Patient-specific adjustment of spinal implants and related systems and methods - Patents.com

Abstract

The computer system receives readings from a sensor embedded in a spinal implant that is implanted in the patient during surgery. The sensor readings are indicative of a load applied to the spinal implant by the patient's spine. The load causes physical discomfort to the patient. A machine learning module is used to extract a feature vector from the implant sensor readings. The feature vector is indicative of the physical discomfort caused by the load. An electrical signal is generated using the machine learning module based on the feature vector. The machine learning module is trained based on a patient dataset to generate an electrical signal to balance the load such that the physical discomfort is reduced. The electrical signal is transmitted to one or more actuators embedded in the spinal implant, causing the one or more actuators to configure the spinal implant to balance the load.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 215,784, filed June 28, 2021, the disclosure of which is incorporated by reference in its entirety herein.

[0002] (Technical field) The present disclosure relates generally to the design and delivery of medical care, and more particularly to systems and methods for designing and delivering patient-specific adjustments of spinal implants. [Background technology]

[0003] Orthopedic implants are used to correct many different conditions in a variety of settings, 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. Spinal surgery itself encompasses a variety of procedures and targets in one or more of the cervical, thoracic, lumbar, and sacrum, and can be performed to treat spinal deformities or degeneration and / or associated back pain, leg pain, and other body pain. Common spinal deformities that can be treated using spinal implants include irregular spinal curvatures such as scoliosis, lordosis, kyphosis (hypercurvature or hypocurvature), and irregular spinal displacements (e.g., spondylolisthesis). Other spinal conditions that can be treated with spinal 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 / 990,810 [Patent Document 2] U.S. Patent Application Serial No. 16 / 987,113 [Patent Document 3] U.S. Patent Application Serial No. 17 / 100,396 [Patent Document 4] U.S. Patent Application Serial No. 17 / 835,777 [Patent Document 5] U.S. Patent Application Serial No. 16 / 990,810 Summary of the Invention

[0005] The accompanying drawings illustrate embodiments of the systems, methods, and various other aspects of the present disclosure. Those skilled in the art will appreciate that the boundaries of illustrated elements in the drawings (e.g., boxes, groups of boxes, or other shapes) represent one example of boundaries. In some examples, an element can be designed as multiple elements, or multiple elements can 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 drawings are not necessarily to scale, with emphasis instead being placed on illustrating the principles. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a network connectivity diagram illustrating a system for providing patient-specific medical care, according to one embodiment. [Diagram 2] FIG. 2 illustrates a computing device suitable for use in connection with the illustrated system, according to one embodiment. [Diagram 3] FIG. 1 is a flow diagram illustrating a method for providing patient-specific medical care, according to one embodiment. [Figure 4A] FIG. 1 illustrates an exemplary data set that may be used and / or generated in connection with the methods described herein, according to one embodiment, and shows a patient data set. [Figure 4B] FIG. 1 illustrates an exemplary dataset that may be used and / or generated in connection with the methods described herein, according to one embodiment, and shows a reference patient dataset. [Figure 4C]FIG. 4C illustrates an exemplary dataset that may be used and / or generated in connection with the methods described herein, according to one embodiment, and shows similarity scores and result scores for the reference patient dataset of FIG. 4B. [Diagram 5] FIG. 1 is a flow diagram illustrating another method for providing patient-specific medical care, according to one embodiment. [Figure 6] FIG. 1 is a partial schematic diagram of a surgical setup and associated computer system for providing patient-specific medical care, according to one embodiment. [Figure 7A] FIG. 1 illustrates an exemplary patient data set that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 7B] FIG. 1 illustrates an exemplary patient data set that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 7C] FIG. 1 illustrates an exemplary patient data set that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 7D] FIG. 1 illustrates an exemplary patient data set that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 8A] FIG. 2 illustrates an exemplary virtual model of a patient's spine that can be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 8B] FIG. 2 illustrates an exemplary virtual model of a patient's spine that can be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 9A-1] FIG. 1 illustrates an exemplary virtual model of a patient's spine in a pre-operative anatomical configuration. [Figure 9A-2] FIG. 1 illustrates an exemplary virtual model of a patient's spine in a pre-operative anatomical configuration. [Figure 9B-1] FIG. 1 illustrates an exemplary virtual model of a patient's spine in a corrected anatomical configuration. [Figure 9B-2] FIG. 1 illustrates an exemplary virtual model of a patient's spine in a corrected anatomical configuration. [Figure 10] FIG. 1 illustrates an exemplary surgical plan for a patient-specific surgical procedure that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 11A] FIG. 11 illustrates an exemplary surgical plan report that details the surgical plan shown in FIG. 10 for a surgeon's review, according to one embodiment, and that can be used and / or generated in connection with the methods described herein. [Figure 11B] FIG. 11 illustrates an exemplary surgical plan report that details the surgical plan shown in FIG. 10 for a surgeon's review, according to one embodiment, and that can be used and / or generated in connection with the methods described herein. [Figure 12A] FIG. 1 illustrates an exemplary patient-specific implant that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 12B] FIG. 1 illustrates an exemplary patient-specific implant that may be used and / or generated in connection with the methods described herein, according to one embodiment. [Figure 13] FIG. 1 illustrates a segment of a patient's spine after multiple patient-specific implants have been implanted, according to one embodiment. [Figure 14A] 1 is a schematic anterior view of a patient-specific interbody fusion device deployed between a first vertebra (e.g., a relatively superior vertebra) and a second vertebra (e.g., a relatively inferior vertebra), according to one embodiment. [Figure 14B] 1 is a schematic side view of a patient-specific interbody fusion device deployed between a first vertebra (e.g., a relatively superior vertebra) and a second vertebra (e.g., a relatively inferior vertebra), according to one embodiment. [Figure 15] 1 is a flow chart illustrating a process for patient-specific adjustment of a spinal implant, according to one embodiment. [Figure 16A] FIG. 1 illustrates a patient's spine and a remote device for controlling the actuation of an intervertebral implant, according to one embodiment. [Figure 16B]FIG. 1 illustrates an example correction plan that may be used and / or generated in connection with the systems and methods described herein, according to one embodiment. [Figure 17A] 1A-1D illustrate a patient's spine in different configurations, according to one embodiment. [Figure 17B] 1A-1D illustrate a patient's spine in different configurations, according to one embodiment. [Figure 17C] 1A-1D illustrate a patient's spine in different configurations, according to one embodiment. [Figure 17D] 1A-1D illustrate a patient's spine in different configurations, according to one embodiment. [Figure 18] FIG. 1 is a side view of an interbody fusion device and spinal fastener, according to one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The present technology is directed to systems and methods for designing and implementing patient-specific adjustments of spinal implants. Spinal fusion, also known as spondylodesis or spinal fusion, is a neurosurgical or orthopedic surgical technique that joins two or more vertebrae. Spinal fusion can be used to treat a variety of conditions that affect any level of the spine, i.e., lumbar, cervical, and thoracic. Generally, spinal fusion is performed to decompress and stabilize the spine, thereby preventing movement between the fused vertebrae. Spinal fusion is most commonly performed to relieve mechanical pain of the vertebrae or pain and pressure on the spinal cord that occurs when the disc wears down (e.g., resulting from degenerative disc disease). Other common pathologies treated by spinal fusion include spinal stenosis, spondylolisthesis, spondylosis, spinal fractures, scoliosis, kyphosis, and the like.

[0008] Spinal fusion procedures can utilize interbody fusion (IBF) devices. IBF devices can help restore interbody height, restore lordosis and coronal misalignment, and / or stabilize the spine until bony fusion occurs between the vertebral bodies. Exemplary IBF devices can be configured for anterior lumbar interbody fusion (ALIF), lateral lumbar interbody fusion (LLIF), oblique lateral interbody fusion, posterior lumbar interbody fusion (PLIF), or translumbar interbody fusion (TLIF). In some embodiments, the IBF device can be a cervical cage. The IBF device can also have multiple expandable mechanisms that provide intraoperative adjustment capabilities. In some embodiments, the expandable IBF device also provides adjustment capabilities (e.g., preoperative, intraoperative, and / or postoperative adjustment capabilities) of, for example, spinal curvature, vertebral height, lordosis recovery, and / or coronal recovery.

[0009] Although the disclosure herein primarily describes systems and methods for treatment planning in the orthopedic context, the technology may be applied to medical procedures and devices in other fields (e.g., other types of surgical practices) as well. Additionally, while many embodiments herein describe systems and methods relating to implanted devices, the technology may be applied to other types of medical devices (e.g., non-implanted devices) as well.

[0010] For example, in many embodiments disclosed herein, the computer system receives implant sensor readings from one or more implant sensors of a spinal implant configured in a first physical configuration according to a patient's correction plan. The implant sensor readings are received after surgery has been performed and are indicative of loads applied to the spinal implant by the patient's spine. The computer system extracts a feature vector from the implant sensor readings using a machine learning module of the computer system. The feature vector is indicative of a target correction. The computer system generates an implant electrical signal using the machine learning module based on the feature vector. The machine learning module is trained based on a patient data set and generates an implant electrical signal for, for example, adjusting the load to achieve the target correction. The computer system transmits the implant electrical signal to the spinal implant to move the spinal implant to a second physical configuration for the target correction. In some embodiments, the spinal implant is post-operatively adjusted according to a predetermined adjustable implant correction plan that accounts for one or more of disease progression, additional surgical interventions, aging, sensed metrics, and the like.

[0011] In some embodiments, the computer system receives patient data, an anatomical configuration of the patient's spine is determined based on the received patient data, the computer system identifies a target correction based on the anatomical configuration and available adjustment capabilities of the spinal implant, and the identified target correction is used to extract a feature vector.

[0012] In some embodiments, the correction plan comprises criteria for implanting a spinal implant.

[0013] In some embodiments, the computer system receives device sensor readings from one or more device sensors embedded in an intervertebral fusion device implant implanted in the patient during surgery. The device sensor readings are received after the surgery is performed and before the implant sensor readings are received. The computer system generates a device electrical signal using a machine learning module based on the device sensor readings. The machine learning module is trained based on a patient dataset to generate device sensor readings to reduce physical discomfort caused by the intervertebral fusion device.

[0014] In some embodiments, the feature vector further indicates at least one of lumbar lordosis (LL), Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine.

[0015] In some embodiments, configuring the spinal implant into the second physical configuration includes adjusting, with one or more implant actuators, at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a connector, a tether, a fastener, or a joint replacement of the spinal implant.

[0016] In some embodiments, configuring the spinal implant in the second physical configuration includes adjusting a reservoir coupled to the spinal implant to change the amount of at least one of a pharmaceutical, biological, biochemical, anesthetic, or steroid delivered to the patient.

[0017] In some embodiments, a method of providing medical care includes comparing a patient dataset of a patient to be treated to a plurality of reference patient datasets (e.g., data of previously treated patients). The method can include selecting a subset of the reference patient dataset based, for example, on similarity between the reference patient dataset and the patient dataset and / or whether the reference patient had a good treatment outcome. The selected subset can be used to generate a surgical procedure and / or medical device design that is more likely to result in a favorable treatment outcome for the particular patient. In some embodiments, the selected subset is analyzed to identify correlations between the patient's pathology, surgical procedure, device design, and / or treatment outcome, and these correlations are used to determine a personalized treatment protocol that is more likely to be successful.

[0018] In the context of orthopedic surgery, systems with improved computing capabilities (e.g., predictive analytics, machine learning, neural networks, artificial intelligence (AI)) can use large data sets to define improved or optimal surgical interventions and / or implant designs for a particular patient. A patient's entire data can be characterized and compared to aggregated data (e.g., parameters, metrics, pathology, treatments, outcomes) from a cohort of previous patients. In some embodiments, the systems described herein use this aggregated data to develop potential treatment solutions (e.g., surgical plans and / or implant designs for spinal and orthopedic surgery) and analyze the associated likelihood of success. These systems can further compare potential treatment solutions to determine an optimal patient-specific solution that is expected to maximize the likelihood of success.

[0019] For example, if a patient presents with a spinal deformity pathology that can be described with data including lumbar lordosis, Cobb angle, coronal parameters (coronal balance, global coronal balance, coronal pelvic tilt, etc.), sagittal parameters (pelvic incidence ratio (PI), sacral tilt, thoracic kyphosis, etc.), and / or pelvic parameters, an algorithm using these data points as inputs can be used to describe an optimal surgical plan and / or implant design to correct the subject's pathology and improve patient outcomes. If additional data inputs are used to describe the pathology (e.g., disc height, segmental flexibility, bone quality, rotational displacement), the algorithm can use these additional inputs to further define an optimal surgical plan and / or implant design for that particular patient and these pathologies.

[0020] In some embodiments, the present technology can automatically or at least semi-automatically determine a corrected anatomical configuration for a target patient suffering from one or more deformities. For example, the computer system described herein can apply mathematical rules to selected parameters (e.g., LL, Cobb angle, etc.) and / or identify similar patients by analyzing a reference patient data set and provide a recommended anatomical configuration that represents an optimal outcome for the target patient if they undergo surgery based on the rules and / or comparison with other patients. In some embodiments, the systems and methods described herein generate a virtual model of the corrected / recommended anatomical configuration (e.g., for review by a surgeon).

[0021] In some embodiments, the technology can also automatically or at least semi-automatically generate a surgical plan to achieve a corrected anatomical configuration previously identified for a target patient. For example, based on a virtual model of the corrected anatomical configuration, the systems and methods herein can determine the type of surgery (e.g., spinal fusion surgery, non-fusion surgery, etc.), the surgical approach (e.g., anterior, posterior, etc.), and / or the spinal parameters (e.g., LL, Cobb angle, etc.) of the corrected anatomical configuration. The surgical plan can be sent to the surgeon for review and approval. In some embodiments, the technology can also design one or more patient-specific implants to achieve the corrected anatomical configuration via the surgical plan.

[0022] In some embodiments, the present technology provides systems and methods for generating multiple anatomical models of a patient. For example, a first model can represent the patient's natural (e.g., pre-operative) anatomical configuration, and a second model can provide a simulation of the patient's corrected (e.g., post-operative) anatomical configuration. The second virtual model can optionally include one or more virtual implants shown as implanted in one or more target regions of the patient. Spinal metrics (e.g., LL, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, etc.) can also be provided for both the pre-operative anatomical configuration and the expected post-operative anatomical configuration.

[0023] In some embodiments, the techniques include generating, designing, and / or providing a patient-specific medical procedure for multiple locations within a patient. For example, the techniques can include identifying at least two target regions or sites within a patient for surgical intervention (e.g., a first vertebral level and a second vertebral level). The techniques can then design at least two patient-specific implants for implantation at the at least two target regions. Each of the at least two patient-specific implants can be specifically designed for a respective target region and therefore can have a different shape. In some embodiments, the corrected anatomical configuration of the patient is achieved solely by implanting each of the at least two patient-specific implants. In the context of spinal surgery, for example, the techniques can provide a first patient-specific interbody device implanted between the L2 and L3 vertebral bodies, a second patient-specific interbody device implanted between the L3 and L4 vertebral bodies, and a third patient-specific interbody device implanted between the L4 and L5 vertebral bodies.

[0024] In some embodiments, the technology can predict, model, or simulate disease progression in a particular patient to aid in diagnosis and / or treatment planning. Simulations can be performed to model and / or estimate the patient's future anatomical configuration and / or spinal metrics (a) in the absence of surgical intervention, or (b) for a variety of different surgical intervention options. Thus, progression modeling can be used to determine the optimal timing of surgical intervention and / or to select which surgical intervention will provide the best long-term outcome. In some embodiments, disease progression modeling is performed using one or more machine learning models trained on multiple reference patients.

[0025] In a specific, non-limiting example, the present technology includes a method for providing patient-specific medical care to a target patient. The method can include receiving a patient dataset for the target patient, the patient dataset including one or more images of the patient's spinal region showing the patient's native anatomical configuration. The method can further include determining a corrected anatomical configuration of the target patient that differs from the native anatomical configuration and creating a virtual model of the corrected anatomical configuration. The method can further include generating a surgical plan and designing one or more patient-specific implants for achieving the corrected anatomical configuration in the target patient. In an exemplary embodiment, the above-described method can be performed by a system having stored thereon computer-executable instructions that, when executed, cause the system to perform the steps of the method.

[0026] In a specific, non-limiting example, the present technology includes a method for designing a patient-specific orthopedic implant for a target patient. The method can include receiving a patient dataset for the target patient, the patient dataset including spinal pathology data of the target patient. The patient dataset can be compared to a plurality of reference patient datasets to identify one or more similar patient datasets in the reference patient dataset, each identified similar patient dataset corresponding to a reference patient having a spinal pathology similar to the target patient and treated with an orthopedic implant. The method can further include selecting a subset of the one or more similar patient datasets based on whether the similar patient datasets indicated that the reference patient followed a successful outcome after implantation of the orthopedic implant. The method can further include identifying, for at least one similar reference patient of the selected subset, surgical procedure data and design data of a respective orthopedic implant that provided a successful outcome for the similar reference patient. Based on the design data and the surgical procedure data that provided a successful outcome in the similar reference patient, a patient-specific orthopedic implant for the target patient and a surgical procedure for implanting the patient-specific orthopedic implant in the target patient can be designed. In some embodiments, the method may further include outputting manufacturing instructions to cause a manufacturing system to manufacture the patient-specific orthopaedic implant according to the generated design. In an exemplary embodiment, the above-described method may be performed by a system storing computer-executable instructions that, when executed, cause the system to perform the steps of the method.

[0027] In some embodiments, the IBF device is individualized to the patient's specific characteristics and / or concerns according to a preoperative plan for height restoration, lordotic and coronal correction, and / or optimal endplate coverage. For example, an expandable IBF device according to the present technology can include a patient-specific endplate that can achieve optimal surface area contact and / or provide a mechanism for adjusting medical intervention from the IBF device (e.g., adjusting segmental height restoration, lordotic correction, and / or coronal correction). In some embodiments, the patient-specific endplate is the result of additive and / or subtractive manufacturing. The patient-specific endplate can then be connected to an expandable mechanism that can also provide a predetermined height restoration, lordotic correction, and / or coronal correction via one or more expansion mechanisms (e.g., expandable jacks, scissor jack mechanisms, screw-driven mechanisms, etc.). In some such embodiments, the expansion mechanism includes one or more joints (e.g., ball joints), hinges, or other connections that can be precisely adjusted to a predetermined angle and then temporarily or permanently locked.

[0028] In an exemplary embodiment, the IBF device includes an expansion mechanism configured to be locked in a desired expansion configuration to promote fusion. The expansion mechanism can include a first lockable ball joint on an upper surface of the mechanism and a second lockable ball joint on a lower surface of the mechanism. The IBF device also includes a first endplate coupled to the mechanism at the first lockable ball joint. In some embodiments, the first endplate includes an upper surface having one or more patient-specific features configured to engage and mate with a topology of an inferior surface of a superior vertebra. The IBF device also includes a second endplate connected to the mechanism at a second lockable ball joint. In some embodiments, the second endplate includes a lower surface having one or more patient-specific features configured to engage and mate with a topology of an upper surface of a lower vertebra.

[0029] In some embodiments, the patient-specific features of the first and / or second endplates can improve the fit between the IBF device and the vertebrae being fused, thereby increasing the traction of the IBF device at the joint. For example, the one or more patient-specific features can customize the fit of the first and second endplates in response to topographical features of the surfaces of the vertebral bodies at the intervertebral joint. In some embodiments, the patient-specific features of the first and / or second endplates can include one or more features that help facilitate a given medical procedure. For example, the first and / or second endplates can include a tilt that helps provide lordotic and / or coronal correction to the patient's spine. In some embodiments, the expandable body includes a screw-jack mechanical expansion mechanism. In some embodiments, the expandable body includes a scissor-jack mechanical expansion mechanism.

[0030] For ease of reference, patient-specific implants are sometimes described herein with reference to up and down, upper and lower, and / or horizontal, xy, vertical, or z directions relative to the spatial orientation of the embodiments shown in the figures. However, it should be understood that patient-specific implants can be moved to and used in different spatial orientations without altering the structure and / or function of the disclosed embodiments of the technology.

[0031] DETAILED DESCRIPTION OF THE DRAWINGS

[0023] 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 in which exemplary embodiments are shown. However, the claimed embodiments may be embodied in many different forms and should not be construed as being limited to the embodiments described herein. The examples described herein are non-limiting examples and are merely examples among other possible implementations. The words "comprising," "having," "containing," and "including," as well as other forms of these, are intended to be equivalent in meaning and open-ended in that the item or items following any one of these words are not intended to be an exhaustive listing of such item or items, or to be limited to only the listed item or items.

[0032] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0033] Moreover, although described herein primarily as a method for customizing an interbody fusion device and the resulting IBF device, those skilled in the art will appreciate that the scope of the invention is not so limited. For example, the patient-specific customization methods disclosed herein can also be used to customize implants for a variety of other medical procedures, such as for insertion into other joints within a patient's body. Thus, the scope of the invention is not limited to any subset of embodiments, but rather is limited only by the limitations set forth in the appended claims.

[0034] 1 is a network diagram illustrating a computer system 100 for providing patient-specific medical care, according to one embodiment. As described in more detail herein, the system 100 is configured to generate a medical treatment plan for a patient. In some embodiments, the system 100 is configured to generate a medical treatment plan for a patient suffering from an orthopedic or spinal disease or disorder, such as trauma (e.g., fracture), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), irregular spinal curvature (e.g., scoliosis, lordosis, kyphosis), irregular spinal displacement (spondylolisthesis, lateral displacement, axial displacement, etc.), osteoarthritis, lumbar degenerative discopathy, cervical degenerative discopathy, lumbar spinal stenosis, cervical spinal stenosis, or a combination thereof. The medical treatment plan may include surgical information, a surgical plan, a technique recommendation (e.g., device and / or instrument recommendation), and / or a medical device design. For example, a medical treatment plan may include at least one therapeutic procedure (e.g., a surgery or intervention) and / or at least one medical device (e.g., an implanted medical device (also referred to herein as an "implant" or "implanted device") or an implant delivery instrument).

[0035] In some embodiments, the system 100 generates a medical treatment plan customized for a particular patient or group of patients, also referred to herein as a "patient-specific" or "individualized" treatment plan. A patient-specific treatment plan can include at least one patient-specific surgical plan and / or at least one patient-specific medical device designed and / or optimized for a patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history). 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 will be understood that a patient-specific treatment plan can also include aspects that are not customized for a particular patient. For example, a patient-specific or personalized surgical procedure can include one or more instructions, parts, steps, etc. that are non-patient specific. Similarly, a patient-specific or personalized medical device can include one or more components that are non-patient specific and / or can be used with non-patient specific instruments or tools. The personalized implant design can be used to manufacture or select patient-specific techniques, including medical devices, instruments, and / or surgical kits. For example, a personalized surgical kit may include one or more patient-specific devices, patient-specific instruments, non-patient-specific technology (e.g., standard instruments, equipment, etc.), instructions for use, patient-specific treatment planning information, or a combination thereof.

