"Segmentation" or "Image segmentation" refers to the process of partitioning 2D images, 3D volumes, or temporal image sequences into meaningful regions, classes, or instances. These segments may correspond to different tissue classes, organs, pathologies, bones, landmarks, background, or other biologically relevant structures. Medical image segmentation addresses challenges such as low contrast, noise, artifacts, occlusion, and acquisition variability. Non-limiting examples include registration/atlas-based methods, shape/appearance models, level-set/active-contour methods, graph-cut/energy-minimization methods, and neural-network models such as convolutional or transformer-based encoder–decoders (e.g., U-Net) and hybrids. Segmentation may be implemented using classical computer-vision, rules-based, statistical, or machine-learning/deep-learning techniques, alone or in combination, and may operate at native or resampled resolution. Outputs may include pixel-wise representations (e.g., binary masks, label maps, probability maps, heatmaps), instance masks, and/or contours, which may be used to derive anatomic data, constructed references, measurements, and annotated images. … more "Segmentation" or "Image segmentation" refers to the process of partitioning 2D images, 3D volumes, or temporal image sequences into meaningful regions, classes, or instances. These segments may correspond to different tissue classes, organs, pathologies, bones, landmarks, background, or other biologically relevant structures. Medical image segmentation addresses challenges such as low contrast, noise, artifacts, occlusion, and acquisition variability. Non-limiting examples include registration/atlas-based methods, shape/appearance models, level-set/active-contour methods, graph-cut/energy-minimization methods, and neural-network models such as convolutional or transformer-based encoder–decoders (e.g., U-Net) and hybrids. Segmentation may be implemented using classical computer-vision, rules-based, statistical, or machine-learning/deep-learning techniques, alone or in combination, and may operate at native or resampled resolution. Outputs may include pixel-wise representations (e.g., binary masks, label maps, probability maps, heatmaps), instance masks, and/or contours, which may be used to derive anatomic data, constructed references, measurements, and annotated images.

As used herein, “anatomic data” refers to any data that is identified, collected, measured, generated, estimated, predicted, simulated, or otherwise obtained in connection with an anatomy of a human, animal, or the like, including both raw and derived information. Examples of anatomic data include, without limitation: (i) location data for anatomical structures, either independently or in relation to other structures within a coordinate system; (ii) classification, labeling, or identification data for one or more anatomical structures; (iii) geometric constructs derived from such structures, including points, centroids, lines, curves, axes, planes, or volumes; (iv) quantitative information such as distances, ratios, angles, surface areas, or other measurements between, across, or within structures; and (v) volumetric, material composition, density, or functional data, as well as other physical, biological, or physiological attributes. Anatomic data may be obtained from, but is not limited to, medical imaging (e.g., radiographs, CT, MRI, fluoroscopy, ultrasound, video, or the like), patient-specific measurements, sensors, monitors, anatomical models, computational simulations, or the like. Such data may be further processed, derived, predicted, or modified using computational algorithms, image processing techniques, artificial intelligence models, machine learning systems, large language models (LLMs), or the like. Anatomic data may be used to generate, manipulate, modify, or enhance annotated images, annotated video sequences, predictive overlays, or other visualizations that identify, highlight, or measure anatomical structures, features, or relationships. … more As used herein, “anatomic data” refers to any data that is identified, collected, measured, generated, estimated, predicted, simulated, or otherwise obtained in connection with an anatomy of a human, animal, or the like, including both raw and derived information. Examples of anatomic data include, without limitation: (i) location data for anatomical structures, either independently or in relation to other structures within a coordinate system; (ii) classification, labeling, or identification data for one or more anatomical structures; (iii) geometric constructs derived from such structures, including points, centroids, lines, curves, axes, planes, or volumes; (iv) quantitative information such as distances, ratios, angles, surface areas, or other measurements between, across, or within structures; and (v) volumetric, material composition, density, or functional data, as well as other physical, biological, or physiological attributes. Anatomic data may be obtained from, but is not limited to, medical imaging (e.g., radiographs, CT, MRI, fluoroscopy, ultrasound, video, or the like), patient-specific measurements, sensors, monitors, anatomical models, computational simulations, or the like. Such data may be further processed, derived, predicted, or modified using computational algorithms, image processing techniques, artificial intelligence models, machine learning systems, large language models (LLMs), or the like. Anatomic data may be used to generate, manipulate, modify, or enhance annotated images, annotated video sequences, predictive overlays, or other visualizations that identify, highlight, or measure anatomical structures, features, or relationships.

