 160, 162, 164, 166 can be used to easily compare pathologic anatomy and corrected anatomy. In one embodiment, a column displaying information about the pathology with panes 156, 158 can show a virtual model of the spine 156 above the relative metrics of that spine 158. The displayed spine can be rotated (zoomed, panned, etc.) to better display areas of interest. Another column containing panes 160, 162 can display images and information (anatomic metrics) about the corrected spine and patient-specific implants in place.
The right column containing pane 164 can display images of pathological and corrected spine superimposed upon each other. The displayed spines can be rotated (zoomed, panned, etc.) to better display areas of interest. Pane 166 can display some important specifications of the patient-specific interbody implants, including posterior height, sagittal angle, coronal angle, anterior-posterior length, and width.
In each view, several dimensions are shown including, coronal angle 218, sagittal angle 220, left lateral height 222, right lateral height 223, width 224, posterior height 226, and anterior-posterior depth 230. Structural elements or struts 232 can been seen in the AP and lateral views 210, 212. Additionally, internal lattice 231 is shown. Lattice 231 can be designed to resist compressive loads and reduce incidences of subsidence in patients with reduced bone density, including those with osteoporosis.
Another feature of PSIB 216 is endplate topography 234. The endplate of the implant can be designed to match the irregular surface of the adjacent vertebral endplate. The topography can have macro- or micro-geometry to encourage fit, fixation, and fusion to the adjacent vertebral endplate.
In another embodiment, surfaces of the patient-specific interbody implant can be configured to encourage bone growth. It has been shown in clinical literature that structures having a particular pore size can encourage attachment of cells that become a precursor for bone formation. One embodiment can be configured to have the appropriate pore size to encourage bone formation.
Additionally, surfaces of the implant can be impregnated with therapeutic agents including anti-inflammatory compounds, antibiotics, or bone proteins. The impregnation could occur as a result of exposing the implant to solution containing the therapeutic agents, manufacturing therapeutic agents into the substrate or surface material, coating the implant with a therapeutic solution, among other methods. In one embodiment, the therapeutic agents can be configured for a timed release to optimize effectiveness.
A patient-specific implant can be manufactured based, at least in part, on the virtual implant configuration selected for the patient. Each patient can receive an implant that is specifically designed for their anatomy. In some procedures, the system 352 can handle the entire design and manufacturing process. In other embodiments, a physician can alter the implant configuration for further customization. An iterative design process can be employed in which the physician and system 352 work together. For example, the system 352 can generate a proposed patient-specific implant. The physician can identify characteristics of the implant to be changed and can input potential design changes. The system 352 can analyze the feedback from the physician to determine a refined patient-specific implant design and to produce a patient-specific model. This process can be repeated any number of times until arriving at a suitable design. Once approved, the implant can be manufactured based on the selected design.
The system 352 can include a surgical assistance system 364 that analyzes implant surgery information, for example, into arrays of integers or histograms, segments images of anatomy, manipulates relationships between anatomic elements, converts patient information into feature vectors, or extracts values from the pre-operative plan. The system 352 can store implant surgery information analyzed by the surgical assistance system 364. The stored information can include received images of a target area, such as MRI scans of a spine, digital images, X-rays, patient information (e.g., sex, weight, etc.), virtual models of the target area, a databased of technology models (e.g., CAD models), and/or a surgeon's pre-operative plan.
In some implementations, surgical assistance system 364 can analyze patient data to identify or develop a corrective procedure, identify anatomical features, etc. The anatomical features can include, without limitation, vertebra, vertebral discs, bony structures, or the like. The surgical assistance system 364 can determine the implant configuration based upon, for example, a corrective virtual model of the subject's spine, risk factors, surgical information (e.g., delivery paths, delivery instruments, etc.), or combinations thereof. In some implementations, the physician can provide the risk factors before or during the procedure. Patient information can include, without limitation, patient sex, age, bone density, health rating, or the like.
In some implementations, the surgical assistance system 364 can apply analysis procedures by supplying implant surgery information to a machine learning model trained to select implant configurations. For example, a neural network model can be trained to select implant configurations for a spinal surgery. The neural network can be trained with training items each comprising a set of images (e.g., camera images, still images, scans, MRI scans, CT scans, X-ray images, laser-scans, etc.) and patient information, an implant configuration used in the surgery, and/or a scored surgery outcome resulting from one or more of: surgeon feedback, patient recovery level, recovery time, results after a set number of years, etc. This neural network can receive the converted surgery information and provide output indicating the pedicle screw configuration.