[0036] The system 100 includes a client computing device 102, which may be a user device such as a smartphone, a mobile device, a laptop, a desktop, a personal computer, a tablet, a phablet, or other such devices known in the art. As discussed further herein, the client computing device 102 may include one or more processors and a memory that stores instructions executable by the one or more processors to perform the methods described herein. The client computing device 102 may be associated with a healthcare provider treating a patient. Although FIG. 1 illustrates a single client computing device 102, in alternative embodiments, the client computing device 102 may instead be implemented as a client computing system that encompasses multiple computing devices such that the operations described herein with respect to the client computing device 102 may instead be performed by a computing system and / or computing device.

[0037] The client computing device 102 is configured to receive a patient dataset 108 associated with a patient to be treated. The patient dataset 108 may include data representative of the patient's condition, anatomical structure, pathology, medical history, preferences, and / or other information or parameters associated with the patient. For example, the patient dataset 108 may include medical history, surgical intervention data, treatment outcome data, progress data (e.g., physician's notes), patient feedback (e.g., feedback obtained using quality of life questionnaires, surveys), clinical data, provider information (e.g., physician, hospital, surgical team), patient information (e.g., demographics, gender, age, height, weight, type of condition, occupation, activity level, organizational information, health assessment, comorbidities, Health Related Quality of Life (HRQL)), vital signs, diagnosis results, medication information, allergies, imaging data (e.g., camera images, magnetic resonance imaging (MRI) images, ultrasound images, computer-aided tomography (CAT) scan images, positron emission tomography (PET) images, x-ray images), diagnostic equipment information (e.g., manufacturer, model number, specifications, user selected settings / configuration, etc.), or the like. In some embodiments, the patient dataset 108 includes data representative of one or more of the following: patient identification number (ID), age, sex, body mass index (BMI), LL, Cobb angle, PI, disc height, segment flexibility, bone quality, rotational displacement, and / or treatment level of the spine.

[0038] The client computing device 102 is operatively connected to the server 106 via a communications network 104, thus enabling data transfer between the client computing device 102 and the server 106. The communications network 104 may be a wired and / or wireless network. If the communications network 104 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.

[0039] Server 106, which may also be referred to as a "treatment support network" or a "prescriptive analytics network," may include one or more computing devices and / or systems. As discussed further herein, server 106 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 106 is implemented as a distributed "cloud" computer system or facility across any suitable combination of hardware and / or virtual computing resources.

[0040] The client computing device 102 and the server 106 may individually or collectively perform various methods described herein to provide patient-specific medical care. For example, some or all of the steps of the methods described herein may be performed by the client computing device 102 alone, the server 106 alone, or a combination of the client computing device 102 and the server 106. Thus, although certain operators are described herein with respect to the server 106, it should be understood that these operators may also be performed by the client computing device 102, and vice versa.

[0041] The server 106 includes at least one database 110 configured to store reference data useful for the treatment planning methods described herein. The reference data can 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 manufacturers or other medical device manufacturers, data from imaging studies, data from simulations, clinical trials, demographic data, treatment data, outcome data, mortality, etc.

[0042] In some embodiments, the database 110 includes a plurality of reference patient data sets, each patient reference data set associated with a corresponding reference patient. For example, the reference patient may be a patient who has previously been treated or a patient who is currently being treated. Each reference patient data set may include data representative of the corresponding reference patient's condition, anatomical structure, pathological structure, medical history, disease progression, preferences, and / or other information or parameters associated with the reference patient, such as any of the data described herein with respect to the patient data set 108. In some embodiments, the reference patient data set includes pre-operative data, intra-operative data, and / or post-operative data. For example, the reference patient data set may include data representative of one or more of a patient ID, age, sex, BMI, LL, Cobb angle, PI, disc height, segment flexibility, bone quality, rotational displacement, and / or spinal treatment level. As another example, the reference patient data set may include treatment data for at least one treatment procedure performed on the reference patient, such as a description of a surgical procedure or intervention (e.g., a surgical approach, a bone resection, a surgical operation, a corrective operation, a placement of an implant or other device). In some embodiments, the treatment data includes medical device design data of at least one medical device used to treat the reference patient, such as 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). In yet another example, the reference patient dataset can include outcome data describing the results of the treatment of the reference patient, such as corrected anatomical metrics, whether or not union occurred, HRQL, activity level, return to work, complications, recovery time, efficacy, mortality, and / or follow-up surgeries.

[0043] In some embodiments, the server 106 receives at least a portion of the reference patient datasets from a plurality of healthcare provider computer systems (e.g., systems 112a-112c, collectively 112). The server 106 may be connected to the healthcare provider computer systems 112 via one or more communication networks (not shown). Each healthcare provider computer system 112 may be associated with a corresponding healthcare provider (e.g., doctor, surgeon, clinic, hospital, healthcare network, etc.). Each healthcare provider computer system 112 may include at least one reference patient dataset (e.g., reference patient datasets 114a-114c, collectively 114) associated with a reference patient treated by the corresponding healthcare provider. The reference patient datasets 114 may include, for example, electronic medical records, electronic health records, biomedical datasets, etc. The reference patient datasets 114 may be received by the server 106 from the healthcare provider computer systems 112 and may be reformatted into different formats for storage in the database 110. Optionally, the reference patient dataset 114 may be processed (e.g., cleaned) to ensure that the patient parameters represented are likely to be useful in the treatment planning methods described herein.

[0044] As described in further detail herein, the server 106 may be configured with one or more algorithms that generate patient-specific treatment plan data (e.g., treatment procedure, medical device) based on the reference data. In some embodiments, the patient-specific data is generated based on a correlation between the patient dataset 108 and the reference data. Optionally, the server 106 may predict outcomes including recovery time, efficacy based on clinical endpoints, likelihood of success, predicted mortality, predicted related follow-up surgery, or the like. In some embodiments, the server 106 may 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, and the like.

[0045] In some embodiments, the server 106 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 106 includes a data analysis module 116 and a treatment planning module 118. In alternative embodiments, one or more of these modules may be combined with one another or omitted. Thus, although certain operations are described herein with respect to a particular module or modules, this is not intended to be limiting and such operations may be performed by a different module or modules in alternative embodiments.

[0046] The data analysis module 116 is configured with one or more algorithms for identifying a subset of reference data from the database 110 that is likely to be useful in developing a patient-specific treatment plan. For example, the data analysis module 116 can compare the patient-specific data (e.g., the patient dataset 108 received from the client computing device 102) with reference data (e.g., a reference patient dataset) from the database 110 to identify similar data (e.g., one or more similar patient datasets in the reference patient dataset). The comparison can be based on one or more parameters, such as age, sex, BMI, LL, PI, and / or treatment level. The parameters can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient dataset 108 and the reference patient dataset. Thus, similar patients can be identified based on whether the similarity score is above, below, or at a specified threshold. For example, the comparison can be performed by assigning a value to each parameter and determining an aggregate difference between the subject patient and each reference patient, as described in more detail below. Reference patients whose aggregated differences fall below a threshold can be considered similar patients.

[0047] The data analysis module 116 may further comprise one or more algorithms for selecting a subset of the reference patient dataset based on, for example, similarity to the patient dataset 108 and / or treatment outcomes of corresponding reference patients. For example, the data analysis module 116 may identify one or more similar patient datasets in the reference patient dataset and select the subset of the similar patient datasets based on whether the similar patient datasets contain data indicative of a favorable or desired treatment outcome. The outcome data may include data representing one or more outcome parameters, such as corrected anatomical metrics, presence or absence of union, HRQL, activity level, complications, recovery time, efficacy, mortality, or follow-up surgery. In some embodiments, as described in more detail below, the data analysis module 116 calculates an outcome score by assigning a value to each outcome parameter. If the outcome score is above, below, or at a predefined threshold, the patient is considered to have a good outcome.

[0048] In some embodiments, the data analysis module 116 selects the subset of the reference patient dataset based at least in part on user input (e.g., from a clinician, surgeon, physician, health care provider). For example, the user input can be used in identifying similar patient datasets. In some embodiments, the weighting of the similarity and / or outcome parameters can be selected by the health care provider or physician to adjust the similarity and / or outcome score based on the clinician's input. In further embodiments, the health care provider or physician can select the set of similarity and / or outcome parameters (or define new similarity and / or outcome parameters) used to generate the similarity and / or outcome score, respectively.

[0049] In some embodiments, the data analysis module 116 includes one or more algorithms used to select a set or subset of reference patient datasets based on criteria other than patient parameters. For example, one or more algorithms can be used to select the subset based on provider parameters (e.g., based on provider rankings / scores, such as hospital / physician specialty, number of procedures performed, hospital rankings, etc.) and / or medical resource parameters (e.g., diagnostic equipment, facilities, surgical equipment, such as surgical robots), or other non-patient related information that can be used to predict outcome and risk profile of procedures for the current provider. For example, reference patient datasets with images captured from similar diagnostic equipment can be aggregated to reduce or limit irregularities due to variability between diagnostic equipment. Additionally, patient-specific treatment plans can be created for a particular provider using data from similar providers (e.g., providers that traditionally have similar outcomes, physician expertise, surgical teams, etc.). In some embodiments, reference provider datasets, hospital datasets, physician datasets, surgical team datasets, post-treatment datasets, and other datasets can be utilized. As an 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 surgery systems. The reference patient dataset can be selected based on patients operated on using comparable robotic surgery systems under similar conditions (e.g., surgical team size and capabilities, hospital resources, etc.).

[0050] The treatment planning module 118 is configured with one or more algorithms to generate at least one treatment plan (e.g., pre-operative plan, surgical plan, post-operative plan, etc.) based on the output from the data analysis module 116. In some embodiments, the treatment planning module 118 is configured to develop and / or implement at least one predictive model for generating a patient-specific treatment plan, also referred to as a "prescriptive model." The predictive model may be developed using clinical knowledge, statistics, machine learning, AI, neural networks, or the like. In some embodiments, the output from the data analysis module 116 is analyzed (e.g., using statistics, machine learning, neural networks, AI) to identify correlations between data sets, patient parameters, healthcare provider parameters, healthcare resource parameters, treatment procedures, medical device designs, and / or treatment outcomes. These correlations may be used to develop 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 may be validated, for example, by inputting data into the model and comparing the output of the model to an expected output.

[0051] In some embodiments, the treatment planning module 118 is configured to generate a treatment plan based on previous treatment data from a reference patient. For example, the treatment planning module 118 can receive a selected subset of the reference patient dataset and / or similar patient dataset from the data analysis module 116 and determine or identify treatment data from the selected subset. The treatment data can include, for example, treatment procedure data (e.g., surgical or intervention data) and / or medical device design data (e.g., implant design data) that are to be transferred to a preferred or desired treatment outcome of the corresponding patient. The treatment planning module 118 can analyze the treatment procedure data and / or medical device design data to determine an optimal treatment protocol for the treated patient. For example, values ​​can be assigned to the treatment procedures and / or medical device designs and aggregated to generate a treatment score. A patient-specific treatment plan can be determined by selecting a treatment plan based on a score (e.g., a higher or highest score; a lower or lowest score; a score above, below, or at 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.

[0052] Alternatively or in combination, the treatment planning module 118 can generate a treatment plan based on correlations between data sets. For example, the treatment planning module 118 can correlate therapeutic procedure data and / or medical device design data from similar patients with good outcomes (e.g., as identified by the data analysis module 116). The correlation analysis can include converting the correlation coefficient values ​​into values ​​or scores. The values / scores can be aggregated, filtered, or otherwise analyzed to determine one or more statistical significances. These correlations can be used to determine therapeutic procedures and / or medical device designs that are likely to be optimal or provide favorable outcomes for the treated patient.

[0053] Alternatively or in combination, the treatment planning module 118 can generate the treatment plan using one or more AI techniques. AI techniques can be used to develop computer systems that can simulate aspects of human intelligence, such as learning, reasoning, planning, problem solving, decision making, etc. AI techniques can 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.

[0054] In some embodiments, the treatment plan module 118 generates the treatment plan using one or more trained machine learning models. A wide variety 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 with a training dataset, which is a dataset of examples used to fit the model's parameters (e.g., the connection weights between "neurons" in an artificial neural network). For example, the training dataset may include any of the reference data stored in the database 110, such as a plurality of reference patient datasets or a selected subset thereof (e.g., a plurality of similar patient datasets).

[0055] In some embodiments, a machine learning model (e.g., a neural network or a naive Bayes classifier) ​​can be trained on a training dataset using a supervised learning method (e.g., gradient descent or stochastic gradient descent). The training dataset can include pairs of generated "input vectors" and associated corresponding "answer vectors" (commonly denoted as targets). The current model is run with the training dataset to generate results for each input vector in the training dataset that are compared to the targets. Based on the results of the comparison and the particular learning algorithm being used, the parameters of the model are adjusted. Model fitting can include both variable selection and parameter estimation. The fitted model can be used to predict responses to observations in a second dataset, called the validation dataset. The validation dataset can provide an unbiased assessment of the model fit on the training dataset while tuning the model parameters. The validation dataset can be used for regularization with early stopping, for example, by stopping training when the error on the validation dataset increases, as this may be a sign of overfitting to the training dataset. In some embodiments, since the error on the validation data set can vary during training, ad-hoc rules can be used to determine when overfitting has truly set in. Finally, a test data set can be used to provide an unbiased assessment of the fit of the final model to the training data set.

[0056] To generate a treatment plan, the patient dataset 108 can be input into the trained machine learning model. Additional data, such as a selected subset of the reference patient dataset and / or similar patient datasets, and / or treatment data from the selected subset, can also be input into the trained machine learning model. The trained machine learning model can then calculate whether various candidate treatment procedures and / or medical device designs are likely to result in a favorable outcome for the patient. Based on these calculations, the trained machine learning model can select at least one treatment plan for the patient. In embodiments where multiple trained machine learning models are used, the models can be run sequentially or simultaneously to compare results and can be periodically updated using the training dataset. The treatment plan module 118 can use one or more of the machine learning models based on the predictive accuracy scores of the models.

[0057] The patient-specific treatment plan generated by the treatment planning module 118 may include at least one patient-specific treatment procedure (e.g., surgery or intervention) and / or at least one patient-specific medical device (e.g., an implant or implant delivery instrument). The patient-specific treatment plan may include an entire surgical plan or a portion thereof. Additionally, one or more patient-specific medical devices may be specifically selected or designed for a corresponding surgical procedure and thus may be used to combine various components of a patient-specific technique to treat the patient.

[0058] In some embodiments, the patient-specific therapeutic procedure includes orthopedic surgery such as spine surgery, hip surgery, knee surgery, temporomandibular joint surgery, wrist surgery, shoulder surgery, elbow surgery, total joint reconstruction (arthroplasty), skull reconstruction, foot or ankle surgery. The spine surgery can include spinal fusion such as PLIF, ALIF, transverse or TLIF, LLIF, direct lateral lumbar interbody fusion (DLIF), or far lateral lumbar interbody fusion (XLIF). In some embodiments, the patient-specific therapeutic procedure includes instructions and / or instructions for performing one or more aspects of the patient-specific surgery. For example, the patient-specific surgery can include one or more of a surgical approach, a corrective manipulation, a bone resection, or a placement of an implant.

[0059] 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 devices (e.g., cages, plates, rods, discs, fusion devices, spacers, rods, expandable devices, stents, brackets, ties, scaffolds, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, joint replacements, hip implants. Examples of instruments include, but are not limited to, screw guides, cannulas, ports, catheters, insertion instruments, etc.

[0060] A patient-specific medical device design may include data describing 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 a corresponding medical device. For example, an orthopedic implant design may include the shape, size, material, and / or effective stiffness (e.g., lattice density, number of struts, location of struts, etc.) of the implant. In some embodiments, the generated patient-specific medical device design is a design for the entire device. Alternatively, the generated design may be for one or more components of the device rather than the entire device.

[0061] 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 components and patient-specific customized components. In some embodiments, the generated design is for a patient-specific medical device that can be used with standard off-the-shelf delivery instruments. For example, the implants (screws, screw holders, rods, etc.) can be designed and manufactured for the patient, and the instruments that deliver the implants can be standard instruments. This approach allows the components to be implanted to be designed and manufactured based on the patient's anatomy and / or surgeon's preferences to enhance treatment. The patient-specific devices 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.

[0062] In embodiments where the patient-specific treatment plan includes a surgical procedure to implant a medical device, the treatment planning module 118 can also store various types of implant surgery information, such as implant parameters (e.g., type, size), availability of implants, aspects of the pre-operative plan (e.g., initial implant configuration, detection and measurement of the patient's anatomy, etc.), FDA requirements for the implant (e.g., specific implant parameters and / or characteristics to comply with FDA regulations), or the like. In some embodiments, the treatment planning module 118 can convert the implant surgery information into a format usable for machine learning based models and algorithms. For example, the implant surgery information can be tagged with specific identifiers for mathematical formulas or converted into a numerical representation suitable for feeding into a trained machine learning model. The treatment planning module 118 can also store information about the patient's anatomy, such as a two-dimensional or three-dimensional image or model of the anatomy, and / or information about the biological, geometric, and / or mechanical properties of the anatomy. The anatomical information can be used to inform the design and / or placement of the implant.

[0063] The treatment plan generated by the treatment planning module 118 can be transmitted to the client computing device 102 via the communication network 104 for output to a user (e.g., a clinician, surgeon, healthcare provider, patient). In some embodiments, the client computing device 102 includes or is operably coupled to a display 122 for outputting the treatment plan, surgical plan, corrective plan, etc. For example, a surgical plan can be displayed to illustrate a predicted post-operative outcome. A corrective plan can be an adjustable implant corrective plan displayed to illustrate post-operative implant adjustment to compensate for one or more of disease progression, predicted new disease, anatomical changes, future interventions, etc. The display 122 can display adjustments of one or more implants 123 according to the corrective plan.

[0064] The display 122 may include a graphical user interface (GUI) for visually depicting various aspects of the treatment plan. For example, the display 122 may show various aspects of the surgery to be performed on the patient, such as the surgical approach, treatment levels, corrective manipulations, tissue resection, and / or implant placement. To facilitate visualization, a virtual model of the surgery may be displayed. As another example, the display 122 may display a design of a medical device to be implanted in the patient, such as a two-dimensional or three-dimensional model of the device design. The display 122 may also display patient information, such as a two-dimensional or three-dimensional image or model of the patient's anatomy where the surgery will be performed and / or where the device will be implanted. The client computing device 102 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.

[0065] In some embodiments, the medical device designs generated by the treatment planning module 118 can be transmitted from the client computing device 102 and / or the server 106 to a manufacturing system 124 for manufacturing the corresponding medical device. The manufacturing system 124 can be located on-site or off-site. On-site manufacturing can reduce the number of patient sessions and / or the time to be able to perform a procedure, while off-site manufacturing is useful for manufacturing 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.

[0066] Various types of manufacturing systems are suitable for use in accordance with embodiments herein. For example, the manufacturing system 124 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), additive manufacturing (LOM), powder bed printing (PP), thermoplastic printing, direct material deposition (DMD), inkjet photo-resin printing, or similar techniques, or combinations thereof. Alternatively or in combination, the manufacturing system 124 can be configured for subtractive (conventional) 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 combinations thereof. The manufacturing system 124 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 various manufacturing techniques described herein). Different components of the system 100 can generate at least a portion of the manufacturing data used by the manufacturing system 124. The manufacturing data can include, but is not limited to, manufacturing instructions (e.g., programs executable by additive manufacturing equipment, subtractive manufacturing equipment, etc.), 3D data, CAD data (e.g., CAD files), CAM data (e.g., CAM files), path data (e.g., print head paths, tool paths, etc.), material data, tolerance data, surface finish data (e.g., surface roughness data), regulatory data (e.g., FDA requirements, reimbursement data, etc.), etc. The manufacturing system 124 can analyze the manufacturability of the implant design based on the received manufacturing data. The implant design can be finalized by modifying the shape, surfaces, etc., and generating manufacturing instructions. In some embodiments, the server 106 generates at least a portion of the manufacturing data sent to the manufacturing system 124.

[0067] The manufacturing system 124 can generate CAM data, printing data (e.g., powder bed printing data, thermoplastic printing data, photo-resin data, etc.), and can include additive manufacturing equipment, subtractive manufacturing equipment, heat processing equipment, and the like. The additive manufacturing equipment can be technologies such as 3D printers, stereolithography devices, DLP devices, FDM devices, SLS devices, SLM devices, EBM devices, LOM devices, powder bed printers, thermoplastic printers, DMD devices, or inkjet photo-resin printers. The subtractive manufacturing equipment can be technologies such as CNC machines, EDM machines, grinders, laser cutters, waterjet machines, manual machines (milling machines, lathes, etc.), or similar technologies. Both additive and subtractive technologies can be used to manufacture implants having complex shapes, surface finishes, material properties, and the like to be implanted. The generated manufacturing instructions can be configured to cause the manufacturing system 124 to manufacture patient-specific orthopedic implants that match or are therapeutically identical to the patient-specific design. In some embodiments, patient-specific medical devices can include features, materials, and designs shared between designs to simplify manufacturing. For example, deployable patient-specific medical devices for different patients can have similar internal deployment mechanisms but different deployment configurations. In some embodiments, components of the patient-specific medical device are selected from a set of available prefabricated components, and the selected prefabricated components can be modified based on manufacturing instructions or data.

[0068] The treatment plans described herein can be performed by a surgeon, a surgical robot, or a combination thereof, thus allowing for treatment flexibility. In some embodiments, the surgical procedure can be performed entirely by the surgeon, entirely by the surgical robot, or a combination thereof. For example, one stage of the surgical procedure can be performed manually by the surgeon and another stage of the procedure can be performed by the surgical robot. In some embodiments, the treatment planning module 118 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 can be transmitted by the client computing device 102 and / or the server 106 to the robotic device.

[0069] After treating the patient according to the treatment plan, the treatment progress can be monitored over one or more time periods to update the data analysis module 116 and / or the treatment plan module 118. The post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models to develop patient-specific treatment plans, patient-specific medical devices, or a combination thereof.

[0070] It will be appreciated that the components of the system 100 can be configured in many different ways. For example, in an alternative embodiment, the database 110, the data analysis module 116, and / or the treatment planning module 118 can be components of the client computing device 102 rather than the server 106. As another example, the database 110, the data analysis module 116, and / or the treatment planning module 118 can be located across multiple different servers, computer systems, or other types of cloud computing resources rather than on a single server 106 or client computing device 102.

[0071] Additionally, in some embodiments, system 100 is operational 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 appliances, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0072] FIG. 2 illustrates a computing device 200 suitable for use in connection with the system 100 of FIG. 1, according to one embodiment. The computing device 200 may be incorporated into various components of the system 100 of FIG. 1, such as the client computing device 102 or the server 106. The computing device 200 includes one or more processors 210 (e.g., CPU, GPU, HPU, etc.). The processor 210 may be a single processing unit or multiple processing units within a device, or may be distributed across multiple devices. The processor 210 may be coupled to other hardware devices using a bus, such as, for example, a PCI bus or a SCSI bus. The processor 210 may be configured to execute one or more computer-readable program instructions, such as program instructions for performing any of the methods described herein.

[0073] Computing device 200 may include one or more input devices 220 that provide input to processor 210, for example, to communicate actions from a user of device 200. The actions may be mediated by a hardware controller that interprets signals received from the input devices and communicates the information to processor 210 using a communication protocol. Input devices 220 may include, for example, a mouse, a keyboard, a touch screen, an infrared sensor, a touch pad, a wearable input device, a camera or image-based input device, a microphone, or other user input device.