As used herein, “model” refers to an informative representation of an object, person or system. Representational models can be broadly divided into the concrete (e.g. physical form) and the abstract (e.g. behavioral patterns, especially as expressed in mathematical form). In abstract form, certain models may be based on data used in a computer system or software program to represent the model. Such models can be referred to as computer models. Computer models can be used to display the model, modify the model, print the model (either on a 2D medium or using a 3D printer or additive manufacturing technology). The printed physical form of the model can be referred to as a 3D model. Computer models can also be used in environments with models of other objects, people, or systems. Computer models can also be used to generate simulations, display in virtual environment systems, display in augmented reality systems, or the like. Computer models can be used in Computer Aided Design (CAD) and/or Computer Aided Manufacturing (CAM) systems. Certain models may be identified with an adjective that identifies the object, person, or system the model represents. For example, a “bone” model is a model of a bone, and a “heart” model is a model of a heart. (Search “model” on Wikipedia.com June 13, 2021. CC-BY-SA 3.0 Modified. Accessed June 23, 2021.) … more As used herein, “model” refers to an informative representation of an object, person or system. Representational models can be broadly divided into the concrete (e.g. physical form) and the abstract (e.g. behavioral patterns, especially as expressed in mathematical form). In abstract form, certain models may be based on data used in a computer system or software program to represent the model. Such models can be referred to as computer models. Computer models can be used to display the model, modify the model, print the model (either on a 2D medium or using a 3D printer or additive manufacturing technology). The printed physical form of the model can be referred to as a 3D model. Computer models can also be used in environments with models of other objects, people, or systems. Computer models can also be used to generate simulations, display in virtual environment systems, display in augmented reality systems, or the like. Computer models can be used in Computer Aided Design (CAD) and/or Computer Aided Manufacturing (CAM) systems. Certain models may be identified with an adjective that identifies the object, person, or system the model represents. For example, a “bone” model is a model of a bone, and a “heart” model is a model of a heart. (Search “model” on Wikipedia.com June 13, 2021. CC-BY-SA 3.0 Modified. Accessed June 23, 2021.)

As used herein, "medical image computing", "medical image processing", or "medical imaging" refers to systems, software, hardware, components, and/or apparatus that involve and combine the fields of computer science, information engineering, electrical engineering, physics, mathematics and medicine. Medical image computing develops computational and mathematical methods for working with medical images and their use for biomedical research and clinical care. One goal for medical image computing is to extract clinically relevant information or knowledge from medical images. While closely related to the field of medical imaging, medical image computing focuses on the computational analysis of the images, not their acquisition. The methods can be grouped into several broad categories: image segmentation, image registration, image-based physiological modeling, and others. (Search "medical image computing" on Wikipedia.com June 24, 2021. CC-BY-SA 3.0 Modified. Accessed June 24, 2021.) Medical image computing may include one or more processors or controllers on one or more computing devices. Such processors or controllers may be referred to herein as medical image processors. Medical imaging and medical image computing together can provide systems and methods to image, quantify and fuse both structural and functional information about a patient in vivo. These two technologies include the transformation of computational models to represent specific subjects/patients, thus paving the way for personalized computational models. Individualization of generic computational models through imaging can be realized in three complementary directions: definition of the subject-specific computational domain (anatomy) and related subdomains (tissue types); definition of boundary and initial conditions from (dynamic and/or functional) imaging; and characterization of structural and functional tissue properties. Medical imaging and medical image computing enable the translation of models to the clinical setting with both diagnostic and therapeutic applications. (Id.) … more As used herein, "medical image computing", "medical image processing", or "medical imaging" refers to systems, software, hardware, components, and/or apparatus that involve and combine the fields of computer science, information engineering, electrical engineering, physics, mathematics and medicine. Medical image computing develops computational and mathematical methods for working with medical images and their use for biomedical research and clinical care. One goal for medical image computing is to extract clinically relevant information or knowledge from medical images. While closely related to the field of medical imaging, medical image computing focuses on the computational analysis of the images, not their acquisition. The methods can be grouped into several broad categories: image segmentation, image registration, image-based physiological modeling, and others. (Search "medical image computing" on Wikipedia.com June 24, 2021. CC-BY-SA 3.0 Modified. Accessed June 24, 2021.) Medical image computing may include one or more processors or controllers on one or more computing devices. Such processors or controllers may be referred to herein as medical image processors. Medical imaging and medical image computing together can provide systems and methods to image, quantify and fuse both structural and functional information about a patient in vivo. These two technologies include the transformation of computational models to represent specific subjects/patients, thus paving the way for personalized computational models. Individualization of generic computational models through imaging can be realized in three complementary directions: definition of the subject-specific computational domain (anatomy) and related subdomains (tissue types); definition of boundary and initial conditions from (dynamic and/or functional) imaging; and characterization of structural and functional tissue properties. Medical imaging and medical image computing enable the translation of models to the clinical setting with both diagnostic and therapeutic applications. (Id.)