The assistance system 364 can generate one or more virtual models (e.g., 2D models, 3D models, CAD models, etc.) for designing and manufacturing items. For example, the surgical assistance system 364 can build a virtual model of a surgery target area suitable for manufacturing surgical items, including implants. The surgical assistance system 364 can also generate implant manufacturing information, or data for generating manufacturing information, based on the computed implant configuration. The models can represent the patient's anatomy, implants, candidate implants, etc. The model can be used to (1) evaluate locations (e.g., map a negative 2D or 3D space), (2) select a bounding anatomical feature, such as a vertebral endplate, (3) create a best-fit virtual implant, (4) define a perimeter of the anatomical feature, and/or (5) extrude a volume defined by the perimeter and perpendicular to, for example, a best-fit plane to the interface of another anatomical feature. Anatomical features in the model can be manipulated according to a corrective procedure. Implants, instruments, and surgical plans can be developed based on the pre or post-manipulated model. Neural networks can be trained to generate and/or modify models, as well as other data, including manufacturing information (e.g., data, algorithms, etc.).
In another example, the surgical assistance system 364 can apply the analysis procedure by performing a finite element analysis on a generated three-dimensional model to assess, for example, stresses, strains, deformation characteristics (e.g., load deformation characteristics), fracture characteristics (e.g., fracture toughness), fatigue life, etc. The surgical assistance system 364 can generate a three-dimensional mesh to analyze the model. Machine learning techniques to create an optimized mesh based on a dataset of vertebrae, bones, implants, tissue sites, or other devices. After performing the analysis, the results could be used to refine the selection of implants, implant components, implant type, implantation site, etc.
The surgical assistance system 364 can perform a finite element analysis on a generated three-dimensional model (e.g., models of the spine, vertebrae, implants, etc.) to assess stresses, strains, deformation characteristics (e.g., load deformation characteristics), fracture characteristics (e.g., fracture toughness), fatigue life, etc. The surgical assistance system 364 can generate a three-dimensional mesh to analyze the model of the implant. Based on these results, the configuration of the implant can be varied based on one or more design criteria (e.g., maximum allowable stresses, fatigue life, etc.). Multiple models can be produced and analyzed to compare different types of implants, which can aid in the selection of a particular implant configuration.
The surgical assistance system 364 can incorporate results from the analysis procedure in suggestions. For example, the results can be used to suggest a surgical plan (e.g., a PLIF plan, a TLIF plan, a LLIF plan, a ALIF plan, etc.), select and configure an implant for a procedure, annotate an image with suggested insertions points and angles, generate a virtual reality or augmented reality representation (including the suggested implant configurations), provide warnings or other feedback to surgeons during a procedure, automatically order the necessary implants, generate surgical technique information (e.g., insertion forces/torques, imaging techniques, delivery instrument information, or the like), etc. The suggestions can be specific to implants. In some procedures, the surgical assistance system 364 can also be configured to provide suggestions for conventional implants. In other procedures, the surgical assistance system 364 can be programmed to provide suggestions for patient-specific or customized implants. The suggestion for the conventional implants may be significantly different from suggestions for patient-specific or customized implants.
The system 352 can simulate procedures using a virtual reality system or modeling system. One or more design parameters (e.g., dimensions, implant configuration, instrument, guides, etc.) can be adjusted based, at least in part, on the simulation. Further simulations (e.g., simulations of different corrective procedures) can be performed for further refining implants. In some embodiments, design changes are made interactively with the simulation and the simulated behavior of the device based on those changes. The design changes can include material properties, dimensions, or the like.
The surgical assistance system 364 can improve efficiency, precision, and/or efficacy of implant surgeries by providing more optimal implant configuration, surgical guidance, customized surgical kits (e.g., on-demand kits), etc. This can reduce operational risks and costs produced by surgical complications, reduce the resources required for preoperative planning efforts, and reduce the need for extensive implant variety to be prepared prior to an implant surgery. The surgical assistance system 364 provides increased precision and efficiency for patients and surgeons.