[0074] The computing device 200 may include a display 230 that is used to display various types of output, such as text, models, virtual surgery, surgical plans, implants, graphics, and / or images (e.g., images with voxels showing radiometric or Hounsfield units representing tissue density at a location). In some embodiments, the display 230 provides graphical and textual visual feedback to the user. The processor 210 may communicate with the display 230 through a hardware controller for the device. In some embodiments, the display 230 includes the input device 220 as part of the display 230, such as when the input device 220 includes a touch screen or includes a gaze direction monitoring system. In alternative embodiments, the display 230 is separate from the input device 220. Examples of display devices include an LCD display screen, an LED display screen, a protrusion, a holographic display, or an augmented reality display (e.g., a head-up display device or a head-mounted device), etc.

[0075] Optionally, other I / O devices 240 may also be coupled to processor 210, such as a network card, a video card, an audio card, a USB, Firewire or other external device, a camera, a printer, speakers, a CD-ROM drive, a DVD drive, a disk drive, or a Blu-ray device. Other I / O devices 240 may also include input ports for information from directly connected medical equipment, such as imaging machines, including MRI machines, X-ray machines, CT machines, etc. Other I / O devices 240 may further include input ports for receiving data from these types of machines over a network or from other sources, such as previously captured data stored in a database.

[0076] In some embodiments, computing device 200 also includes a communications device (not shown) capable of wireless or wired-based communications with network nodes. The communications device can communicate with other devices or servers over the network, for example using TCP / IP protocols. Computing device 200 can utilize the communications device to distribute operations across multiple network devices, including imaging devices, manufacturing devices, and the like.

[0077] The computing device 200 may include memory 250, which may be in a single device or distributed across multiple devices. The memory 250 may include one or more of various hardware devices for volatile and non-volatile storage, and may include both read-only and writeable memory. For example, the memory may comprise random access memory (RAM), various caches, CPU registers, read-only memory (ROM), and writeable non-volatile memory, such as flash memory, hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives, device buffers, and the like. Memory is not a propagating signal separate from the underlying hardware, and thus memory is non-transitory. In some embodiments, the memory 250 may be, for example, a program, software, data, or a non-transitory computer-readable storage medium that stores the same. In some embodiments, the memory 250 may include a program memory 260 that stores programs and software, such as an operating system 262, one or more therapeutic support modules 264, and other application programs 266. The treatment assistance module 264 may include one or more modules configured to perform various methods described herein (e.g., the data analysis module 116 and / or the treatment planning module 118 described with respect to FIG. 1 ). The memory 250 may also include a data memory 270 that may include, for example, reference data, configuration data, data sets, user options or preferences, and the like, which may be provided to the program memory 260 or any other element of the computing device 200.

[0078] FIG. 3 is a flow diagram illustrating a method 300 for providing patient-specific medical care, according to one embodiment. The method 300 can include a data step 310, a modeling step 320, and an execution step 330. The data step 310 can include collecting data (e.g., pathology data) of a patient to be treated, and comparing the patient data to reference data (e.g., previous patient data, such as pathology data, surgery data, and / or outcome data). For example, a step of receiving a patient data set (block 312) can be included. The patient data set can be compared to a plurality of reference patient data sets (block 314), for example, to identify one or more similar patient data sets in the reference patient data set. Each of the reference patient data sets can include data representing one or more of age, sex, BMI, LL, Cobb angle, PI, disc height, segment flexibility, bone quality, rotational displacement, or treatment level of the spine.

[0079] The subset of reference patient datasets may be selected (block 316), for example, based on similarity with the patient dataset and / or treatment outcomes of the corresponding reference patients. For example, a similarity score may be generated for each reference patient dataset based on the comparison of the patient dataset and the reference patient dataset. The similarity score may represent a statistical correlation between the patient data and the reference patient dataset. One or more similar patient datasets may be identified based at least in part on the similarity score.

[0080] In some embodiments, each patient data set of the selected subset includes and / or is associated with data indicative of a favorable treatment outcome (e.g., a favorable treatment outcome based on a single target outcome, an aggregate outcome score, an outcome threshold). The data may include, for example, data representative of one or more of corrected anatomical metrics, presence of fusion, health-related quality of life, activity level, or comorbidities. In some embodiments, the data is or includes an outcome score that can be calculated based on a single target outcome, an aggregate outcome, and / or an outcome threshold.

[0081] Optionally, the data analysis stage 310 can include identifying or determining, for at least one patient data set of the selected subset (e.g., for at least one similar patient data set), surgical data and / or medical device design data associated with a favorable treatment outcome. The surgical data can include data representative of one or more of a surgical approach, a corrective operation, a bone resection, or a placement of an implant to be implanted. The at least one medical device design can include data representative of one or more of physical properties, mechanical properties, or biological properties of a corresponding medical device. In some embodiments, the at least one patient-specific medical device design includes a design of an implant or an implant delivery instrument.

[0082] During the modeling stage 320, surgical procedures and / or medical device designs are generated (block 322). The generating step may include building at least one predictive model based on a selected subset of the patient dataset and / or the reference patient dataset (e.g., using statistics, machine learning, neural networks, AI, etc.). The predictive model may be configured to generate a surgical procedure and / or a medical device design.

[0083] In some embodiments, the predictive model includes one or more trained machine learning models that generate, at least in part, surgical procedures and / or medical device designs. For example, the trained machine learning models can determine a plurality of candidate surgical procedures and / or medical device designs for treating a patient. Each surgical procedure can be associated with a corresponding medical device design. In some embodiments, the surgical procedures and / or medical device designs are determined based on surgical procedure data and / or medical device design data associated with successful outcomes, as described above with respect to the data analysis stage 310. For each surgical procedure and / or corresponding medical device design, the trained machine learning model can calculate a probability of achieving a target outcome (e.g., a favorable or desired outcome) for the patient. The trained machine learning model can then select at least one surgical procedure and / or corresponding medical device design based, at least in part, on the calculated probability.

[0084] The execution stage 330 can include manufacturing the medical device design (block 332). In some embodiments, the medical device design is manufactured by a manufacturing system configured to perform one or more of additive manufacturing, 3D printing, stereolithography, DLP, FDM, SLS, SLM, EBM, LOM, PP, thermoplastic printing, DMD, or inkjet photo-resin printing. The execution stage 330 can optionally include generating manufacturing instructions configured to cause the manufacturing system to manufacture a medical device having the medical device design.

[0085] The executing stage 330 can include performing the surgical procedure (block 334). The surgical procedure can include implanting a medical device having the medical device design into a patient. The surgical procedure can be performed manually, by a surgical robot, or a combination thereof. In embodiments in which the surgical procedure is performed by a surgical robot, the executing stage 330 can include generating control instructions configured to cause the surgical robot to, at least in part, perform the patient-specific surgical procedure.

[0086] Method 300 can be implemented and performed in a variety of ways. In some embodiments, one or more steps of method 300 (e.g., data step 310 and / or modeling step 320) can be implemented as computer readable instructions stored in a memory and executable by one or more processors of any of the computing devices and systems described herein (e.g., system 100) (e.g., client computing device 102 and / or server 106). Alternatively, one or more steps of method 300 (e.g., execution step 330) can be performed by a healthcare provider (e.g., physician, surgeon), a robotic device (e.g., surgical robot), a manufacturing system (e.g., manufacturing system 124), or a combination thereof. In some embodiments, one or more steps of method 300 are omitted (e.g., execution step 330).

[0087] 4A-4C illustrate exemplary data sets that may be used and / or generated in connection with the methods described herein (e.g., data analysis stage 310 described with respect to FIG. 3) according to one embodiment. FIG. 4A illustrates a patient data set 400 for a patient to be treated. The patient data set 400 may include a patient ID and a number of pre-operative patient metrics (e.g., age, sex, BMI, LL, PI, and spinal treatment level). FIG. 4B illustrates a number of reference patient data sets 410. In the illustrated embodiment, the reference patient data set 410 includes a first subset 412 from a study group (Study Group X), a second subset 414 from a practice database (Practice Y), and a third subset 416 from an academic group (University Z). In alternative embodiments, the reference patient data set 410 may include data from other sources, as described herein above. Each reference patient dataset can include a patient ID, multiple preoperative patient metrics (e.g., age, gender, BMI, LL, PI, and spinal treatment level), treatment outcome data (Outcome) (e.g., fused or not, HRQL, complications), and treatment surgical data (Surg.Intervention) (e.g., implant design, implant placement, surgical approach).

[0088] FIG. 4C illustrates a comparison of the patient dataset 400 with the reference patient dataset 410. As described above, the patient dataset 400 can be compared to the reference patient dataset 410 to identify one or more similar patient datasets from the reference patient dataset. In some embodiments, the patient metrics from the reference patient dataset 410 are converted to numerical values, and the patient metrics from the patient dataset 400 are compared to calculate a similarity score 420 ("pre-op similarity") for each reference patient dataset. Reference patient datasets with similarity scores below a threshold can be considered similar to the patient dataset 400. For example, in the illustrated embodiment, the reference patient dataset 410a has a similarity score of 9, the reference patient dataset 410b has a similarity score of 2, the reference patient dataset 410c has a similarity score of 5, and the reference patient dataset 410d has a similarity score of 8. Due to each of these scores being below the threshold of 10, the reference patient datasets 410a-d are identified as similar patient datasets.

[0089] The treatment outcome data of the similar patient data sets 410a-d can be analyzed to determine the surgical procedure and / or implant design with the highest probability of success. For example, the treatment outcome data of each reference patient data set can be converted into a numerical outcome score 430 ("Outcome Index") that represents the likelihood of a good outcome. In the illustrated embodiment, the reference patient data set 410a has an outcome score of 1, the reference patient data set 410b has an outcome score of 1, the reference patient data set 410c has an outcome score of 9, and the reference patient data set 410d has an outcome score of 2. In an embodiment where a lower outcome score correlates with a higher likelihood of a good outcome, the reference patient data sets 410a, 410b, and 410d can be selected. The treatment procedure data from the selected reference patient data sets 410a, 410b, and 410d can then be used to determine at least one surgical procedure (e.g., implant placement, surgical approach) and / or implant design that is likely to provide a favorable outcome for the treated patient.

[0090] In some embodiments, a method of providing medical care to a patient is provided. The method can include comparing a patient dataset with reference data. The patient dataset and the reference data can include any of the data types described herein. The method can include identifying and / or selecting relevant reference data (e.g., data related to the treatment of the patient, such as data of similar patients and / or data of similar treatment procedures) using any of the techniques described herein. A treatment plan can be generated based on the selected data using any of the techniques described herein. The treatment plan can include one or more treatment procedures (e.g., surgical procedures, instructions for the procedures, models or other virtual representations of the procedures), one or more medical devices (e.g., devices to be implanted, instruments for delivering the devices, surgery kits), or combinations thereof.

[0091] In some embodiments, a system for generating a medical treatment plan is provided. The system can compare a patient dataset to a plurality of reference patient datasets using any of the techniques described herein. A subset of the reference patient datasets can be selected, for example, based on similarity and / or treatment outcome, or any other technique as described herein. A medical treatment plan can be generated based at least in part on the selected subset using any of the techniques described herein. The medical treatment plan can include one or more therapeutic procedures, one or more medical devices, or any of the other aspects of the treatment plan described herein, or a combination thereof.

[0092] In further embodiments, the system is configured to use past patient data. The system can select past patient data to develop or select a treatment plan, design a medical device, etc. The past data can be selected based on one or more similarities between the current patient and previous patients to develop a prescriptive treatment plan designed for a desired outcome. The prescriptive treatment plan can be tailored to the current patient to increase the likelihood of achieving the desired outcome. In some embodiments, the system can analyze and / or select a subset of the past data to generate one or more therapeutic procedures, one or more medical devices, or a combination thereof. In some embodiments, the system can use a subset of data from one or more previous patient groups with good outcomes to generate a baseline historical data set used, for example, to design, develop, or select a treatment plan, a medical device, or a combination thereof.

[0093] 5 is a flow diagram illustrating a method 500 for providing patient-specific medical care according to another embodiment of the present technology. The method 500 may begin at step 502 with receiving a patient dataset for a particular patient in need of medical care. The patient dataset may include data representative of the patient's condition, anatomical structure, pathological structure, symptoms, medical history, preferences, and / or other information or parameters related to the patient. For example, the patient dataset 808 may include surgical intervention data, treatment outcome data, progress data (e.g., surgeon's notes), patient feedback (e.g., feedback obtained using quality of life questionnaires, surveys), clinical data, patient information (e.g., demographics, gender, age, height, weight, type of medical condition, occupation, activity level, organizational 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 the like. The patient dataset may also include image data such as camera images, MRI images, ultrasound images, CAT scan images, PET images, x-ray images, etc. In some embodiments, the patient dataset includes data representative of one or more of the following: patient identification number (ID), age, sex, BMI, LL, Cobb angle, PI, disc height, segment flexibility, bone quality, rotational displacement, and / or treatment level of the spine. The patient dataset may be received at a server, computing device, or other computing system. For example, in some embodiments, the patient dataset may be received at the server 106 shown in FIG. 1 or at the computing system 606 described below with respect to FIG. 6. In some embodiments, the computing system that receives the patient dataset in step 502 also stores one or more software modules (e.g., the data analysis module 116 and / or the treatment planning module 118 shown in FIG. 1, or additional software modules for performing various operator operations of method 500). Additional details for collecting and receiving the patient dataset are described below with respect to FIGS. 6-7D.

[0094] In some embodiments, the received patient data set may include disease metrics such as LL, Cobb angle, coronal parameters (e.g., coronal balance, global coronal balance, coronal pelvic tilt, etc.), sagittal parameters (e.g., PI, sacral tilt, thoracic lordosis, etc.), and / or pelvic parameters. The disease metrics may include micro-measurements (e.g., metrics associated with specific or individual segments of the patient's spine) and / or macro-measurements (e.g., metrics associated with multiple segments of the patient's spine). In some embodiments, the disease metrics are not included in the patient data set and the method 500 includes determining (e.g., automatically determining) one or more disease metrics based on the patient image data, as described below.

[0095] Once the patient dataset is received in step 502, the method 500 may proceed to create a virtual model of the patient's native anatomy (also referred to as "pre-operative anatomy") in step 503. The virtual model may be based on image data included in the patient dataset received in step 502. For example, the same computer system that received the patient dataset in step 502 may analyze the image data in the patient dataset to generate a virtual model of the patient's native anatomy. The virtual model may be a two-dimensional or three-dimensional visual representation of the patient's native anatomy. The virtual model may include one or more regions of interest and may include some or all of the patient's anatomy in the regions of interest (e.g., any combination of tissue types including, but not limited to, bony structures, cartilage, soft tissue, vascular tissue, neural tissue, etc.). As a non-limiting example, the virtual model may include a visual representation of the patient's spinal region, including some or all of the sacrum, lumbar region, thoracic region, and / or cervical region. In some embodiments, the virtual model includes soft tissue, cartilage, and other non-bony structures. In other embodiments, the virtual model includes only the patient's bone structure. Examples of virtual models of native anatomy are described below with respect to Figures 8A and 8B. In some embodiments, method 500 can optionally omit creating a virtual model of the patient's native anatomy in step 503 and proceed directly from step 502 to step 504.

[0096] In some embodiments, the computer system that generated the virtual model in step 502 may also determine (e.g., automatically determine or measure) one or more disease metrics of the patient based on the virtual model. For example, the computer system may analyze the virtual model to determine the patient's pre-operative LL, Cobb angle, coronal parameters (e.g., coronal balance, global coronal balance, coronal pelvic tilt, etc.), sagittal parameters (e.g., PI, sacral slope, thoracic lordosis, etc.), and / or pelvic parameters. The disease metrics may include micro-measurements (e.g., metrics associated with a particular or individual segment of the patient's spine) and / or macro-measurements (e.g., metrics associated with multiple segments of the patient's spine).

[0097] The method 500 may proceed at step 504 to create a virtual model of the patient's corrected anatomical configuration (which may also be referred to herein as a "planned configuration," "optimized shape," "post-operative anatomical configuration," or "target outcome"). For example, the computer system may use the analytical procedure described above to determine a "corrected" or "optimized" anatomical configuration for a particular patient that represents an ideal surgical outcome for the particular patient. This may be done, for example, by analyzing multiple reference patient data sets to identify post-operative anatomical configurations of similar patients who had good post-operative outcomes, as described in detail above with respect to FIGS. 1-4C (e.g., based on similarity between the reference patient data sets and the patient data sets and / or whether the reference patients had good treatment outcomes). This may also include applying one or more mathematical rules that define an optimal anatomical outcome (e.g., positional relationships between anatomical elements) and / or target (e.g., acceptable) post-operative metrics / design criteria (e.g., adjust anatomy such that post-operative sagittal longitudinal axis is less than 7 mm, post-operative Cobb angle is less than 10 degrees, etc.). The target post-operative metrics may include, but are not limited to, a target coronal parameter, a target sagittal parameter, a target PI angle, a target Cobb angle, a target shoulder slope angle, a target iliopsoas angle, a target coronal balance, a target Cobb angle, a target lordosis angle, and / or a target intervertebral space height. The difference between the native anatomical alignment and the corrected anatomical alignment is sometimes referred to as a "patient-specific correction" or a "target correction."

[0098] Once the corrected anatomical arrangement is determined, the computer system can generate a two-dimensional or three-dimensional visual representation of the patient's anatomy with the corrected anatomical arrangement. Similar to the virtual model created in step 503, the virtual model of the patient's corrected anatomical configuration can include one or more regions of interest and can include some or all of the patient's anatomical configuration within the regions of interest (e.g., any combination of tissue types including, but not limited to, bony structures, cartilage, soft tissue, vascular tissue, neural tissue, etc.). As a non-limiting example, the virtual model can include a visual representation of the patient's spinal region in the corrected anatomical configuration, including some or all of the sacrum, lumbar region, thoracic region, and / or cervical region. In some embodiments, the virtual model includes soft tissue, cartilage, and other non-bony structures. In other embodiments, the virtual model includes only the patient's bony structures. An example of a virtual model of a native anatomical configuration is described below with respect to FIGS. 9A-1-9B-2.

[0099] The method 500 may proceed at step 506 to generate (e.g., automatically generate) a surgical plan to achieve the corrected anatomical configuration indicated by the virtual model. The surgical plan may include pre-operative plans, surgical plans, post-operative plans, and / or specific spinal metrics associated with optimal surgical outcomes. For example, the surgical plan may include a specific surgical procedure to achieve the corrected anatomical configuration. In the context of spinal surgery, the surgical plan may include a specific fusion procedure (e.g., PLIF, ALIF, TLIF, LLIF, DLIF, XLIF, etc.) across a specific range of vertebral levels (e.g., L1-L4, L1-L5, L3-T12, etc.). Of course, other surgical procedures, such as non-fusion surgical approaches and orthopedic procedures for other regions of the patient, may be specified to achieve the corrected anatomical configuration. The surgical plan may also include one or more expected spinal metrics (e.g., LL, Cobb angle, coronal parameters, sagittal parameters, and / or pelvic parameters) corresponding to the expected post-operative patient anatomy. The surgical plan may be generated by the same or a different computer system than the computer system that created the virtual model of the corrected anatomical configuration. In some embodiments, the surgical plan may also be based on one or more reference patient data sets, as described above with respect to FIGS. 1-4C. In some embodiments, the surgical plan may also be based at least in part on surgeon-specific preferences and / or outcomes associated with the particular surgeon performing the procedure. In some embodiments, multiple surgical plans are generated in step 506 to provide the surgeon with multiple options. An example of a surgical plan is described below with respect to FIG. 10.

[0100] After the virtual model of the corrected anatomical configuration is created in step 504 and the surgical plan is generated in step 506, the method 500 can proceed to transmitting the virtual model of the corrected anatomical configuration and the surgical plan for review by the surgeon in step 508. In some embodiments, the virtual model and the surgical plan are transmitted as a surgical plan report, examples of which are described with respect to FIGS. 11A-11B. In some embodiments, the same computer system used in steps 502-506 can transmit the virtual model and the surgical plan to a computing device (e.g., the client computing device 102 described in FIG. 1 or the computing device 602 described below with respect to FIG. 6) for review by the surgeon. This can include transmitting the virtual model and the surgical plan directly to the computing device or uploading the virtual model and the surgical plan to a cloud or other storage system and then downloading. Although step 508 describes transmitting the surgical plan and the virtual model to the surgeon, one of ordinary skill in the art will understand from the disclosure herein that an image of the virtual model may be included in the surgical plan transmitted to the surgeon and that the actual model may not be included (e.g., to reduce the file size transmitted). Further, the information transmitted to the surgeon in step 508 may include a virtual model of the patient's native anatomy (or an image thereof) in addition to the virtual model of the corrected anatomy. In embodiments in which more than one surgical plan is generated in step 506, method 500 may include transmitting one or more of the surgical plans to the surgeon for review and selection.

[0101] The surgeon may review the virtual model and the surgical plan and, at step 510, either approve or reject the surgical plan (or, if one or more surgical plans were provided at step 508, select one of the provided surgical plans). If the surgeon does not approve the surgical plan at step 510, the surgeon may optionally provide feedback and / or suggested revisions to the surgical plan (e.g., by adjusting the virtual model or by changing one or more aspects of the plan). Thus, method 500 may include receiving (e.g., via a computer system) the surgeon's feedback and / or suggested revisions. If the surgeon's feedback and / or suggested revisions are received at step 512, method 500 may proceed at step 514 to modify (e.g., automatically via a computer system) the virtual model and / or the surgical plan based at least in part on the surgeon's feedback and / or suggested revisions received at step 512. In some embodiments, if the surgeon rejects the surgical plan, the surgeon does not provide feedback and / or suggested revisions. In such embodiments, step 512 may be omitted and method 500 may proceed to modifying (e.g., automatically via a computer system) the virtual model and / or surgical plan by selecting new and / or additional reference patient data sets at step 514. The modified virtual model and / or surgical plan may then be sent to the surgeon for review. Steps 508, 510, 512, and 514 may be repeated as many times as necessary until the surgeon approves the surgical plan. Although described as the surgeon reviewing, modifying, approving, and / or rejecting the surgical plan, in some embodiments the surgeon may also review, modify, approve, and / or reject the corrected anatomical configuration shown via the virtual model.

[0102] Once the surgeon's approval of the surgical plan is received at step 510, the method 500 can proceed to determine, at step 513, whether a suitable adjustable implant orthodontic plan ("orthodontic plan") can be generated based on the approved surgical plan to provide one or more post-operative implant adjustments. For example, the computer system can perform one or more simulations in which one or more implants make post-operative adjustments designed to achieve a simulated outcome (e.g., outcome of the surgical plan, orthodontic plan, etc.). If the simulated outcome meets suitability criteria (e.g., user-entered criteria, threshold metrics from the surgical plan, etc.), the computer system can determine that the orthodontic plan is suitable for the surgical plan. In some embodiments, the computer system can request a revision of the surgical plan to generate a orthodontic plan for the revised surgical plan. This provides flexibility to align the surgical plan with the adjustable implant orthodontic plan.