In orthopedic surgeries, the surgical assistance system 364 can select or recommend implants, surgical techniques, patient treatment plans, or the like. In spinal surgeries, the surgical assistance system 364 can select interbody implants, pedicle screws, and/or surgical techniques to make surgeons more efficient and precise, as compared to existing surgical kits and procedures. The surgical assistance system 364 can also improve surgical robotics/navigation systems, and provide improved intelligence for selecting implant application parameters. For example, the surgical assistance system 364 empowers surgical robots and navigation systems for spinal surgeries to increase procedure efficiency and reduce surgery duration by providing information on types and sizes, along with expected insertion angles. In addition, hospitals benefit from reduced surgery durations and reduced costs of purchasing, shipping, and storing alternative implant options. Medical imaging and viewing technologies can integrate with the surgical assistance system 364, thereby providing more intelligent and intuitive results.
The surgical assistance system 364 can include one or more input devices 420 that provide input to the processor(s) 345 (e.g., CPU(s), GPU(s), HPU(s), etc.), notifying it of actions. The input devices 320 can be used to manipulate a model of the spine, as discussed in connection with
The system 352 can include a display 300 used to display text, models, virtual procedures, surgical plans, implants, and graphics. In some implementations, display 330 provides graphical and textual visual feedback to a user. In some implementations, display 330 includes the input device as part of the display, such as when the input device is a touchscreen or is equipped with an eye direction monitoring system. The processors 345 can communicate with a hardware controller for devices, such as for a display 330. In some implementations, the display is separate from the input device. Examples of display devices are: an LCD display screen, an LED display screen, a projected, holographic, or augmented reality display (such as a heads-up display device or a head-mounted device), and so on. Other I/O devices 340 can also be coupled to the processors 345, such as a network card, video card, audio card, USB, firewire or other external device, camera, printer, speakers, CD-ROM drive, DVD drive, disk drive, or Blu-Ray device. Other I/O 340 can also include input ports for information from directly connected medical equipment such as imaging apparatuses, including MRI machines, X-Ray machines, CT machines, etc. Other I/O 340 can further include input ports for receiving data from these types of machine from other sources, such as across a network or from previously captured data, for example, stored in a database.
In some implementations, the system 352 also includes a communication device capable of communicating wirelessly or wire-based with a network node. The communication device can communicate with another device or a server through a network using, for example, TCP/IP protocols. System 452 can utilize the communication device to distribute operations across multiple network devices, including imaging equipment, manufacturing equipment, etc.
The system 452 can include memory 350. The processors 345 can have access to the memory 350, which can be in a device or distributed across multiple devices. Memory 350 includes one or more of various hardware devices for volatile and non-volatile storage, and can include both read-only and writable memory. For example, a memory can comprise 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, device buffers, and so forth. A memory is not a propagating signal divorced from underlying hardware; a memory is thus non-transitory. Memory 350 can include program memory 360 that stores programs and software, such as an operating system 362, surgical assistance system 364, and other application programs 366. Memory 350 can also include data memory 370 that can include, e.g., implant information, configuration data, settings, user options or preferences, etc., which can be provided to the program memory 360 or any element of the system 352, such as the manufacturing system 367. The system 452 can be programmed to perform the methods discussed in connection with
The patient data can include images of the patient's body, clinician input, treatment plan information, or the like. The corrected model can be generated by processing (e.g., segmenting, filtering, edge detection, partitioning, etc.) the images and then analyzing, for example, anatomical features of interest. Anatomical features can be manipulated (e.g., resized, moved, translated, rotated, etc.) to generate the corrected model. The corrected model can be used to simulate different procedures with different virtual implants. At block 550, patient-specific implants (e.g., implant 110 of
The methods (e.g., methods 400 and 500) can include other steps disclosed herein. Some implementations can be operational with numerous other computing system environments or configurations. Examples of computing systems, environments, and/or configurations that may be suitable for use with the technology include, but are not limited to, personal computers, server computers, handheld or laptop devices, cellular telephones, wearable electronics, tablet devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, or the like.
The embodiments, features, systems, devices, materials, methods and techniques described herein may, in some embodiments, be similar to any one or more of the embodiments, features, systems, devices, materials, methods and techniques described in the following:
All of the above-identified patents and applications are incorporated by reference in their entireties. In addition, the embodiments, features, systems, devices, materials, methods and techniques described herein may, in certain embodiments, be applied to or used in connection with any one or more of the embodiments, features, systems, devices, or other matter.
The ranges disclosed herein also encompass any and all overlap, sub-ranges, and combinations thereof. Language such as “up to,” “at least,” “greater than,” “less than,” “between,” and the like includes the number recited. Numbers preceded by a term such as “approximately”, “about”, and “substantially” as used herein include the recited numbers (e.g., about 10%=10%), and also represent an amount close to the stated amount that still performs a desired function or achieves a desired result. For example, the terms “approximately”, “about”, and “substantially” may refer to an amount that is within less than 10% of, within less than 5% of, within less than 1% of, within less than 0.1% of, and within less than 0.01% of the stated amount.
While embodiments have been shown and described, various modifications may be made without departing from the scope of the inventive concepts disclosed herein.
This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62/730,336, filed Sep. 12, 2018, which is hereby incorporated by reference in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
4704686 | Aldinger | Nov 1987 | A |
4936862 | Walker et al. | Jun 1990 | A |
5431562 | Andreiko et al. | Jul 1995 | A |
D420995 | Imamura | Feb 2000 | S |
D436580 | Navano | Jan 2001 | S |
6315553 | Sachdeva | Nov 2001 | B1 |
6540512 | Sachdeva | Apr 2003 | B1 |
6696073 | Boyce et al. | Feb 2004 | B2 |
6772026 | Bradbury | Aug 2004 | B2 |
6932842 | Litschko et al. | Aug 2005 | B1 |
6978188 | Christensen | Dec 2005 | B1 |
6988241 | Guttman | Jan 2006 | B1 |
7174282 | Hollister et al. | Feb 2007 | B2 |
7187790 | Sabol et al. | Mar 2007 | B2 |
D548242 | Viegers | Aug 2007 | S |
D614191 | Takano | Apr 2010 | S |
7747305 | Dean et al. | Jun 2010 | B2 |
7756314 | Karau et al. | Jul 2010 | B2 |
7799077 | Lang | Sep 2010 | B2 |
D633514 | Tokunaga | Mar 2011 | S |
D656153 | Imamura | Mar 2012 | S |
8246680 | Betz | Aug 2012 | B2 |
8265949 | Haddad | Sep 2012 | B2 |
8275594 | Lin | Sep 2012 | B2 |
8337507 | Lang | Dec 2012 | B2 |
8394142 | Bertagnoli | Mar 2013 | B2 |
8457930 | Shroeder | Jun 2013 | B2 |
8532806 | Masson | Sep 2013 | B1 |
8556983 | Bojarski et al. | Oct 2013 | B2 |
8644568 | Hoffman | Feb 2014 | B1 |
8735773 | Lang | May 2014 | B2 |
8758357 | Frey | Jun 2014 | B2 |
8775133 | Schroeder | Jul 2014 | B2 |
8781557 | Dean | Jul 2014 | B2 |
8843229 | Vanasse | Sep 2014 | B2 |
8855389 | Hoffman | Oct 2014 | B1 |
8870889 | Frey | Oct 2014 | B2 |
9020788 | Lang | Apr 2015 | B2 |