[0103] If the computer system determines that a suitable correction plan can be generated, the method 500 can proceed to step 515. The computer system can, for example, analyze one or more surgical plans, adjustable implant designs, selectable sensor readings, and / or design parameters to generate a correction plan that meets one or more outcome criteria. In some embodiments, a reference data set can be used to determine a patient-specific post-operative correction plan. For example, step 513 can include one or more acts of step 316 of FIG. 3 to select a subset of the reference data set having implant data to be implanted based on a similarity criterion. This allows the computer system to use reference data that meets the criteria to design the implant to be implanted (e.g., non-adjustable implant, non-adjustable implant, etc.). In some embodiments, the physician can input design parameters, including, but not limited to, disc height, anterior disc height, posterior disc height, lordosis angle, and ranges or values ​​of other parameters disclosed herein. Exemplary design parameters for adjustable implants and physician approval are discussed in connection with FIG. 16B and can be used to generate a patient-specific post-operative correction plan in the form of a correction plan (e.g., an implant-specific correction plan, a multi-implant correction plan, etc.) used to perform single-level or multi-level post-operative spinal adjustments.

[0104] In some embodiments, the computer system can generate a correction plan based on design parameters entered by the physician, for example, by selecting the levels for treatment, the number of implants to be implanted (e.g., per level, per spinal segment, etc.), and the design of each implant. The correction plan can include, for example, post-operative adjustments that can be incorporated into post-operative therapy or other plans disclosed herein. In some embodiments, the computer system can determine the type of adjustable implant (e.g., expandable disc, rod, expandable cage, etc.), range of motion of individual implants, sensing capabilities of implants, etc., based on computer-generated simulations, for example, using a three-dimensional model of the patient's anatomy, etc. The physician can approve or modify the correction plan.

[0105] Method 500 may proceed to step 517, where a patient-specific implant may be designed (e.g., via the same computer system that performed one or more of steps 502-514) based on the surgical plan and the correction plan. The patient-specific implant may have a configuration after implantation to achieve a target correction, such as a correction in the surgical plan. Adjustment capabilities of the patient-specific implant may then be designed based on the correction plan, which may include, but is not limited to, one or more implant configurations (e.g., undeployed or folded configuration, partially deployed or deployed configuration, fully deployed or fully deployed configuration), range of motion, relationships between implants (e.g., configuration relationships, loading relationships, etc.), sensing capabilities, etc. In some embodiments, the computer system may simulate natural anatomical changes, e.g., associated with disease progression, aging, target spinal configurations, or other anatomical changes to design an implant that can achieve results through post-operative in vivo adjustments. In some embodiments, the computer system may simulate multiple scenarios of anatomical changes. The scenarios may be ranked to prioritize and / or select design parameters. The system can, for example, request physician input to select a ranking. In some embodiments, the computer system can score and rank the scenarios. The highest ranked scenario can be selected to design an implant that meets a confidence score threshold.

[0106] The method 500 may proceed to manufacturing the patient-specific implant at step 518. The implant may be manufactured using additive manufacturing techniques such as 3D printing, stereolithography, DLP, FDM, SLS, SLM, EBM, LOM, PP, thermoplastic printing, DMD, or inkjet photoresin printing, or similar techniques, or combinations thereof. Alternatively or additionally, the implant may be manufactured using subtractive manufacturing techniques such as CNC machining, EDM, grinding, laser cutting, waterjet machining, manual machining (e.g., milling, lathe / turning), or combinations thereof. The implant may be manufactured by any suitable manufacturing system (e.g., manufacturing system 124 shown in FIG. 1 or manufacturing system 630 described below with respect to FIG. 6). In some embodiments, the implant is manufactured by a manufacturing system executing computer readable manufacturing instructions generated by a computer system at step 516.

[0107] Once the implant is manufactured at step 518, the method 500 may proceed to implanting the patient-specific implant into the patient at step 520. The surgery may be performed manually, by a robotic surgical platform (e.g., a surgical robot), or by a combination thereof. In embodiments in which the surgery is performed at least in part by a robotic surgical platform, the surgical plan may include computer readable control instructions configured to cause the surgical robot to at least in part perform the patient-specific surgical procedure. Additional details regarding the robotic surgical platform are described below with respect to FIG. 6.

[0108] If in step 512, an appropriate correction plan is not identified for the approved surgical plan, the method 500 may proceed to step 516 to design a patient-specific implant based on the corrected anatomical configuration and the surgical plan (e.g., via the same computer system that performed steps 502-514). For example, the patient-specific implant may be specially designed to guide the patient's anatomy into the corrected anatomy (e.g., transform the patient's anatomy from the native anatomy to the corrected anatomy) when implanted in a particular patient. The patient-specific implant may be designed such that, once implanted, the patient's anatomy will occupy the corrected anatomy for the expected useful life of the implant (e.g., 5+ years, 10+ years, 20+ years, 50+ years, etc.). In some embodiments, the patient-specific implant is designed based solely on a virtual model of the corrected anatomical configuration and / or without reference to preoperative patient images.

[0109] The patient-specific implant can be any of the implants described herein or in the patent documents incorporated herein by reference. For example, the patient-specific implant can include a screw (e.g., bone screw, spinal screw, pedicle screw, facet screw), interbody implant device (e.g., intervertebral implant), cage, plate, rod, disc, fusion device, spacer, rod, expandable device, stent, bracket, tie, scaffold, fixation device, anchor, nut, bolt, rivet, connector, tether, fastener, joint replacement (e.g., artificial disc), hip implant, or the like. The design of the patient-specific implant can include data describing 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 implant. For example, the orthopedic implant design may include the implant's shape, size, material, and / or effective stiffness (e.g., lattice density, number of struts, location of struts, etc.) An example of a patient-specific implant designed via method 500 is described below with respect to Figures 12A and 12B.

[0110] In some embodiments, designing the implant in step 516 can optionally include generating manufacturing instructions for manufacturing the implant. For example, a computer system can generate computer-executable manufacturing instructions that, when executed by a manufacturing system, cause the manufacturing system to manufacture the implant.

[0111] In some embodiments, the patient-specific implant is designed in step 516 only after the surgeon has reviewed and approved the virtual model with the corrected anatomical configuration and surgical plan. Thus, in some embodiments, the implant design is not sent to the surgeon along with the surgical plan in step 508 and is not manufactured prior to the surgeon's approval of the surgical plan. Without being bound by theory, waiting to design the patient-specific implant until the surgeon has approved the surgical plan may increase the efficiency of method 500 and / or reduce the resources required to perform method 500.

[0112] Method 500 may then proceed to steps 518 and 520, as described above. Method 500 may be implemented and performed in a variety of ways. In some embodiments, steps 502-516 may be performed by a computer system associated with a first entity (e.g., computer system 606, described below with respect to FIG. 6), step 518 may be performed by a manufacturing system associated with a second entity, and step 520 may be performed by a surgical provider, surgeon, and / or robotic surgical platform associated with a third entity. Any of the above steps may also be implemented as computer readable instructions stored in memory and executable by one or more processors of the associated computer systems.

[0113] FIG. 6 is a schematic diagram of a surgical setup including selected systems and devices that can be used to provide patient-specific medical care, such as for performing the method 500 described with respect to FIG. 5. As shown, the surgical setup includes a computing device 602, a computing system 606, a cloud 608, a manufacturing system 630, and a robotic surgery platform 650. The computing device 602 can be a user device, such as a smartphone, a mobile device, a laptop, a desktop, a personal computer, a tablet, a phablet, or other such devices known in the art. In operation, a user (e.g., a surgeon) can use the computing device 602 to collect, search, review, modify, or otherwise interact with a patient dataset. The computing system 606 can include any suitable computing system configured to store one or more software modules for identifying a reference patient dataset, determining a patient-specific surgical plan, generating a virtual model of the patient anatomy, designing a patient-specific implant plant, and the like. The one or more software modules can include algorithms, machine learning models, AI architectures, and the like for performing selected operations. Cloud 608 may be any suitable network and / or storage system and may include any combination of hardware and / or virtual computing resources. Manufacturing system 630 may be any suitable manufacturing system for manufacturing patient-specific implants, including any of those previously described herein. Robotic surgical platform 650 (referred to herein as "platform 650") may be configured to perform or otherwise assist in one or more aspects of a surgical procedure.

[0114] In an exemplary operation, the computing device 602, the computing system 606, the cloud 608, the manufacturing system 630, and the platform 650 can be used to provide patient-specific medical care, such as to perform the method 500 described with respect to FIG. 5. For example, the computing system 606 can receive a patient dataset from the computing device 602 (e.g., step 502 of the method 500). In some embodiments, the computing device 602 can transmit the patient dataset directly to the computing system 606. In other embodiments, the computing device 602 can upload the patient dataset to the cloud 608, and the computing system 606 can download or otherwise access the patient dataset from the cloud. Once the computer system 606 receives the patient data set, the computer system 606 can create a virtual model of the patient's native anatomical configuration (e.g., step 503 of method 500), create a virtual model of the corrected anatomical configuration (e.g., step 504 of method 500), and / or generate a surgical plan to achieve the corrected anatomical configuration (e.g., step 506 of method 500). The computer system can perform the above-mentioned operations via one or more software modules, which in some embodiments include machine learning models or other AI architectures. Once the virtual model and surgical plan are created, the computer system 606 can send the virtual model and surgical plan to a surgeon for review (e.g., step 508 of method 500). This can include, for example, sending the virtual model and surgical plan directly to the computing device 602 for the surgeon's review. In other embodiments, this can include uploading the virtual model and surgical plan to the cloud 608. The surgeon can then use the computing device 602 to download or otherwise access the virtual model and surgical plan from the cloud 608.

[0115] The surgeon can use the computing device 602 to review the virtual model and the surgical plan. The surgeon can also use the computing device 602 to approve or reject the surgical plan and provide feedback regarding the surgical plan. The surgeon's approval, rejection, or feedback regarding the surgical plan can be transmitted to and received by the computing system 606 (e.g., steps 510 and 512 of method 500). The computing system 606 can then modify the virtual model and / or the surgical plan (e.g., step 514 of method 500). The computing system 606 can transmit the modified virtual model and surgical plan to the surgeon for review (e.g., by uploading to the cloud 608 or by transmitting directly to the computing device 602).

[0116] The computer system 606 may also use one or more software modules to design a patient-specific implant based on the corrected anatomical configuration and surgical plan (e.g., step 516 of method 500). In some embodiments, the software modules rely on one or more algorithms, machine learning models, or other AI architectures to design the implant. Once the computer system 606 designs the patient-specific implant, the computer system 606 may upload the design and / or manufacturing instructions to the cloud 608. The computer system 606 may also create manufacturing instructions (e.g., computer readable manufacturing instructions) for manufacturing the patient-specific implant. In such an embodiment, the computer system 606 may upload the manufacturing instructions to the cloud 608.

[0117] The manufacturing system 630 can download or otherwise access the design and / or fabrication instructions for the patient-specific implant from the cloud 608. The manufacturing system can then manufacture the patient-specific implant (e.g., step 518 of method 500) using additive, subtractive, or other suitable manufacturing techniques.

[0118] The robotic surgical platform 650 can perform or assist in one or more aspects of a surgical procedure (e.g., step 520 of method 500). For example, the platform 650 can prepare tissue for incision, make incisions, make resections, remove tissue, manipulate tissue, perform corrective manipulations, deliver an implant to a target site, deploy an implant at a target site, adjust an implant at a target site, manipulate an implant after implantation, secure an implant at a target site, remove an implant, suture tissue, etc. Accordingly, the platform 650 can include one or more arms 655 and end effectors for holding various surgical tools (e.g., graspers, clips, needles, needle drivers, irrigation tools, suction tools, staplers, screwdriver assemblies, etc.), imaging instruments (e.g., cameras, sensors, etc.), and / or medical devices (e.g., implant 600), thereby enabling the platform 650 to perform one or more aspects of a surgical plan. Although shown as having one arm 655, one skilled in the art will appreciate that the platform 650 can have multiple arms (e.g., two, three, four, or more arms) and any number of joints, linkages, motors, and degrees of freedom. In some embodiments, the platform 650 has a first arm dedicated to holding one or more imaging instruments, while the remaining arms can hold various surgical instruments. In some embodiments, the surgical instruments can be releasably secured to the arms so that they can be selectively replaced before, during, or after a surgical procedure. The arms can be movable through various ranges of motion (e.g., degrees of freedom) to provide appropriate dexterity for performing various aspects of a surgical procedure.

[0119] The platform 650 can include a control module 660 for controlling the movement of the arm 655. In some embodiments, the control module 660 includes a user input device (not shown) for controlling the movement of the arm 655. The user input device can be a joystick, a mouse, a keyboard, a touch screen, an infrared sensor, a touch pad, a wearable input device, a camera or image-based input device, a microphone, or other user input device. A user (e.g., a surgeon) can interact with the user input device to control the movement of the arm 655.

[0120] In some embodiments, the control module 660 includes one or more operators for executing machine-readable surgical instructions that, when executed, automatically control the movement of the arm 655 to perform one or more aspects of a surgical procedure. In some embodiments, the control module 660 can receive (e.g., from the cloud 608) machine-readable surgical instructions that, when executed by the control module 660, cause the platform 650 to perform one or more steps of the surgical procedure. For example, the machine-readable surgical instructions can instruct the platform 650 to prepare tissue for incision, make an incision, make a resection, remove tissue, manipulate tissue, perform a corrective operation, deliver the implant 600 to a target site, deploy the implant 600 at the target site, adjust a configuration of the implant 600 at the target site, manipulate the implant 600 once implanted, secure the implant 600 at the target site, remove the implant 600, suture tissue, etc. Thus, the surgical instructions may include specific instructions for articulating the arm 655 to perform or otherwise assist in the implantation of a patient-specific implant.

[0121] In some embodiments, the platform 650 can generate (e.g., as opposed to simply receiving) machine-readable surgical instructions based on the surgical plan. For example, the surgical plan can include information regarding the delivery path, tools, and site to be implanted. The platform 650 can analyze the surgical plan and develop executable surgical instructions to perform a patient-specific surgery based on the capabilities of the robotic system (e.g., configuration and number of robotic arms, functionality of end effectors, guidance system, visualization system, etc.). This allows the surgical setup shown in FIG. 6 to be compatible with various types of robotic surgical systems.

[0122] The platform 650 may include one or more communication devices (e.g., components having VLC, WiMAX, LTE, WLAN, IR communications, PSTN, radio, Bluetooth, and / or Wi-Fi capabilities) for establishing a connection with the cloud 608 and / or the computing device 602 to access and / or download the surgical plan and / or machine-readable surgical instructions. For example, the cloud 608 may receive a request for a particular surgical plan from the platform 650 and transmit the plan to the platform 650. Once identified, the cloud 608 may transmit the surgical plan directly to the platform 650 for execution. In some embodiments, the cloud 608 may transmit the surgical plan to one or more intermediate network devices (e.g., the computing device 602) rather than directly to the platform 650. A user may review the surgical plan using the computing device 602 before transmitting the surgical plan to the platform 650 for execution. Additional details for identifying, storing, downloading, and accessing patient-specific surgical plans are described in U.S. patent application Ser. No. 16 / 990,810, filed Aug. 11, 2020, the disclosure of which is incorporated by reference in its entirety into this specification.

[0123] Platform 650 may include additional components not explicitly shown in FIG. 6. For example, in various embodiments, platform 650 may include one or more displays (e.g., an LCD display screen, an LED display screen, a projection display, a holographic display, or an augmented reality display (e.g., a heads-up display device or a head-mounted device)), one or more I / O devices (e.g., a network card, a video card, an audio card, a USB, Firewire or other external device, a camera, a printer, a speaker, a CD-ROM drive, a DVD drive, a disk drive, or a Blu-ray device), and / or memory (e.g., RAM, various caches, CPU registers, ROM, and writeable non-volatile memory such as flash memory, hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives, device buffers, etc.). In some embodiments, the above-mentioned components may be substantially similar to similar components described in detail with respect to computing device 200 of FIG. 2.

[0124] Without being bound by theory, it is expected that the use of a robotic surgical platform to perform various aspects of the surgical plan described herein will provide several advantages over conventional surgical techniques. For example, the use of a robotic surgical platform may improve surgical outcomes and / or reduce recovery time, for example, by reducing incision size, reducing blood loss, reducing the time of the surgical procedure, improving the accuracy and precision of the surgery (e.g., placement of the implant at the target location), etc. The platform 650 may also avoid or reduce user input errors by including one or more scanners to obtain information from, for example, instruments (e.g., instruments with search capabilities), tools, patient-specific implant 600 (e.g., after implant 600 is grasped by arm 655), etc. The platform 650 may verify the use of appropriate instruments before and during the surgical procedure. If the platform 650 identifies an inappropriate instrument or tool, it may send a warning to the user that a different instrument or tool should be attached. The user may scan the new instrument to verify that it is appropriate for the surgical plan. In some embodiments, the surgical plan includes instructions for use, a list of instruments, instrument specifications, replacement instruments, etc. The base 650 can perform pre-operative and post-operative validation routines based on information from the scanner.

[0125] 7A-13 further illustrate selected aspects of providing patient-specific medical care, e.g., according to method 500. For example, FIGS. 7A-7D illustrate an example of a patient dataset 700 (e.g., received in step 502 of method 500). The patient dataset 700 can include any of the information previously described with respect to patient datasets. For example, the patient dataset 700 can include patient information 701 (e.g., patient identification number, patient MRN, patient name, gender, age, BMI, date of surgery, surgeon, etc., as shown in FIGS. 7A and 7B), diagnosis information 702 (e.g., Oswestry Disability Index (ODI), VAS-Back score, VAS-Leg score, pre-operative pelvic incidence ratio, pre-operative lumbar lordosis, pre-operative PI-LL angle, pre-operative lumbar coronal Cobb, etc., as shown in FIGS. 7B and 7C), and image data 703 (e.g., x-ray, CT, MRI, etc., as shown in FIG. 7D). In the illustrated embodiment, the patient dataset 700 is collected by a health care provider (e.g., a surgeon, a nurse, etc.) using a digital and / or fillable report that can be accessed using a computing device (e.g., computing device 602 shown in FIG. 6). In some embodiments, the patient dataset 700 can be generated automatically or at least partially automatically based on the patient's digital medical record. In any case, once collected, the patient dataset 700 can be transmitted to a computing system (e.g., computing system 606 shown in FIG. 6) configured to generate a surgical plan for the patient.

[0126] 8A and 8B show an example of a virtual model 800 of a patient's native anatomy (e.g., as created in step 503 of method 500). In particular, FIG. 8A is a close-up view of the virtual model 800 of a patient's native anatomy, showing the patient's native anatomy of the lower spinal region. The virtual model 800 is a three-dimensional visual representation of the patient's native anatomy. In the illustrated embodiment, the virtual model includes a portion of the spinal column extending from the sacrum to the L4 vertebral level. Of course, the virtual model can include other regions of the patient's spinal column, including the cervical, thoracic, lumbar, and sacrum. Although the illustrated virtual model 800 includes only the bone structure of the patient's anatomy, in other embodiments, it can include additional structures such as cartilage, soft tissue, vascular tissue, nerve tissue, etc.

[0127] 8B illustrates a virtual model display 850 (sometimes referred to herein as "display 850") showing different views of the virtual model 800. The virtual model display 850 includes a three-dimensional view of the virtual model 800, one or more coronal sections 802 of the virtual model 800, one or more axial sections 804 of the virtual model 800, and / or one or more sagittal sections 806 of the virtual model 800. Of course, other views are possible and can be included in the virtual model display 850. In some embodiments, the virtual model 800 can be interactive, allowing a user to manipulate (e.g., rotate) the orientation or view of the virtual model 800, change the depth of the displayed sections, select and isolate particular bone structures, etc.

[0128] 9A-1-9B-2 show examples of a virtual model of a patient's native anatomical configuration (e.g., created in step 503 of method 500) and a virtual model of a patient's corrected anatomical configuration (e.g., created in step 504 of method 500). In particular, FIGS. 9A-1 and 9A-2 are front and side views, respectively, of a virtual model 910 showing a patient's native anatomical configuration, and FIGS. 9B-1 and 9B-2 are front and side views, respectively, of a virtual model 920 showing a corrected anatomical configuration of the same patient. Referring first to FIG. 9A-1, the front view of the virtual model 910 shows that the patient has an abnormal curvature of the spine (e.g., scoliosis), as indicated by the line X along the rostral-caudal axis of the spine. Referring now to FIG. 9A-1, the side view of the virtual model 910 shows that the patient has a collapsed intervertebral disc or a reduced spacing between adjacent vertebral endplates, as indicated by the ellipse Y. 9B-1 and 9B-2 show a corrected virtual model 920 that takes into account the abnormal anatomical configuration shown in FIG. 9A-1 and 9A-2. For example, FIG. 9B-1, a front view of virtual model 920, shows a patient's spine with corrected alignment (e.g., abnormal curvature has been reduced). This correction is shown by line X, which also follows the rostral-caudal axis of the spine. FIG. 9B-2, a side view of virtual model 920, shows a patient's spine with restored disc height (e.g., increased spacing between adjacent vertebral endplates), also shown by ellipse Y. Line X and ellipse Y are provided in FIGS. 9A-1-9B-2 to more clearly show the correction between virtual models 910 and 920, and are not necessarily included in virtual models generated in accordance with the present technique.

[0129] 10 illustrates an example of a surgical plan 1000 (e.g., as generated in step 506 of method 500). The surgical plan 1000 may include pre-operative patient metrics 1002, post-operative predicted patient metrics 1004, one or more patient images (e.g., patient images 703 received as part of a patient dataset), a virtual model 920 (which may be the model itself or one or more images derived from the model) of the patient's native anatomy (e.g., pre-operative patient anatomy), and / or corrected patient anatomy (e.g., predicted post-operative patient anatomy). The virtual model 920 of the predicted post-operative patient anatomy may optionally include one or more implants 1012 shown as being implanted in the patient's spinal region to show how the patient's anatomy will look after surgery. Although four implants 1012 are shown in the virtual model 920, the surgical plan 1000 may include more or fewer implants 1012, including 1, 2, 3, 5, 6, 7, 8 or more implants 1012.

[0130] The surgical plan 1000 may include additional information beyond that illustrated in FIG. 10. For example, the surgical plan 1000 may include pre-operative instructions, intra-operative instructions, and / or post-operative instructions. The surgical instructions may include one or more specific procedures to be performed (e.g., PLIF, ALIF, TLIF, LLIF, DLIF, XLIF, etc.) and / or one or more specific targets of the procedure (e.g., fusion of vertebral levels L1-L4, anchor screws inserted on the lateral aspect of L4, etc.). Although the surgical plan 1000 is illustrated in FIG. 10 as a visual report, the surgical plan 1000 may also be encoded with computer executable instructions that, when executed by a processor coupled to a computing device, cause the computing device to display the surgical plan 1000. In some embodiments, the surgical plan 1000 may also include machine readable surgical instructions for executing the surgical plan. For example, the surgical plan may include surgical instructions for a robotic surgical platform to execute one or more steps of the surgical plan 1000.