D735231 | Omiya | Jul 2015 | S |
D737309 | Kito | Aug 2015 | S |
9198678 | Frey et al. | Dec 2015 | B2 |
9208558 | Dean | Dec 2015 | B2 |
D757025 | Kim | May 2016 | S |
D761842 | Johnson | Jul 2016 | S |
9411939 | Furrer | Aug 2016 | B2 |
9445907 | Meridew | Sep 2016 | B2 |
9452050 | Miles et al. | Sep 2016 | B2 |
D774076 | Fuller | Dec 2016 | S |
9542525 | Arisoy et al. | Jan 2017 | B2 |
9642633 | Frey et al. | May 2017 | B2 |
9693831 | Mosnier et al. | Jul 2017 | B2 |
9707058 | Bassett | Jul 2017 | B2 |
9715563 | Schroeder | Jul 2017 | B1 |
D797760 | Tsujimura | Sep 2017 | S |
D797766 | Ibsies | Sep 2017 | S |
D798312 | Tsujimura | Sep 2017 | S |
9757245 | O'Neil et al. | Sep 2017 | B2 |
D798894 | Ibsies | Oct 2017 | S |
9775680 | Bojarski et al. | Oct 2017 | B2 |
9782228 | Mosnier et al. | Oct 2017 | B2 |
D812628 | Okado | Mar 2018 | S |
9993341 | Vanasse | Jun 2018 | B2 |
10034676 | Donner | Jul 2018 | B2 |
D825605 | Jann | Aug 2018 | S |
D826977 | Nakajima | Aug 2018 | S |
10089413 | Wirx-Speetjens et al. | Oct 2018 | B2 |
D841675 | Hoffman | Feb 2019 | S |
10213311 | Mafhouz | Feb 2019 | B2 |
D845973 | Jaycobs | Apr 2019 | S |
D845974 | Cooperman | Apr 2019 | S |
D847165 | Kolbenheyer | Apr 2019 | S |
D848468 | Ng | May 2019 | S |
D849029 | Cooperman | May 2019 | S |
D849773 | Jiang | May 2019 | S |
10292770 | Ryan | May 2019 | B2 |
10299863 | Grbic et al. | May 2019 | B2 |
D854560 | Field | Jul 2019 | S |
D854561 | Field | Jul 2019 | S |
10390958 | Maclennan | Aug 2019 | B2 |
D860237 | Li | Sep 2019 | S |
D860238 | Bhardwaj | Sep 2019 | S |
D866577 | Eisert | Nov 2019 | S |
D867379 | Ang | Nov 2019 | S |
D867389 | Jamison | Nov 2019 | S |
10463433 | Turner et al. | Nov 2019 | B2 |
D870762 | Mendoza | Dec 2019 | S |
10512546 | Kamer et al. | Dec 2019 | B2 |
10517681 | Roh et al. | Dec 2019 | B2 |
D872117 | Kobayashi | Jan 2020 | S |
D872756 | Howell | Jan 2020 | S |
D874490 | Dodsworth | Feb 2020 | S |
D875761 | Heffernan | Feb 2020 | S |
D876454 | Knowles | Feb 2020 | S |
D876462 | Li | Feb 2020 | S |
D877167 | Knowles | Mar 2020 | S |
D879112 | Hejazi | Mar 2020 | S |
10588589 | Bregman-Amitai et al. | Mar 2020 | B2 |
10603055 | Donner et al. | Mar 2020 | B2 |
D880513 | Wang | Apr 2020 | S |
D881908 | Sunil | Apr 2020 | S |
D881910 | Lin | Apr 2020 | S |
10621289 | Schroeder | Apr 2020 | B2 |
10631988 | Arnold et al. | Apr 2020 | B2 |
D884008 | Thornberg | May 2020 | S |
10646236 | Donner et al. | May 2020 | B2 |
10646258 | Donner et al. | May 2020 | B2 |
10736698 | Bohl | Aug 2020 | B2 |
10751188 | Guo et al. | Aug 2020 | B2 |
D896825 | Abel | Sep 2020 | S |
D896828 | Linares | Sep 2020 | S |
D898054 | Everhart | Oct 2020 | S |
D899438 | Crafts | Oct 2020 | S |
10806597 | Sournac et al. | Oct 2020 | B2 |
10902944 | Casey et al. | Jan 2021 | B1 |
D916868 | Evangeliou | Apr 2021 | S |
D916879 | Mitsumori | Apr 2021 | S |
D918253 | Choe | May 2021 | S |
11000334 | Young | May 2021 | B1 |
D921675 | Kmak | Jun 2021 | S |
D921677 | Kmak | Jun 2021 | S |
D921687 | Kmak | Jun 2021 | S |
D924909 | Nasu | Jul 2021 | S |
D925567 | Hayamizu | Jul 2021 | S |
D927528 | Heisler | Aug 2021 | S |
D933692 | Smith | Oct 2021 | S |
D937870 | Pinto | Dec 2021 | S |
D937876 | Harvey | Dec 2021 | S |
D938461 | Hoffman | Dec 2021 | S |
D938986 | Grossberg | Dec 2021 | S |
D940178 | Ang | Jan 2022 | S |
D946022 | Nuttbrown | Mar 2022 | S |
D946023 | Nuttbrown | Mar 2022 | S |
D946024 | Vogler-Ivashchanka | Mar 2022 | S |
D946616 | Tsai | Mar 2022 | S |
D958151 | Casey et al. | Jul 2022 | S |
20020007294 | Bradbury et al. | Jan 2002 | A1 |
20040104512 | Eidenschink | Jun 2004 | A1 |
20040171924 | Mire et al. | Sep 2004 | A1 |
20050049590 | Alleyne et al. | Mar 2005 | A1 |
20050271996 | Sporbert et al. | Dec 2005 | A1 |
20060009780 | Foley | Jan 2006 | A1 |
20070118243 | Schroeder | May 2007 | A1 |
20070276501 | Betz et al. | Nov 2007 | A1 |
20080161680 | von Jako | Jul 2008 | A1 |
20080195240 | Martin | Aug 2008 | A1 |
20080227047 | Lowe | Sep 2008 | A1 |
20090062739 | Anderson | Mar 2009 | A1 |
20100191088 | Anderson | Jul 2010 | A1 |
20100292963 | Schroeder | Nov 2010 | A1 |
20100324692 | Uthgenannt | Dec 2010 | A1 |
20110218545 | Catanzarite et al. | Sep 2011 | A1 |
20110301710 | Mather et al. | Dec 2011 | A1 |
20120010710 | Frigg | Jan 2012 | A1 |
20120084064 | Dzenis et al. | Apr 2012 | A1 |
20120116203 | Vancraen | May 2012 | A1 |
20120150243 | Crawford et al. | Jun 2012 | A9 |
20120191192 | Park | Jul 2012 | A1 |
20120287238 | Onishi | Nov 2012 | A1 |
20120296433 | Farin | Nov 2012 | A1 |
20120322018 | Lowe | Dec 2012 | A1 |
20130211531 | Steines et al. | Aug 2013 | A1 |
20130323669 | Lowe | Dec 2013 | A1 |
20140072608 | Karagkiozaki et al. | Mar 2014 | A1 |
20140074438 | Furrer | Mar 2014 | A1 |
20140081659 | Nawana et al. | Mar 2014 | A1 |
20140086780 | Miller | Mar 2014 | A1 |
20140100886 | Woods | Apr 2014 | A1 |
20140164022 | Reed et al. | Jun 2014 | A1 |
20140263674 | Cerveny | Sep 2014 | A1 |
20140350614 | Frey | Nov 2014 | A1 |
20150079533 | Lowe | Mar 2015 | A1 |
20150105891 | Golway et al. | Apr 2015 | A1 |
20150199488 | Falchuk | Jul 2015 | A1 |
20150213225 | Amarasingham | Jul 2015 | A1 |
20150324490 | Page | Nov 2015 | A1 |
20150328004 | Mafhouz | Nov 2015 | A1 |
20150332018 | Rosen | Nov 2015 | A1 |
20160001039 | Armour et al. | Jan 2016 | A1 |
20160015465 | Steines et al. | Jan 2016 | A1 |
20160030067 | Frey et al. | Feb 2016 | A1 |
20160074048 | Pavlovskaia et al. | Mar 2016 | A1 |
20160117817 | Seel | Apr 2016 | A1 |
20160143744 | Bojarski et al. | May 2016 | A1 |
20160184054 | Lowe | Jun 2016 | A1 |
20160210374 | Mosnier et al. | Jul 2016 | A1 |
20160217268 | Otto et al. | Jul 2016 | A1 |
20160242857 | Scholl | Aug 2016 | A1 |
20160300026 | Bogoni et al. | Oct 2016 | A1 |
20160354039 | Soto et al. | Dec 2016 | A1 |
20160378919 | McNutt et al. | Dec 2016 | A1 |
20170000566 | Gordon | Jan 2017 | A1 |
20170014169 | Dean et al. | Jan 2017 | A1 |
20170020679 | Maclennan | Jan 2017 | A1 |
20170035514 | Fox et al. | Feb 2017 | A1 |
20170061375 | Laster et al. | Mar 2017 | A1 |
20170068792 | Reiner | Mar 2017 | A1 |
20170135706 | Frey et al. | May 2017 | A1 |
20170143494 | Mahfouz | May 2017 | A1 |
20170143831 | Varanasi et al. | May 2017 | A1 |