[0131] 11A, 11B provide a series of images illustrating an example of a patient surgical plan report 1100 that includes the surgical plan 1000 and can be sent to the surgeon for review and approval (e.g., as sent in step 508 of method 500). The surgical plan report 1100 can include a multi-page report detailing aspects of the surgical plan 1000. For example, the multi-page report can include a first page 1101 (e.g., as shown in FIG. 10) showing an overview of the surgical plan 1000, a second page 1102 showing a patient image (e.g., such as the received patient image 703, e.g., the patient image 703 received in step 502 and shown in FIG. 7D), a third page 1103 showing a close-up of a virtual model of the corrected anatomical configuration (e.g., the virtual model 920 shown in FIG. 9), and a fourth page 1104 prompting the surgeon to either approve or reject the surgical plan 900. Of course, additional information regarding the surgical plan can also be presented with the report 1100 in the same or a different format. In some embodiments, if the surgeon rejects the surgical plan 1000, the surgeon may be prompted to provide feedback regarding aspects of the surgical plan 1000 that the surgeon would like adjusted.

[0132] The patient surgical plan report 1100 can be presented to the surgeon on a digital display of a computing device (e.g., the client computing device 102 shown in FIG. 1 or the computing device 602 shown in FIG. 6). In some embodiments, the report 1100 is interactive, allowing the surgeon to manipulate various aspects of the report 1100 (e.g., adjusting the view of the virtual model, zooming in, zooming out, annotations, etc.). However, even though the report 1100 is interactive, the surgeon generally cannot directly modify the surgical plan 1000. Rather, the surgeon can provide feedback and suggested changes to the surgical plan 1000, which can be sent back to the computing system that generated the surgical plan 1000 for analysis and refinement.

[0133] FIG. 12A illustrates an example of a patient-specific implant 1200 (e.g., designed in step 516 and manufactured in step 518 of method 500), and FIG. 12B illustrates the implant 1200 implanted in a patient. The implant 1200 can be an orthopedic or other implant specifically designed to guide the patient's body to conform to a previously identified corrected anatomical configuration. In the illustrated embodiment, the implant 1200 is an interbody device having a first (e.g., upper) surface 1202 configured to engage a lower endplate surface of an upper vertebral body and a second (e.g., lower) surface 1204 configured to engage an upper endplate surface of a lower vertebral body. The first surface 1202 can have a patient-specific topography designed to match (e.g., mate with) the topography of the lower endplate surface of the upper vertebral body to form a substantially gap-free interface therebetween. Similarly, the second surface 1204 can have a patient-specific topography designed to match or mate with the topography of the upper endplate surface of the lower vertebral body to form a generally gap-free interface therebetween. The implant 1200 can also include a recess 1206 or other features configured to promote bone formation. Because the implant 1200 is patient-specific and designed to induce geometric changes in the patient, the implant 1200 is not necessarily symmetrical and is often asymmetrical. For example, in the illustrated embodiment, the implant 1200 has a non-uniform thickness such that the plane defined by the first surface 1202 is not parallel to the central longitudinal axis A of the implant 1200. Of course, because the implants described herein, including the implant 1200, are patient-specific, the present technology is not limited to any particular implant design or features. Additional features of patient-specific implants that can be designed and manufactured in accordance with the present technology are described in patent applications Ser. Nos. 16 / 987,113 and 17 / 100,396, the disclosures of which are incorporated herein by reference in their entireties.

[0134] The patient-specific medical procedures described herein may include implanting two or more patient-specific implants into a patient to achieve a corrected anatomical configuration (e.g., a multi-site procedure). For example, FIG. 13 illustrates a lower spinal region with three patient-specific implants 1300a-c implanted at different vertebral levels. More specifically, a first implant 1300a is implanted between the L3 and L4 vertebral bodies, a second implant 1300b is implanted between the L4 and L5 vertebral bodies, and a third implant 1300c is implanted between the L5 vertebral body and the sacrum. Together, the implants 1300a-c can cause the patient's spinal implant region to assume a previously identified corrected anatomical configuration (e.g., transform the patient's anatomical configuration from a pathological pre-operative configuration to an optimized post-operative configuration). In some embodiments, more or fewer implants are used to achieve a corrected anatomical configuration. For example, in some embodiments, one, two, four, five, six, seven, eight, or more implants are used to achieve a corrected anatomical configuration. In embodiments including more than one implant, the implants do not necessarily have the same shape, size, or function. Indeed, multiple implants often have different shapes and topographies to correspond to the target vertebral levels at which they are implanted. As also shown in FIG. 13, the patient-specific medical procedures described herein can include treating a patient at multiple target regions (e.g., multiple vertebral levels).

[0135] In addition to designing patient-specific medical care based on reference patient data sets, the system and method of the present technology can also design patient-specific medical care based on the disease progression of a particular patient. Thus, in some embodiments, the present technology includes a software module (e.g., a machine learning model or other algorithm) that can be used to analyze, predict, and / or model the disease progression of a particular patient. The machine learning model can be trained based on multiple reference patient data sets including the patient data described with respect to FIG. 1 as well as disease progression metrics of each of the reference patients. The progression metrics can include measurements of disease metrics over a period of time. Suitable metrics can include spinopelvic parameters (e.g., LL, pelvic tilt, sagittal vertical axis (SVA), Cobb angle, coronal offset, etc.), disability scores, functional ability scores, flexibility scores, VAS pain scores, or the like. The progression of metrics for each reference patient can be correlated with other patient information (e.g., age, sex, height, weight, activity level, diet, etc.) of the particular reference patient.

[0136] In some embodiments, the technology includes a disease progression module that includes algorithms, machine learning models, or other software analysis tools to predict disease progression for a particular patient. The disease progression module can be trained based on a reference patient data set that includes patient information (e.g., age, sex, height, weight, activity level, diet) and disease metrics (e.g., diagnosis, spinopelvic parameters such as LL, pelvic tilt, SVA, Cobb angle, coronal offset, disability score, functional ability score, flexibility score, VAS pain score, etc.). The disease metrics can include values ​​for a period of time. For example, the reference patient data can include values ​​of the disease metrics on a daily, weekly, monthly, bimonthly, yearly, or other basis. By measuring the metrics over a period of time, the change in the value of the metric can be tracked as an estimate of disease progression and correlated with other patient data.

[0137] Thus, in some embodiments, the disease progression module can estimate the rate of disease progression for a particular patient. Progression can be estimated by providing an estimated change in one or more disease metrics over a period of time (e.g., X% increase in disease metric per year). This rate can be constant (e.g., 5% increase in pelvic tilt per year) or variable (e.g., 5% increase in pelvic tilt in year 1, 10% increase in pelvic tilt in year 2, etc.). In some embodiments, the estimated rate of progression can be transmitted to a surgeon or other healthcare provider, who can review and update the estimate as necessary.

[0138] As a non-limiting example, a particular patient who is a 55-year-old male may have an SVA value of 6 mm. The disease progression module can analyze the patient reference dataset to identify disease progression in individual reference patients who have one or more similarities to the particular patient (e.g., individual reference patients who have an SVA value of about 6 mm and are about the same age, weight, height, and / or sex as the patient). Based on this analysis, the disease progression module can predict the rate of disease progression in the absence of surgical intervention (e.g., the patient's VAS pain score is likely to increase by 5%, 10%, or 15% per year in the absence of surgical intervention, the SVA value is likely to continue to increase by 5% per year in the absence of surgical intervention, etc.).

[0139] The systems and methods described herein can also generate models / simulations based on the estimated disease progression rate, thereby modeling different outcomes over a desired time period. Additionally, the models / simulations can consider any number of additional diseases or conditions to predict the patient's overall health, mobility, etc. These additional diseases or conditions can be used in combination with other patient health factors (e.g., height, weight, age, activity level, etc.) to generate a patient health score that reflects the patient's overall health. The patient health score can be displayed for review by the surgeon and / or can be incorporated into the disease progression estimate. Thus, the technology can generate one or more virtual simulations of predicted disease progression to show how the patient's anatomy is predicted to change over time. Physician input can be used to generate or modify the virtual simulations. The technology can generate one or more post-treatment virtual simulations based on the received physician input for review by the healthcare provider, patient, etc.

[0140] In some embodiments, the technology can also predict, model, and / or simulate disease progression based on one or more potential surgical interventions. For example, the disease progression module can simulate how a patient's anatomy will look one, two, five, or ten years post-surgery for multiple surgical intervention options. The simulations can also incorporate non-surgical factors such as the patient's age, height, weight, sex, activity level, other health conditions, or the like, as previously described. Based on these simulations, the system and / or surgeon can select which surgical intervention is most suitable for long-term effectiveness. These simulations can also be used to determine patient-specific corrections to compensate for the disease progression of the protrusion.

[0141] Thus, in some embodiments, multiple disease progression models (e.g., 2, 3, 4, 5, 6 or more) are simulated to provide disease progression data for multiple different surgical intervention options or other scenarios. For example, a disease progression module can generate a model that predicts postoperative disease progression for each of three different surgical interventions. A surgeon or other healthcare provider can review the disease progression models and, based on the review, select the one of the three surgical intervention options that is likely to provide the best long-term outcome for the patient. Of course, as described herein, the selection of the optimal surgical intervention can also be fully or semi-automated.

[0142] Based on the modeled disease progression, the systems and methods described herein can also (i) identify the optimal time for surgical intervention and / or (ii) identify the optimal type of surgery for the patient. In some embodiments, the technology thus includes an intervention timing module that includes an algorithm, machine learning model, or other software analysis tool for determining the optimal timing of surgical intervention in a particular patient. This can be done, for example, by analyzing patient reference data that includes (i) pre-operative disease progression metrics of the individual reference patients, (ii) disease metrics at the time of surgical intervention of the individual reference patients, (iii) post-operative disease progression metrics of the individual reference patients, and / or (iv) scored surgical outcomes of the individual reference patients. The intervention timing module can compare the disease metrics of a particular patient to a reference patient dataset to determine, for similar patients, the time point in disease progression at which surgical intervention provided the best outcome.

[0143] As a non-limiting example, the reference patient data set may include data related to the SVA of the reference patient. The data may include (i) the SVA value of the individual patient at a time period before the surgical intervention (e.g., how fast and to what extent the SVA value changed), (ii) the SVA of the individual patient at the time of the surgical intervention, (iii) the change in SVA after the surgical intervention, and (iv) how successful the surgical intervention was (e.g., based on pain, quality of life, or other factors). Based on the above data, the intervention timing module may identify when a surgical intervention is most likely to result in a favorable outcome based on the value of the SVA of the particular patient. Of course, the above metrics are provided by way of example only, and the intervention timing module may incorporate other metrics (e.g., LL, pelvic tilt, SVA, Cobb angle, coronal offset, disability score, functional ability score, flexibility score, VAS pain score) instead of or in combination with SVA to predict when a surgical intervention is most likely to result in a favorable outcome for a particular patient.

[0144] The intervention timing module may also incorporate one or more mathematical rules based on value thresholds for various disease metrics. For example, the intervention timing module may indicate that surgical intervention is necessary if one or more disease metrics exceed a predetermined threshold or meet other criteria. Exemplary thresholds indicating that surgical intervention is necessary include SVA values ​​greater than 7mm, lumbar lordosis and pelvic incidence misalignment greater than 10 degrees, Cobb angle greater than 10 degrees, and / or a combination of Cobb angle and LL / PI misalignment greater than 20 degrees. Of course, other thresholds and metrics may be used, and the above are provided by way of example only and in no way limit the present disclosure. In some embodiments, the above rules may be tailored to a particular patient population (e.g., for men over 50 years of age, an SVA value greater than 7mm indicates the need for surgical intervention). If a particular patient does not exceed a threshold indicating that surgical intervention is recommended, the intervention timing module may provide an estimate of when the patient's metrics will exceed one or more thresholds, thereby providing the patient with an estimate of when surgical intervention will become recommended.

[0145] The present technology can also include a treatment planning module that can identify the type of surgical plan that is best suited for a patient based on the patient's disease progression. The treatment planning module can be an algorithm, machine learning model, or other software analysis tool that is trained based on multiple reference patient data sets, as described above, or other software analysis tool. The treatment planning module can also incorporate one or more mathematical rules for identifying the surgical plan. As a non-limiting example, if the LL / PI mismatch is between 10 degrees and 20 degrees, the treatment planning module can recommend an anterior fixation surgery, but if the LL / PI mismatch is greater than 20 degrees, the treatment planning module can recommend both anterior and posterior fixation surgery. As another non-limiting example, if the SVA value is between 7mm and 15mm, the treatment planning module can recommend posterior fixation surgery, but if the SVA is greater than 15mm, the treatment planning module can recommend both posterior and anterior fixation surgery. Of course, other rules can be used, and the above are provided merely as examples and are not intended to limit the present disclosure.

[0146] Without being bound by theory, the incorporation of disease progression modeling into the patient-specific medical procedures described herein can further enhance the efficacy of surgery. For example, in many cases, it may be disadvantageous for an operator to perform surgery after a patient's disease has progressed to an irreversible or unstable state. However, it may also be disadvantageous to perform surgery too early before the patient's disease causes symptoms and / or when the patient's disease may not progress further. Thus, the disease progression module and / or intervention timing module help identify the window in which surgical intervention for a particular patient is most likely to result in a favorable outcome for that patient. Figures 14A and 14B are schematic anterior and lateral views, respectively, of a deployed patient-specific IBF device 1430 ("device 1430") deployed between a first vertebra 1410 (e.g., a relatively superior vertebra) and a second vertebra 1420 (e.g., a relatively inferior vertebra), in accordance with some embodiments of the present technology. The device 1430 can be in a folded or low profile configuration for delivery to the disc space between the first and second vertebrae 1410, 1420. For example, the folded device 1430 can be inserted manually by surgical navigation or via a surgical robot and then expanded at the implantation site. Once deployed, the device 1430 can provide one or more adjustments, corrections (e.g., corrections to the alignment of the first and second vertebrae 1410, 1420, spinal column segments, etc.), etc., as described in more detail below. FIG. 14B shows the device 1430 fully expanded along a vertical axis indicated by arrow 1437 to space the first and second vertebrae 1410, 1420 apart. As described in more detail below, the device 1430 can be contoured and / or otherwise customized to match the contours of the first and second vertebrae 1410, 1420. An auxiliary implant 1453 is also shown. In some embodiments, the auxiliary implant 1453 is patient-specific and includes a curved rod 1457 to provide spacing between the vertebrae. Fasteners 1459 can connect the rod 1457 to the vertebrae.Supplemental patient-specific implants can include, but are not limited to, rod and screw systems, interspinous spacers, or other orthopedic implants. In some embodiments, the device 1430 can also be used with non-patient-specific devices and implants. The systems disclosed herein can design implants that are implanted at different locations to provide specific treatments.

[0147] In the illustrated embodiment of FIG. 14A, the device 1430 includes an expansion body or body 1431 operable to controllably expand or deploy the device 1430. The body 1431 has an expanded or expanded configuration selected based on the treatment to be performed. The internal components of the body 1431 can be designed and manufactured to achieve a desired treatment plan. The central reservoir 1432 can be configured to expand hydraulically or pneumatically. The patient-specific expansion can be selected, at least in part, based on the design of the other components of the device 1430. The device 1430 can have a patient-specific design selected, for example, to enhance fusion (e.g., bone growth into the vertebral bodies), enhance fixation between the vertebral bodies, limit stress within the vertebral bodies, or for such purposes. For example, the device 1430 can have a volume or receiving window 1445 for receiving a material, such as a material that promotes bone growth.

[0148] The body 1431 can be configured to expand from a collapsed configuration to an expanded configuration (shown in FIGS. 14A and 14B ) and can include one or more expansion mechanisms (e.g., a screw jack mechanism, a wedge, a scissor mechanism, etc.), angled or inclined surfaces, an inflatable member, or other components to cause deployment. Additionally, the body 1431 can include linkages, pin connections, link assemblies, or other components to connect various other components. In some embodiments, the device 1430 includes a drive feature 1436 (e.g., a drive head, a screw head, a bolt head, etc.) that can be coupled to a drive instrument. The drive feature 1436 can be coupled to one or more drive elements (e.g., a screw body, a wedge member, a drive shaft, etc.) of the device 1430. In some embodiments, the drive feature 1436 can be rotated in opposite directions to controllably expand or collapse the device 1430. The body 1431 can include an outer cover or bellows 1439 that surrounds the internal moveable components. In other embodiments, the internal movable components may be exposed to the surrounding environment, and the upper and lower components 1434, 1435 may inhibit or limit tissue movement between the components of the body 1431.

[0149] 14A and 14B, the device 1430 may further include a first endplate 1440 (e.g., an upper endplate) and a lockable joint 1438a coupling the first endplate 1440 to the body 1431. The device 1430 may further include a second endplate 1450 (e.g., a lower endplate) connected to the body 1431 via a lockable joint 1438b. The lockable joints 1438a, 1438b may be selectively transitionable between an unlocked configuration and a locked configuration. In the unlocked configuration, the lockable joints 1438a, 1438b allow the first endplate 1440 and the second endplate 1450 to move relative to the body 1431. In the locked configuration, the lockable joints 1438a, 1438b prevent or at least reduce movement of the first endplate 1440 and the second endplate 1450 relative to the body. Thus, in the unlocked configuration, the lockable joints 1438a, 1438b can be configured to provide a desired range of motion that allows the endplates 1440, 1450 to conform to the patient's anatomy. In some embodiments, the lockable joints 1438a, 1438b are ball joints, hinges, tethers, or other connections that allow relative movement between the endplates 1440, 1450. The lockable joints 1438a, 1438b can be connected to opposite ends of the body 1431 such that the lockable joints 1438a, 1438b are moved with the respective endplates 1440, 1450 during expansion of the device 1430. In some embodiments, the maximum range of motion of the lockable joints 1438a, 1438b is selected based on the desired range of motion of the spinal column segments. In some embodiments, the position, configuration, and / or movement provided by the lockable joints 1438a, 1438b after locking can be selected based on the desired range of motion.

[0150] The first endplate 1440 includes a first surface 1442 (e.g., an upper surface) that mates with the lower surface 1412 of the first vertebra and a second surface 1444 (e.g., a lower surface) that mates with the lockable joint 1438a and / or the upper component 1434. Further, as illustrated in Figures 14A and 14B, the first surface 1442 is customized to the patient-specific topology of the lower vertebral surface 1412 of the first vertebra 1410. For example, as illustrated with respect to Figure 14A, the lower vertebral surface 1412 can include patient-specific features 1414, such as recessed areas, valleys, or divots, as illustrated. A flat endplate that is not customized to the patient-specific topology of the lower vertebral surface 1412 would result in gaps 1462 at the patient-specific features 1414 (i.e., gaps 1462 where the first endplate 1440 does not contact the first vertebra 1410). However, in the illustrated embodiment, the contoured first surface 1442 conforms to the lower vertebral surface 1412 to increase the contact area, thereby limiting or reducing stresses, such as stresses, in the first vertebra 1410 and / or the device 1430. Furthermore, as a result of the more complete contact made by the first endplate 1440, the device 1430 is expected to contact a more optimal surface area with the first vertebra 1410, improving traction of the device 1430 and / or improving the expected outcome of a medical procedure in which the device 1430 is used.

[0151] The contoured first surface 1442 of the device 1430 can also reduce or limit movement between the first vertebra 1410 and the device 1430. Reducing movement can help reduce spinal fixation time. In some embodiments, the contoured first surface 1442 can have a thickened or protruding area that is substantially geometrically contemporaneous with the patient-specific features 1414 along the lower vertebral surface 1412. This further helps the endplate 1440 seat against the first vertebra 1410. When an axial load is applied to the device 1430, the customized fit at the interface can limit, reduce, or substantially prevent relative movement between the device 1430 and the first vertebra 1410. In some procedures, the device 1430 can be configured to provide a generally gap-free interface when the device 1430 is in a fully expanded implanted configuration.

[0152] Similarly, the second endplate 1450 includes a first surface 1452 (e.g., a lower surface) that mates with the upper surface 1422 of the second vertebra 1420 and a second surface 1454 (e.g., an upper surface) that mates with the lockable joint 1438b and / or the lower component 1435. Further, as illustrated in FIGS. 14A and 14B, the first surface 1452 is customized to the patient-specific topology of the upper surface 1422 of the second vertebra 1420. For example, as illustrated with respect to FIG. 14A, the upper surface 1422 can include a number of patient-specific features 1424, such as the valleys or divots shown. An endplate that is not customized to the patient-specific topology of the upper surface 1422 will accordingly include one or more gaps 1464 corresponding to the patient-specific features 1424 where the second endplate 1450 does not contact the second vertebra 1420. In contrast, the patient-specific topology of the second endplate 1452 can be contoured to occupy the gap 1464 and therefore contact the upper surface 1422 of the second vertebra 1420 at the patient-specific feature 1424. As a result of the more complete contact made by the second endplate 1450, the device 1430 is expected to make more optimal surface area contact with the second vertebra 1420 to improve traction of the device 1430 and / or improve the expected outcome of a medical procedure using the device 1430.

[0153] In some embodiments, the first and second endplates 1440, 1450 can additionally or alternatively be customized for a medical procedure prescribed for a patient. In some embodiments, the first and second endplates 1440, 1450 are configured to help provide height restoration, lordotic correction, and / or coronal correction. For example, the first and second endplates 1440, 1450 can vary in thickness (e.g., thereby including a gradient) in the xy plane to help provide lordotic correction and / or coronal correction. In some embodiments, the height restoration, lordotic correction, and / or coronal correction provided by the first and second endplates 1440, 1450 can be patient-specific (e.g., based on a predetermined amount of correction and / or patient-specific factors, as described in more detail below).

[0154] In some embodiments, the first and second endplates 1440, 1450 can additionally or alternatively be customized to account for load bearing strengths, toughness, fatigue properties, or characteristics that may vary along the endplates 1440, 1450. For example, the first and second endplates 1440, 1450 can compensate for strong and / or weak zones identified in the first and second vertebrae 1410, 1420 that are patient specific. In some embodiments, the first and second endplates 1440, 1450 can be configured to apply more force to identified strong or high load bearing zones (e.g., zones comprised of bone or tissue having a relatively high yield strength, fracture toughness, etc.) in the first and second vertebrae 1410, 1420 and / or apply less force (or no force) to identified weak zones (e.g., zones comprised of bone or tissue having a relatively low yield strength, fracture toughness, etc.).

[0155] In some embodiments, the first and second endplates 1440, 1450 can additionally or alternatively be customized to achieve a desired fit. The desired fit can be designed, for example, to reduce movement between the device 1430 and the vertebral body, to facilitate seating during an implantation procedure, to increase friction, or the like. The first and second endplates 1440, 1450 can include anchors, texturing, protrusions, or other suitable elements selected to provide the desired fit.