20170216047 | Hawkes et al. | Aug 2017 | A1 |
20170220740 | D'Urso | Aug 2017 | A1 |
20170252107 | Turner et al. | Sep 2017 | A1 |
20170262595 | Vorhis | Sep 2017 | A1 |
20170340447 | Mahfouz | Nov 2017 | A1 |
20170354510 | O'Neil et al. | Dec 2017 | A1 |
20170367645 | Klinder | Dec 2017 | A1 |
20180008349 | Gillman | Jan 2018 | A1 |
20180113992 | Eltorai et al. | Apr 2018 | A1 |
20180116727 | Caldwell et al. | May 2018 | A1 |
20180168499 | Bergold et al. | Jun 2018 | A1 |
20180168731 | Reid et al. | Jun 2018 | A1 |
20180185075 | She | Jul 2018 | A1 |
20180233222 | Daley | Aug 2018 | A1 |
20180233225 | Experton et al. | Aug 2018 | A1 |
20180250075 | Cho | Sep 2018 | A1 |
20180303552 | Ryan | Oct 2018 | A1 |
20180303616 | Bhattacharyya et al. | Oct 2018 | A1 |
20180308569 | Luellen | Oct 2018 | A1 |
20180338841 | Miller et al. | Nov 2018 | A1 |
20190029757 | Roh et al. | Jan 2019 | A1 |
20190065685 | Pickover | Feb 2019 | A1 |
20190146458 | Roh et al. | May 2019 | A1 |
20190167435 | Cordonnier | Jun 2019 | A1 |
20190201106 | Siemionow | Jul 2019 | A1 |
20190262084 | Roh et al. | Aug 2019 | A1 |
20190266597 | Mohtar | Aug 2019 | A1 |
20190282367 | Casey et al. | Sep 2019 | A1 |
20190321193 | Casey et al. | Oct 2019 | A1 |
20190328929 | Kugler et al. | Oct 2019 | A1 |
20190333622 | Levin | Oct 2019 | A1 |
20190354693 | Yoon | Nov 2019 | A1 |
20190380792 | Poltaretskyi et al. | Dec 2019 | A1 |
20200021570 | Lin | Jan 2020 | A1 |
20200085509 | Roh et al. | Mar 2020 | A1 |
20200170802 | Casey et al. | Jun 2020 | A1 |
20200261156 | Schmidt | Aug 2020 | A1 |
20200289288 | Müller et al. | Sep 2020 | A1 |
20200315708 | Mosnier et al. | Oct 2020 | A1 |
20210059822 | Casey et al. | Mar 2021 | A1 |
20210064605 | Balint | Mar 2021 | A1 |
20210145519 | Mosnier et al. | May 2021 | A1 |
20210210189 | Casey et al. | Jul 2021 | A1 |
20210287770 | Anderson | Sep 2021 | A1 |
20210382457 | Roh et al. | Dec 2021 | A1 |
20220000556 | Casey et al. | Jan 2022 | A1 |
20220000625 | Cordonnier | Jan 2022 | A1 |
20220006642 | Maj et al. | Jan 2022 | A1 |
20220039965 | Casey et al. | Feb 2022 | A1 |
20220047402 | Casey et al. | Feb 2022 | A1 |
20220110686 | Roh et al. | Apr 2022 | A1 |
20220160405 | Casey et al. | May 2022 | A1 |
20220160518 | Casey et al. | May 2022 | A1 |
Number | Date | Country |
---|---|---|
104318009 | Jan 2015 | CN |
104353121 | Feb 2015 | CN |
204468348 | Jul 2015 | CN |
105796214 | Jul 2016 | CN |
106202861 | Dec 2016 | CN |
107220933 | Sep 2017 | CN |
113643790 | Nov 2017 | CN |
108670506 | Oct 2018 | CN |
110575289 | Dec 2019 | CN |
111281613 | Jun 2020 | CN |
112155792 | Jan 2021 | CN |
3120796 | Jan 2017 | EP |
9507509 | Mar 1995 | WO |
2004110309 | Dec 2004 | WO |
2010151564 | Dec 2010 | WO |
2012154534 | Nov 2012 | WO |
2014180972 | Nov 2014 | WO |
2016172694 | Oct 2016 | WO |
2019112917 | Jun 2019 | WO |
2019148154 | Aug 2019 | WO |
2022045956 | Mar 2022 | WO |
Entry |
---|
International Search Report and Written Opinion for International Patent Application No. PCT/US21/12065, dated Apr. 29, 2021 (19 pages). |
U.S. Appl. No. 15/958,409 for Ryan, filed Apr. 21, 2017. |