[0156] In addition to (or instead of) the patient-specific characteristics of the first and second endplates 1440, 1450, the device 1430 can be configured to provide a precise, predetermined height restoration, lordotic correction, and / or coronal angle correction. In some embodiments, the device 1430 can be adjusted intraoperatively. For example, a surgical instrument can be connected to the first and second endplates 1440, 1450 to adjust the lockable joint 1438 intraoperatively until the inclination of the first and second endplates 1440, 1450 provides the predetermined lordotic correction and / or coronal segment correction, and then the lockable joint 1438 can be locked. In another example, a surgical instrument can be connected intraoperatively to the drive feature 1436 of the device 1430 to expand the device 1430 until the predetermined height restoration is achieved. Once the predetermined height restoration is achieved, the device 1430 can be locked in a configuration that provides the predetermined height restoration. In some embodiments, the device 1430 is preoperatively adjusted and locked and then inserted to achieve a predetermined correction. In some embodiments, one or more components of the device 1430 are adjusted and locked preoperatively while other components are adjusted intraoperatively. For example, in some embodiments, the lockable joints 1438a, 1438b can be preoperatively adjusted and locked to achieve a predetermined angle of the first and second endplates 1440, 1450 while the device 1430 is expanded in situ to provide a predetermined height restoration.

[0157] In some intraoperative embodiments, the device 1430 is inserted and adjusted to achieve optimal height and angle correction under surgical navigation guidance. For example, surgical instruments can be used to monitor the expansion and / or angle correction of the device 1430. In some embodiments, the device 1430 includes a lockable mechanical and / or electrical stop (not shown) that can be set preoperatively to stop the expansion and / or angle correction at a predetermined point. For example, the device 1430 can include a lockable mechanical mechanism that prevents the device 1430 from expanding beyond a predetermined height restoration. In these embodiments, intraoperative adjustments can be precisely adjusted to a predetermined configuration without the use of additional surgical instruments by adjusting until the lockable stop prevents further adjustment.

[0158] Without being bound by theory, the device 1430 is expected to provide several advantages over conventional IBF devices. First, the device 1430 can be configured for two types of adjustment: (1) increasing (e.g., expanding) the space / distance between the first endplate 1440 and the second endplate 1450 to restore proper intervertebral spacing, and (2) selectively and independently altering the angle of the first endplate 1440 and the second endplate 1450 relative to the body 1431 via manipulation of the lockable joints 1438a, 1438b to restore proper intervertebral alignment. Second, as detailed above, the device 1430 is designed with patient-specific features that are expected to improve fit, optimize load-bearing areas, reduce the likelihood of overcorrection, or otherwise improve device performance. Of course, other advantages of the device 1430 and the present technology will be apparent to one of ordinary skill in the art based on this detailed description and figures. Thus, the present technology is not limited by the advantages described above. Indeed, additional examples and features of expandable IBF devices are described in U.S. patent application Ser. No. 17 / 835,777, the disclosure of which is incorporated herein by reference in its entirety.

[0159] In addition to the above, in some embodiments, as shown in FIG. 14B, the device 1430 includes one or more sensors 1476 (sometimes referred to as device sensors), e.g., embedded in the device 1430 or otherwise coupled to the device 1430. The sensors 1476 can be pressure sensors, temperature sensors, transducers, accelerometers, fluid sensors, etc. The sensors 1476 measure at least one of the following when the device 1430 is implanted in a patient: pressure exerted on the device 1430 by the patient's body, the temperature of the patient's body, movement of the patient's body, etc. In some embodiments, the device 1430 includes one or more actuators 1474 embedded in the device 1430. The actuators 1474 receive electrical signals generated ex situ (e.g., from a controller (not shown) located outside the patient) and move parts of the device 1430 to place the device 1430 into different physical configurations as described above. For example, the actuator 1474 can provide one or more adjustments or corrections to the physical configuration of the device 1430, expand the device 1430 along a vertical axis (indicated by arrow 1437 to separate the first and second vertebrae 1410, 1420), rotate the spatial orientation of the first endplate 1440 and / or the second endplate 1450 relative to the body 1431 (via the first and second lockable joints 1438a, 1438b), contour and / or otherwise position the device 1430 to match the contours of the first and second vertebrae 1410, 1420, etc.

[0160] In some embodiments, as shown in FIG. 14B, the auxiliary implant 1453 includes one or more sensors 1480 (sometimes referred to as implant sensors) embedded in the auxiliary implant 1453. The auxiliary implant 1453 may also be referred to as a "spinal implant." The sensor 1480 may be a pressure sensor, a temperature sensor, a transducer, an accelerometer, a fluid sensor, or the like. Similar to the sensor 1480 of the device 1430, the sensor 1480 measures at least one of the pressure exerted by the patient's body on the spinal implant 1453 when the spinal implant 1453 is implanted in the patient, the temperature of the patient's body, the movement of the patient's body, or the like. However, unlike the device 1430, the spinal implant 1453 is not implanted in the disc space between the first vertebra 1410 and the second vertebra 1420, and thus the sensor 1480 is positioned to measure one or more physiological parameters at a location spaced apart from the device 1430. In some embodiments, parameters measured by sensor 1476 on device 1430 and sensor 1480 on spinal implant 1453 are compared to help determine appropriate adjustments to device 1430 and / or spinal implant 1453. For example, sensor 1476 can detect a first pressure value indicative of a first load on device 1430 and sensor 1480 can detect a second pressure value indicative of a second load on spinal implant 1453. If the relationship between the first load and the second load is outside a predetermined range (e.g., within 50%, 75%, 80% of a threshold or range) or does not meet another predetermined metric (e.g., the first load is a particular value, e.g., at least 10% greater, at least 20% greater, at least 50% greater, etc., than the second load), one or both of device 1430 and spinal implant 1453 can be adjusted until the sensed parameters meet the predetermined range or other metric.

[0161] Thus, in some embodiments, the spinal implant 1453 includes one or more actuators 1478 (sometimes referred to as implant actuators) embedded in the spinal implant 1453. The actuators 1478 receive electrical signals generated ex situ (e.g., from a controller (not shown) located outside the patient) and move portions of the spinal implant 1453 to place the spinal implant 1453 into different physical configurations, as described below.

[0162] For example, in some embodiments, the rod 1457 includes a first movable portion 1470a that can be moved relative to a second movable portion 1470b of the rod 1457 using one or more actuators 1478 embedded in the spinal implant 1453. In some embodiments, the rod 1457 includes an inner member 1472 that can be moved relative to a fastener 1459 that couples the rod 1457 to the vertebrae such that the rod 1457 extends or retracts. For example, the one or more actuators 1478 are operable to controllably expand or deploy the spinal implant 1453. The spinal implant 1453 has a deployed or implanted configuration that is selected based on the treatment to be performed. The patient-specific expansion can be selected based, at least in part, on the design of the other components of the spinal implant 1453. The rod 1457 and the device 1430 can be configured to provide a desired range of motion. In some embodiments, the maximum range of motion of the rod 1457 is selected based on the desired range of motion of the device 1430.

[0163] The spinal implant 1453 can be configured to be expanded from a collapsed configuration to an expanded configuration (shown in FIG. 14B) and can include one or more expansion mechanisms (e.g., a screw jack mechanism, a wedge, a scissor mechanism, etc.), angled or angled surfaces, inflatable members, or other components to cause deployment. Additionally, the spinal implant 1453 can include linkages, pin connections, linkage assemblies, or other components to connect various other components. In some embodiments, the spinal implant 1453 includes a drive feature (e.g., a drive head, a screw head, a bolt head, etc.) that can be coupled to a drive instrument.

[0164] In some embodiments, the computer system can be coupled to the spinal implant 1453 and / or device 1430 in either a wired or wireless configuration. The computer system can be the same as or similar to the computer system 100 shown and described in more detail with reference to FIG. 1. The computer system can be implemented using any of the components shown and described in more detail with reference to FIGS. 1 and 2. The computer system is used to program and adjust the physical configuration of the spinal implant 1453 and / or device 1430 during and / or after surgery to balance the loads that the patient's body exerts on the spinal implant 1453 and / or device 1430. In this way, the computer system adjusts the mechanical properties of the spinal implant 1453 and / or device 1430. During surgery, the spinal implant 1453 and / or device 1430 is positioned in the patient without any load being applied (i.e., without simulating the actual load when the patient is standing). The initial position of the spinal implant 1453 and / or device 1430 may be referred to as the "small delivery configuration."

[0165] After surgery, the spinal implant 1453 and / or device 1430 are in a load-bearing configuration. The computer system can adjust the configuration of the spinal implant 1453 and / or device 1430 while the patient is awake after surgery to reduce pain and / or physical discomfort experienced by the patient in real time; and further adjust the configuration to provide an improved biomechanical system, including alignment, balance, or spacing. In some embodiments, for example, the computer system receives implant sensor readings from one or more implant sensors 1480 embedded in the spinal implant 1453. The spinal implant 1453 is configured in a first physical configuration. The implant sensor readings are received after surgery has been performed and the patient is awake. For example, the implant sensor readings are digital and / or analog signals that are implanted using the method shown and described in more detail with reference to FIG. 2. The implant sensor readings are indicative of loads applied by the patient's spine to the spinal implant 1453. This load may cause physical discomfort to the patient when the spinal implant 1453 is configured in the first physical configuration.

[0166] In some embodiments, the computer system receives device sensor readings from one or more device sensors 1476 embedded in the intervertebral fusion device 1430 that is implanted in the patient during surgery. The device sensor readings are received after the surgery is performed and before the implant sensor readings are received. This is because the fusion device 1430 can be adjusted soon after surgery (before the vertebrae are fused) and the rod 1457 can be adjusted several months later. Thus, in some embodiments, whether the device 1430 or the rod 1457 is adjusted depends at least in part on the time that has passed since surgery. For example, if the computer system determines that an adjustment is needed to reduce patient discomfort, improve load bearing, or the like, the computer system can also determine whether to adjust the device 1430 or the rod 1457. In some embodiments, the computer system can adjust the device 1430 if the adjustment is performed intraoperatively and / or before a certain amount of time has passed since surgery, such as within 3 days, within 1 week, within 2 weeks, etc. The computer system can adjust the rod 1457 if adjustments are to be made post-operatively and / or after a certain amount of time has passed since surgery, such as more than three days post-operatively, more than one week post-operatively, more than two weeks post-operatively, etc. In this manner, the computer system can utilize the time since surgery to help determine appropriate adjustments to reduce patient discomfort, improve load bearing, and otherwise improve long-term surgical outcomes. In some embodiments, the device 1430 and / or rod 1457 can be adjusted based on, for example, a surgical plan, an orthodontic plan, a post-operative treatment plan, etc. For example, the computer system can generate an orthodontic plan having a post-operative adjustment protocol, as described with reference to FIG. 5. The device 1430 and / or rod 14572 can be programmed to perform one or more post-operative adjustments according to a predetermined schedule. The schedule can include adjustments at specific times, adjustments based on sensor readings, physician input, etc.In some embodiments, the devices 1430 and / or rods 157 can make intra-operative or post-operative adjustments based on data collected in real time to achieve one or more target outcomes, such as range of motion, pelvic parameters within target ranges, etc. The devices 1430 and / or rods 157 can confirm adjustments achieved based on these sensor readings and can communicate with each other to monitor and / or confirm spinal correction.

[0167] The computer system uses a machine learning module of the computer system to extract a feature vector from the implant sensor readings. The feature vector is indicative of physical discomfort due to loading. In some embodiments, the feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine. The use of machine learning to implement the embodiments disclosed herein is illustrated and described in more detail with reference to FIG. 1. The feature vector includes features that are individual measurable characteristics or characteristics of the raw implant sensor measurements. For example, the features can be numerical or structural. In one embodiment, the machine learning module applies dimensionality reduction (e.g., via linear discriminant analysis (LDA), principal component analysis (PCA), or the like) to reduce the amount of data in the features of the content items to a smaller, more representative data set.

[0168] The computer system generates the implant electrical signals using a machine learning module based on the feature vector. The machine learning module is trained based on the patient data set to generate implant electrical signals that balance the load to reduce physical discomfort caused by the load. Use of the patient data set is illustrated and described in more detail with reference to FIGS. 4A-4C and 7A-7D. In some embodiments, the computer system generates the device electrical signals using a machine learning module based on device sensor readings. The machine learning module is trained based on the patient data set to generate device sensor readings to reduce physical discomfort caused by the intervertebral fusion device 1430. The computer system transmits implant electrical signals to one or more implant actuators 1478 embedded in the spinal implant 1453 to cause the one or more implant actuators 1478 to configure the spinal implant 1453 in a second physical configuration such that the load is balanced. The machine learning or artificial intelligence module can run in a feedback loop such that a first patient is treated and the machine learning module learns to treat the first patient and other patients.

[0169] In some embodiments, configuring the spinal implant 1453 into the second physical configuration includes adjusting at least one of the spinal implant's screws, cages, plates, rods 1457, disks, spacers, expandable devices, stents, brackets, ties, scaffolds, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, or joint replacements using one or more implant actuators. In other embodiments, the spinal implant 1453 includes a reservoir that includes at least one of anesthesia or a steroid. Alternatively, the reservoir can be coupled to the spinal implant 1453. Configuring the spinal implant 1453 into the second physical configuration can include adjusting the reservoir to change the amount of anesthesia or steroid delivered to the patient. Adjusting the biochemical properties provides pain management, reduced infection, and favorable biological responses.

[0170] FIG 15 is a flow diagram illustrating a process 1500 for patient-specific adjustment of a spinal implant. In some embodiments, the process 1500 of FIG 15 is performed by a computer system, such as the exemplary computer system 100 shown and described in more detail with reference to FIG 1. A particular entity, such as the data analysis module 116 shown and described in more detail with reference to FIG 1, performs some or all of the steps of the process in other embodiments. Similarly, embodiments may include different and / or additional steps or may perform steps in a different order.

[0171] The computer system receives 1504 implant sensor readings from one or more implant sensors embedded in the spinal implant configured in the first physical configuration. The implant sensors are the same as or similar to the sensors 1476 and / or 1480 shown and described in more detail with reference to FIGS. 14A and 14B. The spinal implants can be the same as or similar to the spinal implant 1453 and / or device 1430 shown and described in more detail with reference to FIGS. 14A and 14B. The spinal implants are implanted in the patient during surgery. The implant sensor readings are received after the surgery is performed and are indicative of loads applied to the spinal implant by the patient's spine. The loads cause physical discomfort to the patient when the spinal implant is configured in the first physical configuration.

[0172] The computer system extracts 1508 a feature vector from the implant sensor readings using a machine learning module of the computer system. The feature vector is indicative of a physical discomfort caused by the load. In some embodiments, the feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine. The use of machine learning to implement the embodiments disclosed herein is shown and described in more detail with reference to FIG. 1.

[0173] The computer system generates 1512 an implant electrical signal using a machine learning module based on the feature vector. The machine learning module is trained based on the patient data set to generate an implant electrical signal that balances the load so that physical discomfort caused by the load is reduced. Use of the patient data set is illustrated and described in more detail with reference to FIGS. 4A-4C and 7A-7D. In some embodiments, the computer system generates 1512 an implant electrical signal using a machine learning module based on the device sensor readings. The machine learning module is trained based on the patient data set to generate device sensor readings to reduce physical discomfort caused by the intervertebral fixation device 1430.

[0174] The computer system sends 1516 an implant electrical signal to one or more implant actuators embedded in the spinal implant, causing the one or more implant actuators to configure the spinal implant in a second physical configuration such that the load is balanced. The implant actuators can be the same as or similar to the actuators 1474 and / or 1478 shown and described in more detail with reference to Figures 14A and 14B. The machine learning or artificial intelligence module can run in a feedback loop such that a first patient is treated and the machine learning module learns to treat other patients.

[0175] As one of ordinary skill in the art can appreciate from the disclosure herein, device 1430 is provided as a simple schematic example of a patient-specific IBF device. The patient-specific implants described herein are designed to fit the anatomy of an individual patient, and therefore the size, shape, and geometry of the patient-specific implant will vary depending on the anatomy of the individual patient. Thus, the technology is not limited to a particular IBF device or implant design and thus may include other implants other than those shown or described herein, including other disc or joint replacements not expressly described herein. In various embodiments, a spinal procedure may include implantation of one or more levels of implants.

[0176] 16A shows a patient's spine 2030 with intervertebral implants placed at each individual level. The implants 2000a-g (collectively "implants 2000") can be implanted to match the anatomical features of the individual levels. A remote device or controller 2049 that controls the actuators of the implants 2000 can be used to perform non-invasive post-operative spinal adjustments. The remote device 2049 can wirelessly communicate with selected or all of the implants.

[0177] The implant 2000a is implanted in a level 2031 having normal endplates with no defects in the surface topology. The endplates of the implant 2000a can have a convex shape that matches the illustrated concave endplates of the adjacent vertebrae of the level 2031. The implant 2000a can have an actuator mechanism 2001 that is powered by an externally applied field (e.g., a magnetic field or another field) provided by a remote device 2049. The actuation mechanism 2001 can include an inductively rechargeable power source, actuation elements, a processor, a transmitter / receiver, etc. The location, number, and capabilities of the actuation mechanism can be selected based on the available adjustment functions (e.g., range expansion / contraction, driving force, etc.).

[0178] The implants 2000 can have patient-specific characteristics. Implant 2000b is implanted at level 2032 with severe concave shapes of the superior and inferior vertebrae. Implant 2000b has a large convex contour that matches the corresponding concave shape of the superior vertebrae. Implant 2000c is implanted at level 2033 with an upper endplate that is longitudinally adjacent to the superior vertebrae but has a non-adjacent focal defect. Implant 2000d has an upper endplate 2052 with a contour feature 2056 that generally corresponds to the focal defect to better fit the upper endplate. The localized defect in the patient's spine can range from a relatively small cavity (e.g., as shown at level 2033) to a relatively large valley (e.g., as shown at level 2034). Additionally, the localized defect can include protuberances (not shown) where excess bone and / or cartilage collect, requiring a concave contour feature on the implant's endplate to match them. Implant 2000e is implanted into level 2035, which has corner defects in the upper and lower vertebral bodies. The corner defects are located at least partially in the longitudinal direction of the vertebrae. The corner defects can include missing corners cut at various angles, protrusions (not shown) at the corners, and / or rough topology at the corners (e.g., on the missing corners, on the protrusions, and / or on the otherwise normal surface of the corners). Implant 2000e has an upper endplate 2052 with a peripheral contour 2058 configured to fit the corner defect of the upper endplate and a lower endplate 2053 with a peripheral contour 2060 configured to fit the corner defect of the lower endplate. Other adjacent levels, such as level 2036, can be formed by endplates with relatively smooth, planar or linear topologies. In such an embodiment, implant 2000f with a relatively smooth contour can be implanted into level 2036.

[0179] An implant 2000g is implanted at a level 2037 having an upper vertebra with an erosive defect on the lower surface of the upper vertebra. An external device 2049 can command the implant 2000g to move to a target location. As shown, the erosive defect is spread across the entire surface of the vertebra and can include multiple valleys and peaks therein. In some patients, the erosive defect can be included in focal and / or angular regions of the surface. In some patients, the erosive defect can include one or more deep valleys and / or one or more high peaks. As shown, the implant 2000g can have an upper endplate 2052 configured to mate with the erosive defect of the upper vertebra.

[0180] 16B illustrates a patient-specific surgical plan 2100 (e.g., generated in step 515 of method 500) that can be used and / or generated in connection with the methods described herein, according to one embodiment. The correction plan 2100 can be an adjustable implant correction plan that incorporates all or a portion of the surgical plans or other plans disclosed herein. The correction plan 2100 can include, without limitation, intra-operative and / or pre-operative patient metrics (e.g., pre-operative patient metrics 1002 discussed in connection with FIGS. 10-13), post-operative predicted patient metrics (e.g., post-operative predicted patient metrics 1004 discussed in connection with FIGS. 10-13), and adjustment metrics 2110.

[0181] The adjustment metrics 2110 can include any number of planned adjustments to the adjustable spinal implant. The illustrated correction plan 2100 includes planned adjustments 2120a, 2120b, 2120c (collectively "adjustments 2120"). Each adjustment 2120 can include associated post-adjustment metrics that can be reviewed by the physician. For example, the physician can review and approve these metrics by selecting an approve button. The computer system can then design the adjustable implant based on the approved adjustments (e.g., design the adjustable implant to have an adjustable range of motion that can accommodate the approved adjustments). If the physician wants to modify the adjustment, the physician can select a modify button. The physician can then input one or more parameters or metrics for the adjustment. The computer system can update the spine model according to the input parameters or metrics. Arrows (e.g., arrows 2130a, 2130b, 2130c) can indicate adjustments such as range of motion, adjustment values, etc. Adjustments 2 and 3 include adjustment indicators (shown as arrows) that indicate planned adjustments, such as the adjustments discussed in connection with Figures 17A-17D. The physician can approve / select individual target intra-operative and / or post-operative configurations for different loading conditions.

[0182] 17A-17D show orientations of the patient's spine that result in different implant loading outcomes. FIG. 17A shows the patient's spine in a generally horizontal orientation. For example, the implant can be implanted when the patient's body is generally horizontal so that the spine is generally unloaded. During surgery, it can be difficult to determine how the spine's loading will compare to the predicted loading. Thus, the implant can be reconfigured post-operatively to move the spine to a post-operative target position. Although FIGS. 17B-17D show adjustments when the post-operative patient's spine is in a vertical orientation (e.g., sitting or standing), post-operative adjustments can also be made when the patient is in other orientations. This allows for post-operative adjustments based on post-operative loading, dynamic visualization, etc.

[0183] In spinal fusion procedures, the implants may be adjusted in position for fixation to the vertebrae shortly after surgery (e.g., hours, days, etc.). In spinal alignment procedures, the implants may adjust the discs periodically to compensate for patient improvement, disease progression, etc. For example, adjustments 1-3 of Figs. 17B-17D may be performed monthly, yearly, or at physician-determined intervals. The number of adjustment sessions, the time period between adjustment sessions, and changes to the spine may be selected based on the treatment plan, patient recovery, etc. For example, the system of Fig. 1 and the computer device of Fig. 2 may be used to generate correction plans and adjustment plans. For example, the computer system may be used to determine a corrected anatomical configuration of the patient to achieve a target treatment outcome. The computer system may use at least one machine learning model to predict disease progression of a disease affecting the patient's spine based on the patient's patient data set. The computer system may identify an operable implant configured to be implanted in the patient to achieve the corrected anatomical configuration. The operable implant is movable between multiple configurations to compensate for predicted disease progression based on the target treatment outcome. The at least one machine learning model can determine whether to reconfigure the at least one device based on the post-adjustment images. The post-adjustment images can include dynamic sit / stand x-ray images, and in some adjustment procedures, the spine can be visualized (e.g., using fluoroscopy) while invasively or non-invasively actuating the actuable implant.

[0184] In some embodiments, one or more anatomical corrections for the patient are generated based on the pre-adjustment images and a patient-specific pre-operative correction plan. The computer system can generate a series of corrected anatomical models representing anatomical changes over a period of time based on the patient-specific corrections to the native anatomy and the predicted disease progression. The corrected anatomical models can be viewed and modified by the user as part of the pre-operative correction plan. The pre-operative correction plan can be generated by comparing the patient data set to a number of reference patient data sets to identify one or more similar patient data sets in the number of reference patient data sets, each similar patient data set corresponding to a reference patient that (a) has spinal pathology data similar to the patient and / or (b) has been treated with a post-operatively adjustable orthopedic implant. In some embodiments, a virtual model of the spine is generated. The virtual model is used to predict disease progression. An operable implant can be designed to fit the virtual model throughout the predicted disease progression. The simulation can be modified and re-run based on the post-operative adjustments (see FIGS. 17A-17D). Additional implants configured to cooperate with the operable implant can be designed to achieve a target treatment outcome and configured for multi-level adjustments. The plans disclosed herein provide results from a simulation of the multi-level adjustments (e.g., analysis of each level, overall spinal correction score, etc.).

[0185] FIG. 18 illustrates an implant system 2400 including implants 2200a, 2200b (also referred to herein as “patient devices” 2200a, 2200b) that can be selectively and independently non-invasively reconfigured. The computer system can receive a patient dataset for the patient and compare the patient dataset to a plurality of reference patient datasets to identify one or more similar patient datasets within the plurality of reference patient datasets. A subset of the one or more similar patient datasets can be selected such that each similar patient dataset of the selected subset includes data indicative of a favorable treatment outcome. The system can identify, for at least one similar patient dataset of the selected subset, surgical procedure data and medical device design data and implant adjustment data associated with a favorable treatment outcome. Based on the surgical procedure data and medical device design data of the medical device design data and implant adjustment data, the computer system generates at least one patient-specific surgical procedure and at least one patient-specific medical device design for the patient. The at least one patient-specific medical device design is configured to be non-invasively actuated using an internal actuator 2450 (shown in phantom).

[0186] In some embodiments, the series of corrected anatomical models represent predicted anatomical changes over a period of time based on patient-specific corrections to the patient's native anatomy and predicted disease progression. Multiple treatment locations can be identified along the patient's spine. An implant 2200a, 2200b can be designed for each treatment site based on the patient-specific corrections and to correct the anatomical changes with post-operative adjustment of the implant. The physician can review the corrected anatomical models and modify / approve the corrected anatomical models as described above.

[0187] The implants 2200a, 2200b may be configured to measure, but are not limited to, lumbar lordosis, Cobb angle, coronal parameters (e.g., coronal balance, global coronal balance, coronal pelvic tilt, etc.), sagittal parameters (pelvic incidence, sacral tilt, thoracic lordosis, etc.), pelvic parameters, or combinations thereof. The implant system 2400 may store algorithms that use the collected data (e.g., sensor readings, device settings / configuration data, etc.) as inputs used to determine, for example, a pathology (e.g., disc height, segment flexibility, bone quality, rotational displacement), and the algorithms may use these additional inputs to further define an optimal implant configuration for the current pathology. To treat multiple diseases / pathologies, the implant system 2400 may predict the outcome of one or more diseases / pathologies based on the adjustments. The predicted results may then be ranked, categorized, weighted, tabulated, etc. to assess the patient's condition / pathology and determine whether to adjust the implant configuration, adjustment protocol, or patient monitoring plan, etc. This allows conditions / diseases to be ranked and prioritized. In some embodiments, the implant can collect data to track fatigue or useful life. In some embodiments, the implant system 2400 uses sensor readings for diagnostics.

[0188] The patient devices 2200a, 2200b can be moved simultaneously or sequentially to provide the target correction. Additional sensor data can be collected during this process to monitor the adjustment. If the applied load reaches a maximum allowable value, the patient devices 2200a, 2200b can slow down the actuator speed or determine an alternative target configuration. The implant system 2400 then determines one or more configurations of the patient devices 2200a, 2200b to provide the target anatomical configuration or correction. In some embodiments, the implant system 2400 can generate the target anatomical configuration or correction based on the sensor readings. If the patient devices 2200a, 2200b detect values ​​(e.g., strain, load, force, pressure, etc.) outside of the allowable range, the patient devices 2200a, 2200b move to another configuration to keep the detected value within range, thereby limiting or avoiding an undesirable state or condition.

[0189] If the threshold correction is not achieved, a notification can be sent to a remote device to alert the patient and / or healthcare provider. Additional treatment or surgery can then be performed. In some embodiments, the patient devices 2200a, 2200b are programmed with one or more target anatomical configurations of the patient, such as spinal curvature, vertebral spacing, etc. The patient devices 2200a, 2200b can communicate with each other to monitor the patient's current anatomical configuration. If the anatomical configuration is outside of an acceptable range or above / below a predetermined value, the patient devices 2200, 2200b can automatically move the anatomical features (e.g., vertebral bodies) to an acceptable configuration for height restoration, angle correction (e.g., lordotic angle correction, coronal angle correction, etc.), etc.

[0190] The difference between the native anatomical configuration and the corrected anatomical configuration may be referred to as a "patient-specific correction" or a "target correction." The embodiments, features, systems, devices, materials, methods, and techniques described herein may be used in certain embodiments, in conjunction with or in any one or more of the embodiments, features, systems, or devices. In some embodiments, mathematical rules that define the patient-specific correction, optimal anatomical outcome (e.g., positional relationships between anatomical elements) and / or post-operative metrics / design criteria (e.g., adjusted anatomical elements) are used such that the post-operative metrics are within acceptable ranges for the patient's condition (e.g., lying down, standing vertically, etc.). The target post-operative metrics may include, but are not limited to, target coronal parameters, target sagittal parameters, target pelvic incidence angle, target Cobb angle, target shoulder slope angle, target ilio-lumbar angle, target coronal balance, target lordosis angle, and / or target intervertebral space height. For example, the sagittal axis when the patient is standing vertically may be less than a set value (e.g., 6 mm, 7 mm, 9 mm, etc.), and the postoperative Cobb angle may be less than a set value (e.g., 8 degrees, 9 degrees, 10 degrees, etc.).

[0191] The implanted patient device 2200a can be an artificial disc having sensors 2438a, 2438b (collectively "sensors 438") configured to measure pressure, load or force applied by a first vertebra 410 (e.g., a relatively superior vertebra) and a second vertebra 2420 (e.g., a relatively inferior vertebra). The patient device 2200a can transmit sensor readings from the sensors 2438a, 2438b to the patient device 2200b, which is illustrated as an expandable rod assembly having sensors (e.g., force sensors, pressure sensors, etc.) and a controller 2469. The patient devices 2200a, 2200b can receive signals transmitted from a controller located external to the patient and in response can move to another configuration based on the received signals. In other embodiments, the patient device 2200a generates and transmits commands to the patient device 2200b, or vice versa. Both patient devices 2200a and 2200b can expand to increase the height of the intervertebral space 2410, for example, to reduce or eliminate neural compression, while maintaining a target characteristic of the spine (e.g., curvature, alignment, etc.). The amount of expansion provided by each device can be substantially the same (e.g., to maintain the current spinal curvature along that section of the spine), or substantially different (e.g., to change the spinal curvature along that section of the spine), etc. In some embodiments, the characteristics of patient device 2200a are controlled based on sensor readings. Such characteristics include compressibility, range of motion, etc.

[0192] The implanted patient device 2200 may have modules or programs incorporating one or more mathematical rules based on ranges or thresholds of various disease metrics. For example, an intervention timing module may indicate that surgical intervention is necessary if one or more disease metrics exceed a predetermined threshold or meet other criteria. Exemplary thresholds indicating that an adjustment is necessary include a combination of SVA values ​​greater than 7 mm, lumbar lordosis and pelvic incidence mismatch greater than 10 degrees, Cobb angle greater than 10 degrees, and / or Cobb angle and LL / PI mismatch greater than 20 degrees. In some embodiments, the indication for adjustment may be based on a percentage change over a period of time. For example, if the SVA value increases by 20% over a period of time, the patient device 200 may be reconfigured to lower the SVA value to within 10% of the target SVA value. Other thresholds and metrics may be used, and the above are provided by way of example only and are not intended to limit the present disclosure. In some embodiments, the above rules may be tailored to a particular patient population (e.g., men over 50 years old or women over 40 years old, etc.). If a particular patient does not exceed a threshold indicating that an adjustment is recommended, the implanted patient device 2200a, 2200b can provide an estimate of when the patient's metrics will exceed one or more thresholds, thereby providing the patient with an estimate of when an adjustment will become recommended. The estimate can be transmitted to an external device for viewing.

[0193] The present technology can also include a treatment planning module that can identify the optimal type of modification based on the patient's disease progression. The treatment planning module can be an algorithm, machine learning model, or other software analysis tool that is trained based on multiple reference patient data sets, as described above, or other software analysis tool. The treatment planning module can also incorporate one or more mathematical rules to identify adjustments to counter or slow disease progression. As a non-limiting example, if the LL / PI mismatch is between 5 degrees and 10 degrees, the treatment planning module can recommend adjustments to reduce the LL / PI mismatch to less than 5 degrees.

[0194] The patient device 2200 can include a patient-specific endplate 2415 and a body 2417. The endplates can be configured to engage the vertebral bodies 2410 and 2420, respectively. The body 2417 can include electronic devices (e.g., processors, storage devices, communication devices, etc.), sensors 2438a, 2438b, and actuator devices 2450. In some embodiments, the actuator devices are fluid powered and the sensors 2438a, 2438b are pressure sensors.

[0195] In some embodiments, implant 2000a and / or implant 2000b may also be used in conjunction with non-patient specific devices and implants. The systems disclosed herein may include any number of implants designed to be implanted at different locations to provide specific treatments.

[0196] In some procedures, the patient device 2200a can be in a folded or low profile configuration between the first and second vertebrae 2410, 2420. For example, the device 2200a can be manually inserted in the folded configuration by surgical navigation or via a surgical robot and then deployed at the implant site. Once deployed / expanded, the device 2200a can provide one or more adjustments, corrections (e.g., corrections to the alignment of the first and second vertebrae 2410, 2420, segments, etc.), etc., as described in more detail below. FIG. 18 shows the implant 2200a fully expanded along a vertical axis, indicated by arrows 2437, 2439, to hold the first and second vertebrae 2410, 2420 apart. Once the implant 2200a is implanted, the patient can lie generally horizontally so that the spine is generally off-load. After surgery, the patient can stand upright or perform one or more tasks while the sensors 2438a, 2438b continuously or periodically collect data for adjustment of the patient device 2200a. An external device, such as a controller or other computer system, can control the data collection, including sampling rate, detection schedule, etc. The system 2400 can transmit data to a remote device or network. The remote device or network can determine one or more settings (e.g., height, stiffness, endplate position, etc.) based on the received data. The settings are transmitted to the implants 2200a, 2200b so that the implants move to their respective new target configurations. Thus, the implants 2200a, 2200b can also be referred to as "networked" implants. This process can be performed any number of times (e.g., monthly, yearly, etc.) for treatment adjustment functions.

[0197] The implants 2200a, 2200b can be programmed to detect adverse events such as excessive loading, displacement (e.g., lateral migration), or the like. The implants 2200a, 2200b can automatically control adjustments to compensate for adverse events. For example, if the posterior loading exceeds a threshold, the implant 2200a can command the fixation rod implant 2200b to lengthen to reduce the posterior loading of the implant 2200a. The number, configuration, and capabilities of the patient devices 2200a, 2200b can be selected based on the treatment. For example, an intervertebral device (cage, artificial disc, etc.) can be at each level for treatment.

[0198] The networked systems and devices disclosed herein may include a data storage element for storing patient-specific data, a search function for accessing the patient-specific data, or the like. A data storage module having a memory for storing data, and a search module configured to transmit the patient-specific surgical plan from the data storage module to a surgical platform may be configured to execute one or more aspects of the patient-specific surgical plan. Thus, the patient-specific data is linked to the patient-specific implant. The data may be accessed after the implant is implanted. The data may be used to verify aspects of the implant / surgery (e.g., is the implant correctly placed) and may be combined, aggregated, and analyzed with post-implant data (e.g., implant status data, configuration data, sensor data, etc.). U.S. Patent Application No. 16 / 990,810 discloses features, systems, devices, materials, and methods that may be incorporated into or used in conjunction with the networked systems and devices disclosed herein. U.S. Patent Application No. 16 / 990,810 is incorporated herein by reference in its entirety.

[0199] In some embodiments, the technology can also predict, model, and / or simulate disease progression. For example, the disease progression module can simulate how a patient's anatomy will look one, two, five, or ten years after surgery for several surgical intervention options. The simulations can also incorporate non-surgical factors such as the patient's age, height, weight, sex, activity level, other health conditions, or the like, as previously described. Based on these simulations, the system and / or surgeon can select which surgical intervention is most suitable for long-term effectiveness. These simulations can also be used to determine patient-specific corrections to correct protruding disease. The networked systems and devices can generate data for monitoring and predicting disease progression. In some embodiments, one or more of the implantable devices include a disease progression module for local analysis of data. In other embodiments, a remote computing device can include a disease progression module. As the implanted network system corrects, the disease progression module can continuously or periodically predict disease progression.

[0200] The systems disclosed herein may also include multiple disease progression models (e.g., 2, 3, 4, 5, 6, or more) that are simulated to provide disease progression data for multiple different surgical intervention options or other scenarios. For example, the disease progression module may generate a model predicting postoperative disease progression for each of three different surgical interventions. A surgeon or other healthcare provider may review the disease progression models and, based on the review, select the one of the three surgical intervention options that is likely to provide the best long-term outcome for the patient. Of course, the selection of the optimal adjustment may also be fully or semi-automated, as described herein. The implanted network system may be programmed with multiple disease progression models. The disease progression models may be modified based on collected data, healthcare providers, etc.

[0201] In some embodiments, the networked implants can be used to correct many different conditions in a variety of settings, including spinal surgery, hand surgery, shoulder and elbow surgery, total joint reconstruction (arthroplasty), skull reconstruction, pediatric orthopedics, foot and ankle surgery, musculoskeletal oncology, surgical sports medicine, or orthopedic trauma. The spinal implants can dynamically correct irregular spinal curvatures such as scoliosis, lordosis, kyphosis (hyper or hypo), and irregular spinal displacements (such as spondylolisthesis). In this way, the correction can be changed over time (e.g., the device is implanted when the patient is not fully grown) to correct disease progression and patient growth. The networked devices can be designed to treat osteoarthritis, lumbar or cervical degenerative discopathy, lumbar or cervical spinal stenosis.

[0202] As will be appreciated by those skilled in the art, any of the software functions described above may be combined or distributed into one or more software functions or devices for performing the operations described herein. Thus, any of the operations described herein may be performed by any of the computing devices or systems described herein, unless expressly stated otherwise.

[0203] As will be appreciated by those skilled in the art, any of the software modules described above may be combined into a single software module for performing the operations described herein. Similarly, the software modules may be distributed across any combination of computer systems and devices described herein and are not limited to the explicit arrangements described herein. Thus, any of the operations described herein may be performed by any of the computer devices or systems described herein, unless expressly stated otherwise. EXAMPLES

[0204] The technology of the present invention is exemplified according to various aspects, for example, as described below. Various embodiments of the aspects of the technology of the present invention are described as numbered embodiments (1, 2, 3, etc.) for convenience. These are provided as examples and not as limitations of the technology of the present invention. It should be noted that any of the dependent embodiments can be combined in any suitable manner to form their own independent embodiments. Other embodiments can be presented in a similar manner. 1. A method of treating a patient, comprising: receiving, by a computer system, implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration according to an adjustable implant correction plan for the patient, the implant sensor readings indicative of loads applied to the spinal implant by the patient's spine; extracting, by the computer system, a feature vector from the implant sensor readings using a machine learning module of the computer system, the feature vector indicative of a target correction according to the adjustable implant correction plan; generating, by the computer system, an implant electrical signal using the machine learning module and based on the feature vector, the machine learning module being trained based on a patient data set to generate the implant electrical signal that adjusts the load to achieve the target correction; transmitting, by the computer system, the implant electrical signal to the spinal implant to move the spinal implant to a second physical configuration for the targeted correction; A method comprising: 2. receiving, by the computer system, patient data; determining, by the computer system, a spinal anatomical configuration of the patient based on the received patient data; identifying, by the computer system, the target correction based on the anatomical configuration and available adjustment capabilities of the spinal implant, the identified target correction being used to extract the feature vector; The method of example 1 further comprising: 3. The correction plan includes criteria for activating the spinal implant; The method according to Example 1 or 2. 4. receiving, by the computer system, device sensor readings from one or more device sensors embedded in an intervertebral fusion device implanted in the patient, the device sensor readings being received before the implant sensor readings are received from the spinal implant; generating, by the computer system, a device electrical signal using the machine learning module and based on the device sensor readings, the device electrical signal including instructions for adjusting a configuration of the device; Further comprising: The method according to any one of Examples 1 to 3. 5. The feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine. The method according to any one of Examples 1 to 4. 6. Configuring the spinal implant in the second physical configuration includes: using one or more implant actuators to adjust at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant; The method according to any one of Examples 1 to 5. 7. Configuring the spinal implant in the second physical configuration includes: adjusting a reservoir coupled to the spinal implant to vary the amount of at least one of a pharmacological, biological, biochemical, anesthetic, or steroid delivered to the patient. The method according to any one of Examples 1 to 5. 8. A non-transitory computer-readable storage medium having computer instructions stored thereon, comprising: The computer instructions, when executed by one or more computer processors, cause the one or more computer processors to: receiving implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration according to a correction plan for the patient, the implant sensor readings indicative of loads applied to the spinal implant by the patient's spine; extracting a feature vector from the implant sensor readings using a machine learning module, the feature vector indicative of a targeted correction according to a correction plan; generating an implant electrical signal using the machine learning module and based on the feature vector, the machine learning module being trained based on a patient data set to generate the implant electrical signal that adjusts the load to achieve the target correction; transmitting the implant electrical signal to the spinal implant to move the spinal implant to a second physical configuration for the targeted correction; A non-transitory computer-readable storage medium that causes 9. The computer instructions further cause the one or more computer processors to: receiving device sensor readings from one or more device sensors of an intervertebral fusion device implanted in the patient, the device sensor readings being received before the implant sensor readings are received from the spinal implant; generating a device electrical signal using a machine learning module and based on the device sensor readings, the device electrical signal including instructions for adjusting a configuration of the device; To carry out 9. The non-transitory computer readable storage medium of Example 8. 10. The feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine. The non-transitory computer-readable storage medium of Example 8 or 9. 11. Configuring the spinal implant in the second physical configuration comprises: using one or more implant actuators to adjust at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffolding, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant; A non-transitory computer-readable storage medium according to any one of Examples 8 to 10. 12. Configuring the spinal implant in the second physical configuration comprises: using one or more implant actuators to adjust at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant; A non-transitory computer-readable storage medium according to any one of Examples 8 to 10. 13. A system comprising: one or more computer processors; a non-transitory computer readable storage medium having computer instructions stored thereon; Equipped with The computer instructions, when executed by the one or more computer processors, cause the one or more computer processors to: receiving implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration, the implant sensor readings indicative of a load applied to the spinal implant by a spine of the patient; extracting a feature vector from the implant sensor readings using a machine learning module of the system, the feature vector indicative of a targeted correction according to a correction plan; generating an implant electrical signal using the machine learning module and based on the feature vector, the machine learning module being trained based on a patient data set to generate an implant electrical signal that adjusts a load to achieve a target correction; transmitting the implant electrical signal to the spinal implant to move the spinal implant to a second physical configuration for the targeted correction; A system that allows the user to: 14. The computer instructions further cause the one or more computer processors to: receiving device sensor readings from one or more device sensors embedded in an intervertebral fusion device implanted in the patient, the device sensor readings being received before the implant sensor readings are received from the spinal implant; generating a device electrical signal using a machine learning module and based on the device sensor readings, the device electrical signal including instructions for adjusting a configuration of the device; To carry out The system described in Example 13. 15. The feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine. The system according to example 13 or 14. 16. Configuring the spinal implant in the second physical configuration comprises: using one or more implant actuators to adjust at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant; A system according to any one of Examples 13 to 15. 17. Configuring the spinal implant in the second physical configuration comprises: adjusting a reservoir coupled to the spinal implant to vary the amount of at least one of a pharmacological, biological, biochemical, anesthetic, or steroid delivered to the patient. A system according to any one of Examples 13 to 15. 18. A computer-implemented method for treating a spine, comprising: determining, by a computer system, a corrected anatomical configuration of the patient to achieve a target treatment outcome; predicting, by the computer system, a disease progression of a disease affecting the spine of the patient based on a patient dataset of the patient using at least one machine learning model; identifying, by the computer system, an operable implant configured to be implanted in the patient to achieve the corrected anatomical configuration, the operable implant being movable between a plurality of configurations after implantation to compensate for the predicted disease progression based on the targeted treatment outcome; 4. A computer-implemented method comprising: 19. The method further comprises the step of designing, by the computer system, one or more additional implants configured to cooperate with the operable implant to achieve the target therapeutic outcome. The computer-implemented method of Example 18. 20. Generating, by the computer system, a virtual model of the spine; simulating, by the computer system, the predicted disease progression using the virtual model; designing, by the computer system, the operable implant to fit the virtual model throughout the predicted disease progression; Further comprising: 20. The computer-implemented method of Example 18 or 19. 21. simulating, by said computer system, said predicted disease progression and adjustments of said operable implant for viewing by a physician; receiving, by the computer system, physician input for the simulation; simulating, by the computer system, at least one treatment outcome for the patient based on the received physician input, the predicted disease progression, and one or more adjustments to the operable implant; Further comprising: A computer-implemented method according to any one of Examples 18 to 20. 22. The target therapeutic outcome includes a range of acceptable spinal parameters, and adjustability of the actuatable implant is selected to achieve the target therapeutic outcome over a planned useful life. A computer-implemented method according to any one of Examples 18 to 21. 23. Selecting, by the computer system, at least one matching prior patient from one or more similar prior patients; obtaining, by said computer system, disease progression data of said at least one matched prior patient; determining, by the computer system, a patient-specific implant adjustment plan to compensate for the disease progression based on the acquired disease progression data; Further comprising: A computer-implemented method according to any one of Examples 18 to 22. 24. Generating, by the computer system, a plurality of disease progression and implant scenarios; displaying, by the computer system, the disease progression scenario and the implant scenario; receiving, by the computer system, a selection of one or more of the disease progression scenarios for determining a minimal adjustment capability of the operable implant; Further comprising: A computer-implemented method according to any one of Examples 18 to 23. 25. At least one of the disease progression scenario and the implant scenario is: based on at least one of a predicted rate of progression of the disease, a patient health score, or a planned duration of treatment; 25. The computer-implemented method of Example 24. 26. The predicted progression rate is determined based on one or more reference patient data sets. A computer-implemented method according to any one of Examples 18 to 25. 27. A computer-implemented method for providing patient-specific medical care, comprising: receiving, by a computer system, a patient dataset for a patient; comparing, by the computer system, the patient dataset with a plurality of reference patient datasets to identify one or more similar patient datasets in the plurality of reference patient datasets; selecting, by the computer system, a subset of the one or more similar patient data sets, each similar patient data set of the selected subset exhibiting data indicative of a favorable treatment outcome; identifying, by the computer system, for at least one similar patient data set of the selected subset, medical device design data and implant adjustment data associated with the favorable treatment outcome; generating, by the computer system, at least one patient-specific medical device design for the patient based on the medical device design data and the implant adjustment data, the at least one patient-specific medical device design configured to be non-invasively actuated to adjust a configuration of the medical device post-operatively; 4. A computer-implemented method comprising: 28. The above comparison step: generating, by the computer system, for each reference patient data set, a similarity score based on a comparison of the spinal pathology data of the patient data set to the spinal pathology data of the reference patient data set, the similarity score being based at least in part on whether an adjustable implant was used; identifying, by the computer system, the one or more similar patient data sets based at least in part on the similarity score; Including, The computer-implemented method of Example 27. 29. At least one of the similar patient data sets corresponds to a reference patient who (a) has similar spinal pathology data as the patient, and / or (b) has been treated with a respective orthopedic implant using at least one actuator; at least one of the similar patient data sets of the selected subset includes data indicating that treatment with the respective orthopedic implant received by the reference patient resulted in a favorable treatment outcome; The computer-implemented method further includes determining, by the computer system, parameters for scaling up or scaling down the at least one patient-specific medical device design based on the selected subset. 29. The computer-implemented method of Example 27 or 28. 30. The above comparison step: comparing, by the computer system, the patient data set and the reference patient data set; generating, by the computer system, for each reference patient data set, a similarity score based on a comparison of the patient data set with the respective reference patient data set; identifying, by the computer system, the one or more similar patient data sets based at least in part on the similarity score and whether the patient received a post-operatively operable implant; Including, The computer-implemented method of Example 27. 31. The similarity score represents a statistical correlation between the patient dataset and each of the reference patient datasets. 31. The computer-implemented method of Example 30. 32. A computer-implemented method for designing a patient-specific orthopedic implant, comprising: comparing, by a computer system, the patient dataset to a plurality of reference patient datasets to identify one or more similar patient datasets in the plurality of reference patient datasets, each similar patient dataset corresponding to a reference patient that (a) has similar spinal pathology data as the patient, and (b) has been treated with a post-operatively operable implant; identifying, by the computer system, for at least one similar patient data, design data for each implant and surgical actuator data for implanting each of the implants in a corresponding reference patient; generating, by the computer system, a design of the operable orthopaedic implant for the patient's anatomy based on the design data and adjustment data such that operation of the operable orthopaedic implant is remotely controlled by an external controller; 4. A computer-implemented method comprising: 33. The method further includes the step of selecting, by the computer system, a subset of the one or more similar patient data sets used to identify the design data, wherein each similar patient data set of the selected subset includes data indicating that one or more adjustments to the implant received by the reference patient resulted in a favorable treatment outcome. The computer-implemented method of Example 32. 34. A computer system uses a trained machine learning model to: determining a plurality of implant adjustment plans for a period of time and a corresponding plurality of orthopaedic implant designs for treating the patient; determining, for each of the plurality of implant adjustment plans and each of the corresponding plurality of orthopaedic implant designs, a probability of achieving a target treatment outcome for the patient in the time period; selecting at least one of the plurality of implant adjustment plans and at least one of the corresponding plurality of orthopedic implant designs based at least in part on the determined probability of achieving the target treatment outcome in the time period. The method further comprises the step of: 34. The computer-implemented method of Example 32 or 33. 35. A computer-implemented method comprising: generating, with a computer system, an anatomical model of at least a portion of a patient, the anatomical model describing a native anatomy of the patient; generating, with the computer system, a series of corrected anatomical models representing anatomical changes over a period of time based on patient-specific corrections to the native anatomy and predicted disease progression; determining, with the computer system, a plurality of treatment locations along the patient's spine; designing, by said computer system, an implant for each treatment location based on said patient-specific correction to compensate for said anatomical changes by post-operative actuation of said implant; 4. A computer-implemented method comprising: 36. The implant is configured such that, when the implant is implanted in the plurality of treatment locations, the portion of the patient substantially conforms to the corrected anatomical model. The computer-implemented method of Example 35. 37. The anatomical model is a virtual model of at least a portion of the spine. 37. The computer-implemented method of Example 35 or 36. 38. The method further includes comparing, by the computer system, the anatomical model with a corrected anatomical model to determine the plurality of treatment locations. 38. A computer-implemented method according to any one of Examples 35 to 37. 39. A computer-implemented method for non-invasive anatomical adjustment, comprising: acquiring, by a computer system, a pre-aligned image of the patient's spine in a vertical position to load at least one device implanted along the patient's spine; determining, by the computer system, one or more anatomical corrections for the patient based on the pre-adjustment images and a patient-specific pre-operative correction plan; actuating, by the computer system, non-invasively the at least one device from a first configuration to a second configuration to move the vertebrae toward a target anatomical configuration of the patient-specific pre-operative correction plan to provide one or more anatomical corrections; acquiring an adjusted image of the patient with the at least one device in the second configuration; determining whether to reconfigure the at least one device based on the adjusted image; 4. A computer-implemented method comprising: 40. The pre-adjusted image includes at least one of a standing X-ray image or a sitting X-ray image; The adjusted image includes at least one of a standing X-ray image and a sitting X-ray image. The computer-implemented method of Example 39. 41. The step of acquiring the pre-conditioning images includes imaging the spine to generate dynamic sit / stand images while actuating the at least one device. 41. The computer-implemented method of Example 39 or 40. 42. The at least one device includes a plurality of interbody fusion devices, each implanted at a different level in the spine; and non-invasive actuation of the at least one device includes reconfiguring the interbody device to move the patient's post-operative spine to the target anatomical location for creating spinal fusion. 42. A computer-implemented method according to any one of Examples 39 to 41. 43. Obtaining pre-operative images of the patient; determining a post-operative adjustment function of the at least one device based on the patient-specific correction plan; designing said at least one device with said post-operative adjustment capability; Further including: A computer-implemented method according to any one of Examples 39 to 42.