Endo, Kenji et al. “Measurement of whole spine sagittal alignment using the SLOT radiography of the SONIALVISION satire series clinical application.” Medical Now, No. 78; Aug. 2015, 4 pages. |
International Searching Authority, International Search Report and Written Opinion, PCT Patent Application PCT/US2018/063530, dated Feb. 12, 2019, 16 pages. |
Materialise Mimics, “Efficiently turn scans into accurate virtual 3D models,” <www.materialize.com/en/medical/software/mimics>, 1 page. |
Pimenta, Dr. Luiz, “Current Surgical Strategies to Restore Proper Sagittal Alignment,” Journal of Spine 2015, vol. 4. Issue 4, 2 pages. |
Extended European Search Report for European Application No. 18885367.5, dated Aug. 16, 2021, 8 pages. |
International Search Report and Written Opinion for International Application No. PCT/US21/44878, dated Nov. 16, 2021, 18 pages. |
International Search Report and Written Opinion for International Application No. PCT/US21/45503, dated Jan. 11, 2022, 19 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US21/59837, dated Feb. 7, 2022, 19 pages. |
Majdouline et al., “Preoperative assessment and evaluation of instrumentation strategies for the treatment of adolescent idiopathic scoliosis: computer simulation and optimization.” Scoliosis 7, 21 (2012), pp. 1-8. |
Eshkalak, S.K. et al., “The role of three-dimensional printing in healthcare and medicine.” Materials and Design 194, Jul. 10, 20202, 15 pages. |
Extended European Search Report for European Application No. 19859930.0, dated Jun. 22, 2022, 7 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US21/60074, dated Mar. 17, 2022, 21 pages. |
Pruthi, G. et al., “Comprehensive review of guidelines to practice prosthodontic and implant procedures during COVID-19 pandemic.” Journal of Oral Biology and Craniofacial Research 10, Oct. 17, 2020, 8 pages. |
U.S. Appl. No. 17/463,054 for Casey et al., filed Aug. 31, 2021. |
U.S. Appl. No. 17/518,524 for Cordonnier, filed Nov. 3, 2021. |
U.S. Appl. No. 17/678,874 for Cordonnier, filed Feb. 23, 2022. |
U.S. Appl. No. 17/702,591 for Roh et al., filed Mar. 23, 2022. |
U.S. Appl. No. 17/835,777 for Cordonnier, filed Jun. 8, 2022. |
U.S. Appl. No. 17/838,727 for Casey et al., filed Jun. 13, 2022. |
U.S. Appl. No. 17/842,242 for Cordonnier, filed Jun. 16, 2022. |
U.S. Appl. No. 17/851,487 for Cordonnier, filed Jun. 28, 2022. |
U.S. Appl. No. 17/856,625 for Cordonnier, filed Jul. 1, 2022. |
U.S. Appl. No. 17/867,621 for Cordonnier, filed Jul. 18, 2022. |
U.S. Appl. No. 17/875,699 for Casey et al., filed Jul. 28, 2022. |
U.S. Appl. No. 17/878,633 for Cordonnier, filed Aug. 1, 022. |
U.S. Appl. No. 17/880,277 for Casey et al., filed Aug. 3, 2022. |
International Search Report and Written Opinion received for counterpart International Application No. PCT/US2019/050885; dated Jan. 28, 2020 (17 pages). |
International Search Report and Written Opinion received for International Application No. PCT/US2019/063855; dated Feb. 14, 2020 (15 pages). |
Number | Date | Country | |
---|---|---|---|
20200078180 A1 | Mar 2020 | US |
Number | Date | Country | |
---|---|---|---|
62730336 | Sep 2018 | US |