[0205] conclusion The above detailed description describes various embodiments of devices and / or processes through the use of block diagrams, flow charts, and / or examples. To the extent that such block diagrams, flow charts, and / or examples include one or more functions and / or operations, those skilled in the art will appreciate that each function and / or operation within such block diagrams, flow charts, or examples can be individually and / or collectively implemented by a wide range of hardware, software, firmware, or substantially any combination thereof. In some embodiments, some portions of the subject matter described herein can be implemented via an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or other integrated form. However, it will be within the skill of one of ordinary skill in the art in light of this disclosure to design circuitry and / or write software and / or firmware code such that certain aspects of the embodiments disclosed herein, in whole or in part, are implemented in an integrated circuit, 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 as virtually any combination thereof. Moreover, one of ordinary skill in the art will appreciate that the mechanisms of the subject matter described herein can be distributed as a program product in a variety of forms, and that the 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.).

[0206] Those skilled in the art will recognize that it is common in the art to describe devices and / or processes in the manner set forth herein and then use engineering techniques to integrate such described devices and / or processes into a data processing system. 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 one or more of 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, computing entities such as an operating system, drivers, graphical user interfaces, 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 as typically found in data computing / communication and / or network computing / communication systems.

[0207] The subject matter described herein shows different components that are sometimes included within or connected to different other components. It should be understood that such depicted architectures are merely examples, and that in fact many other architectures that achieve the same functionality can be implemented. In a conceptual sense, an arrangement of components to achieve the same functionality is substantially "associated" such that the desired functionality is achieved. Thus, in this specification, any two components that combine to achieve a particular functionality can be considered to be "associated" with each other such that the desired functionality is achieved, regardless of the architecture or intermediate components. Similarly, any two components so associated can also be considered to be "operably connected" or "operably coupled" with each other to achieve the desired functionality, and any two components that can be so associated can also be considered to be "operably coupled" with each other to achieve the desired functionality. Examples of operably coupleable include, but are not limited to, components that can be physically mated and / or physically interactable, and / or components that can be wirelessly interacted and / or wirelessly interactable, and / or components that can be logically interacted and / or logically interactable.

[0208] The embodiments, features, systems, devices, materials, methods, and techniques described in this specification can, in some embodiments, be similar to any one or more of the embodiments, features, systems, devices, materials, methods, and techniques described below. U.S. Patent Application No. 16 / 048,167, filed July 27, 2017, 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 for Assisting Surgeons with Screw Placement During Spinal Surgery" U.S. Patent Application No. 16 / 207,116, filed December 1, 2018, entitled "SYSTEMS AND METHODS FOR MULTI-PLANE 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 Sep. 12, 2019, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS" U.S. Patent Application No. 62 / 773,127, filed November 29, 2018, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS" U.S. Patent Application No. 62 / 928,909, filed October 31, 2019, entitled "SYSTEMS AND METHODS FOR DESIGNING ORTHOPEDIC IMPLANTS BASED ON TISSUE PROPERTIES" U.S. Patent Application No. 16 / 735,222, filed January 6, 2020, entitled "Patient-Specific Medical Procedures and Devices, and Related Systems and Methods" U.S. Patent Application No. 16 / 987,113, filed on August 6, 2020, entitled “Patient-Specific Artificial Discs and Related Systems and Methods” U.S. Patent Application No. 16 / 990,810, filed Aug. 11, 2020, entitled “Linking Patient-Specific Medical Devices with Patient-Specific Data, and Related Systems and Methods” U.S. Patent Application No. 17 / 463,054, filed on August 31, 2021, entitled "Blockchain-Managed Medical Implant" U.S. Patent Application No. 17 / 085,564, filed October 30, 2020, entitled "SYSTEMS AND METHODS FOR DESIGNING ORTHOPEDIC IMPLANTS BASED ON TISSUE PROPERTIES" and U.S. Patent Application No. 17 / 100,396, filed November 20, 2020, entitled “Patient-Specific Vertebral Implant With Positioning Features.”

[0209] All of the above identified patents and applications are incorporated by reference in their entirety. In addition, the embodiments, features, systems, devices, materials, methods, and techniques described herein may be applied to or used in connection with any one or more of the embodiments, features, systems, devices, or other items in particular embodiments.

[0210] Ranges disclosed herein also encompass any and all overlapping portions, subranges, and combinations thereof. Expressions such as "up to," "at least," "greater than," "less than," "between," or the like, include the recited number. Numbers preceded by terms such as "approximately," "about," and "substantially" as used herein include the recited number (e.g., about 10%=10%) and also represent an amount close to the recited amount that still performs a desired function or achieves a desired result. For example, the terms "approximately," "about," and "substantially" can refer to an amount that is within less than 10%, less than 5%, less than 1%, less than 0.1%, and less than 0.01% of the recited amount.

[0211] From the foregoing, it will be understood that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications may be made without departing from the scope and spirit of the present disclosure. Accordingly, the various embodiments disclosed herein are not intended to be limiting. [Explanation of symbols]

[0212] 1410 First vertebra 1420 Second vertebra 1430 Devices 1431 Main unit 1434 Upper Component 1435 Lower Component 1436 Drive Features 1438a, 1438b Lockable joints 1440 First End Plate 1450 2nd end plate 1453 Auxiliary Implants 1457 Rod 1459 Fasteners

Claims

1. A non-transitory computer-readable storage medium storing computer instructions, wherein when the computer instructions are executed by one or more computer processors, the one or more computer processors are caused to receive implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration according to the patient's correction plan, wherein the implant sensor readings indicate a load applied to the spinal implant by the patient's spine, the receiving step; extract a feature vector from the implant sensor readings using a machine learning module, wherein the feature vector indicates a target correction according to the correction plan, the extracting step; generate an implant electrical signal using the machine learning module and based on the feature vector, wherein the machine learning module is trained based on a patient dataset to generate the implant electrical signal for adjusting the load to achieve the target correction, the generating step; transmit the implant electrical signal to the spinal implant to move the spinal implant to a second physical configuration for the target correction; and a non-transitory computer-readable storage medium that causes the above to be performed.

2. The computer instructions further cause the one or more computer processors to receive device sensor readings from one or more device sensors of an intervertebral fixation device implant implanted in the patient, wherein the device sensor readings are received before the implant sensor readings are received from the spinal implant, the receiving step; generate a device electrical signal using the machine learning module and based on the device sensor readings, wherein the device electrical signal includes instructions for adjusting the configuration of the device, the generating step; and The non-transitory computer-readable storage medium according to claim 1, which causes the above to be performed.

3. The non - transitory computer - readable storage medium according to claim 1, wherein the feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, pelvic parameters, intervertebral disc height, segment flexibility, bone quality, or rotational displacement of the spine of the patient.

4. Configuring the spinal implant to the second physical configuration is using one or more implant actuators to adjust at least one of screws, cages, plates, rods, discs, spacers, expandable devices, stents, brackets, ties, skeletons, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, or joint replacements of the spinal implant, the non - transitory computer - readable storage medium according to claim 1.

5. Configuring the spinal implant to the second physical configuration is adjusting a reservoir coupled to the spinal implant to change the amount of at least one of pharmacological, biological, biochemical, anesthetic, or steroid delivered to the patient, the non - transitory computer - readable storage medium according to claim 1.

6. A system comprising one or more computer processors, and a non - transitory computer - readable storage medium storing computer instructions, wherein the computer instructions, when executed by the one or more computer processors, cause the one or more computer processors to receive implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration, wherein the implant sensor readings indicate the load applied to the spinal implant by the patient's spine, the receiving step; extract a feature vector from the implant sensor readings using a machine - learning module of the system, wherein the feature vector indicates a target correction according to a correction plan, the extracting step; A step of generating an implant electrical signal using the machine learning module and based on the feature vector, wherein the machine learning module is trained based on a patient dataset to generate the implant electrical signal for adjusting the load to achieve the target correction, the generating step; A step of transmitting the implant electrical signal to the spinal implant to move the spinal implant to a second physical configuration for the target correction; A system that causes the above to be performed.

7. The computer instructions further cause the one or more computer processors to Receive device sensor readings from one or more device sensors embedded in an intervertebral fixation device implant implanted in the patient, wherein the device sensor readings are received before the implant sensor readings are received from the spinal implant, the receiving step; A step of generating a device electrical signal using a machine learning module and based on the device sensor readings, wherein the device electrical signal includes instructions for adjusting the configuration of the device, the generating step; The system according to claim 6, which causes the above to be performed.

8. The system according to claim 6, wherein the feature vector further indicates at least one of lumbar lordosis, Cobb angle, coronal parameter, sagittal parameter, pelvic parameter, intervertebral disc height, segment flexibility, bone quality, or rotational displacement of the patient's spine.

9. Configuring the spinal implant to the second physical configuration The system according to claim 6, including adjusting at least one of a screw, cage, plate, rod, disc, spacer, expandable device, stent, bracket, tie, skeleton, fixation device, anchor, nut, bolt, rivet, connector, tether, fastener, or joint replacement of the spinal implant using one or more implant actuators.

10. Configuring the spinal implant to the second physical configuration The system according to claim 6, including adjusting a reservoir coupled to the spinal implant to change the amount of at least one of a pharmacological, biological, biochemical, anesthetic, or steroid delivered to the patient.

11. A computer-implemented method for treating the spine, comprising: determining, by a computer system, a corrected anatomical configuration of a patient to achieve a target treatment outcome; predicting, by the computer system, a disease progression of a disease affecting the spine of the patient based on a patient dataset of the patient using at least one machine learning model; identifying, by the computer system, an actuatable implant configured to be implanted in the patient to achieve the corrected anatomical configuration, the actuatable implant being movable between a plurality of configurations after implantation to compensate for the predicted disease progression based on the target treatment outcome; A computer-implemented method comprising the steps of: **Claim 12** The computer-implemented method according to claim 11, further comprising the step of designing, by the computer system, one or more additional implants configured to cooperate with the actuatable implant to achieve the target treatment outcome. **Claim 13** generating, by the computer system, a virtual model of the spine; simulating, by the computer system, the predicted disease progression using the virtual model; designing, by the computer system, the actuatable implant to conform to the virtual model over the predicted disease progression; The computer-implemented method according to claim 11, further comprising the steps of: **Claim 14** simulating, by the computer system, the predicted disease progression and adjustment of the actuatable implant for a physician to view; receiving, by the computer system, physician input for the simulation; simulating, by the computer system, at least one treatment outcome of the patient based on the received physician input, the predicted disease progression, and one or more adjustments of the actuatable implant; The computer-implemented method according to claim 11, further comprising the steps of: **Claim 15** The computer-implemented method according to claim 11, wherein the target treatment result includes a range of acceptable spinal parameters, and the adjustment function of the operable implant is selected to achieve the target treatment result at a planned useful life.

16. The step of selecting, by the computer system, at least one matching previous patient from one or two or more similar previous patients; The step of obtaining, by the computer system, the disease progression data of the at least one matching previous patient; The step of determining, by the computer system, a patient-specific implant adjustment plan for compensating the disease progression based on the obtained disease progression data; The computer-implemented method according to claim 11, further comprising:

17. The step of generating, by the computer system, a plurality of disease progressions and implant scenarios; The step of displaying, by the computer system, the disease progression scenario and the implant scenario; The step of receiving, by the computer system, one or two or more selections from among the disease progression scenarios to determine the minimum adjustment function of the operable implant; The computer-implemented method according to claim 11, further comprising:

18. At least one of the disease progression scenario and the implant scenario is The computer-implemented method according to claim 17, generated based on at least one of the predicted progression rate of the disease, the patient's health score, or the planned treatment period.

19. The computer-implemented method according to claim 11, wherein the predicted progression rate is determined based on one or two or more reference patient datasets.

20. A computer-implemented method for providing patient-specific medical care, comprising: The step of receiving, by a computer system, a patient dataset of a patient; The step of comparing, by the computer system, the patient dataset with the plurality of reference patient datasets to identify one or two or more similar patient datasets in the plurality of reference patient datasets; A step of selecting, by the computer system, a subset of the one or more similar patient data sets, wherein each similar patient data set of the selected subset shows data indicating a favorable treatment result, the selecting step; A step of identifying, by the computer system, medical device design data and implant adjustment data related to the favorable treatment result for at least one similar patient data set of the selected subset; A step of generating, by the computer system, at least one patient-specific medical device design for the patient based on the medical device design data and the implant adjustment data, wherein the at least one patient-specific medical device design is configured to be non-invasively actuated to adjust the configuration of the medical device after surgery, the generating step; A computer-implemented method comprising the above.

21. The comparing step is A step of generating, by the computer system, a similarity score for each reference patient data set based on a comparison between the spinal pathology data of the patient data set and the spinal pathology data of the reference patient data set, wherein the similarity score is at least partially based on whether an adjustable implant was used, the generating step; A step of identifying, by the computer system, the one or more similar patient data sets based at least in part on the similarity score; The computer-implemented method according to claim 20, comprising the above.

22. At least one of the similar patient data sets corresponds to a reference patient having (a) spinal pathology data similar to that of the patient and / or (b) having received treatment with a respective orthopedic implant using at least one actuator, At least one of the similar patient data sets of the selected subset includes data indicating that the treatment with each orthopedic implant received by the reference patient has resulted in a favorable treatment result, The computer-implemented method further includes a step of determining, by the computer system, parameters for expanding or shrinking the at least one patient-specific medical device design based on the selected subset. The computer-implemented method according to claim 20.

23. wherein the comparing step comprises: comparing, by the computer system, the patient data set and the reference patient data sets; generating, by the computer system, a similarity score for each reference patient data set based on a comparison between the patient data set and the respective reference patient data set; identifying, by the computer system, the one or more similar patient data sets based at least in part on the similarity score and whether the patient received a functional implant postoperatively; The computer-implemented method according to claim 20.

24. The computer-implemented method according to claim 23, wherein the similarity score represents a statistical correlation between the patient data set and each respective reference patient data set.

25. A computer-implemented method for designing a patient-specific orthopedic implant, the method comprising: comparing, by a computer system, a patient data set with a plurality of reference patient data sets to identify one or more similar patient data sets in the plurality of reference patient data sets, each similar patient data set corresponding to a reference patient who (a) has spinal pathology data similar to the patient and (b) received treatment with a functional implant postoperatively; identifying, by the computer system, for at least one similar patient data, design data for respective implants and actuator data for a surgical procedure for implanting the respective implants in the corresponding reference patients; generating, by the computer system, a design for the functional orthopedic implant for the patient's anatomical structure such that operation of the functional orthopedic implant is remotely controllable by an external controller based on the design data and the actuator data; The computer-implemented method.

26. A step of selecting, by the computer system, a subset of the one or more similar patient data sets used to identify the design data, wherein each similar patient data set of the selected subset includes data indicating that one or more adjustments to the implant received by the reference patient resulted in a favorable treatment outcome, the method further including the step of selecting, according to claim 25.

27. By a computer system, using a trained machine learning model, Determine a plurality of implant adjustment plans for a period and corresponding a plurality of orthopedic implant designs for treating the patient, For each of the plurality of implant adjustment plans and each of the corresponding plurality of orthopedic implant designs, determine the probability of achieving the target treatment outcome of the patient during the period, Select at least one of the plurality of implant adjustment plans and at least one of the corresponding plurality of orthopedic implant designs based at least in part on the determined probability of achieving the target treatment outcome during the period, The method further including the step of doing so, according to claim 25.

28. A computer-implemented method, A step of generating, by a computer system, at least a partial anatomical model of a patient, the anatomical model describing the patient's native anatomical structure, the step of generating, A step of generating, by the computer system, a series of corrected anatomical models representing anatomical changes over a period based on patient-specific corrections and predicted disease progression to the native anatomical structure, A step of determining, by the computer system, a plurality of treatment positions along the spine of the patient, A step of designing, by the computer system, an implant for each treatment position based on the patient-specific correction to compensate for the anatomical changes by the postoperative operation of the implant, Including a computer-implemented method.

29. When the implant is implanted at the plurality of treatment positions, the implant is configured such that a part of the patient substantially matches the corrected anatomical model, according to claim 28.

30. The computer-implemented method according to claim 28, wherein the anatomical model is a virtual model of at least a part of the spine.

31. The computer-implemented method according to claim 28, further comprising comparing, by the computer system, the anatomical model and the corrected anatomical model to determine the plurality of treatment positions.

32. A computer-implemented method for non-invasive anatomical adjustment, comprising: acquiring, by a computer system, a pre-adjustment image of the spine of the patient in a vertical posture so as to apply a load to at least one device implanted along the spine of the patient; determining, by the computer system, one or more anatomical corrections of the patient based on the pre-adjustment image and a patient-specific preoperative correction plan; providing, by the computer system, one or more anatomical corrections by non-invasively actuating the at least one device from a first configuration to a second configuration to move the spine towards a target anatomical configuration of the patient-specific preoperative correction plan; acquiring, using the at least one device in the second configuration, an image of the patient after adjustment; determining, based on the image after adjustment, whether to reconfigure the at least one device; A computer-implemented method comprising the steps of.

33. The pre-adjustment image includes at least one of a standing X-ray image or a sitting X-ray image, The image after adjustment includes at least one of a standing X-ray image or a sitting X-ray image, The computer-implemented method according to claim 32.

34. The step of acquiring the pre-adjustment image includes imaging the spine and generating a dynamic sitting / standing image while actuating the at least one device, according to the computer-implemented method of claim 32.

35. The at least one device includes a plurality of intervertebral fixation devices each implanted at a different level of the spine, The non-invasive actuation of the at least one device includes reconfiguring the intervertebral fixation device to move the postoperative spine of the patient to the target anatomical configuration to cause spinal fixation, according to the computer-implemented method of claim 32.

36. acquiring a preoperative image of the patient; Determining a postoperative adjustment function of the at least one device based on the patient-specific correction plan; Designing the at least one device with the postoperative adjustment function; The computer-implemented method according to claim 32, further comprising.

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