As used herein, "image registration" refers to a method, process, module, component, apparatus, and/or system that seeks to achieve precision in the alignment of two images. As used here, "image" may refer to one or more of an image of a structure or object, a time series of images such as a video or other time series, another image, or a model (e.g., a computer-based model or a physical model, in either two dimensions or three dimensions). In the simplest case of image registration, two images are aligned. One image may serve as the target image and the other as a source image; the source image is transformed, positioned, realigned, and/or modified to match the target image. An optimization procedure may be applied that updates the transformation of the source image based on a similarity value that evaluates the current quality of the alignment. An iterative procedure of optimization may be repeated until a (local) optimum is found. An example is the registration of CT and PET images to combine structural and metabolic information. Image registration can be used in a variety of medical applications: Studying temporal changes; Longitudinal studies may acquire images over several months or years to study long-term processes, such as disease progression. Time series correspond to images acquired within the same session (seconds or minutes). Time series images can be used to study cognitive processes, heart deformations and respiration; Combining complementary information from different imaging modalities. One example may be the fusion of anatomical and functional information. Since the size and shape of structures vary across modalities, evaluating the alignment quality can be more challenging. Thus, similarity measures such as mutual information may be used; Characterizing a population of subjects. In contrast to intra-subject registration, a one-to-one mapping may not exist between subjects, depending on the structural variability of the organ of interest. Inter-subject registration may be used for atlas construction in computational anatomy. Here, the objective may be to statistically model the anatomy of organs across subjects; Computer-assisted surgery: in computer-assisted surgery pre-operative images such as CT or MRI may be registered to intra-operative images or tracking systems to facilitate image guidance or navigation. Image registration can be done using an intrinsic method or an extrinsic method or a combination of both. The extrinsic image registration method uses an outside object that is introduced into the physical space where the image was taken. The outside object may be referred to using different names herein such as a "reference," "visual reference," "visualization reference," "reference point," "reference marker," "patient reference," or "fiducial marker." The intrinsic image registration method uses information from the image of the patient, such as landmarks and object surfaces. There may be several considerations made when performing image registration: The transformation model. Common choices are rigid, affine, and deformable (i.e., nonlinear) transformation models. B-spline and thin plate spline models are commonly used for parameterized transformation fields. Non-parametric or dense deformation fields carry a displacement vector at every grid location; this may use additional regularization constraints. A specific class of deformation fields are diffeomorphisms, which are invertible transformations with a smooth inverse; The similarity metric. A distance or similarity function is used to quantify the registration quality. This similarity can be calculated either on the original images or on features extracted from the images. Common similarity measures are sum of squared distances (SSD), correlation coefficient, and mutual information. The choice of similarity measure depends on whether the images are from the same modality; the acquisition noise can also play a role in this decision. For example, SSD may be the optimal similarity measure for images of the same modality with Gaussian noise. However, the image statistics in ultrasound may be significantly different from Gaussian noise, leading to the introduction of ultrasound specific similarity measures. Multi-modal registration may use a more sophisticated similarity measure; alternatively, a different image representation can be used, such as structural representations or registering adjacent anatomy; The optimization procedure. Either continuous or discrete optimization is performed. For continuous optimization, gradient-based optimization techniques are applied to improve the convergence speed.(Search "medical image computing" on Wikipedia.com June 24, 2021. CC-BY-SA 3.0 Modified. Accessed June 25, 2021.) … more As used herein, "image registration" refers to a method, process, module, component, apparatus, and/or system that seeks to achieve precision in the alignment of two images. As used here, "image" may refer to one or more of an image of a structure or object, a time series of images such as a video or other time series, another image, or a model (e.g., a computer-based model or a physical model, in either two dimensions or three dimensions). In the simplest case of image registration, two images are aligned. One image may serve as the target image and the other as a source image; the source image is transformed, positioned, realigned, and/or modified to match the target image. An optimization procedure may be applied that updates the transformation of the source image based on a similarity value that evaluates the current quality of the alignment. An iterative procedure of optimization may be repeated until a (local) optimum is found. An example is the registration of CT and PET images to combine structural and metabolic information. Image registration can be used in a variety of medical applications: Studying temporal changes; Longitudinal studies may acquire images over several months or years to study long-term processes, such as disease progression. Time series correspond to images acquired within the same session (seconds or minutes). Time series images can be used to study cognitive processes, heart deformations and respiration; Combining complementary information from different imaging modalities. One example may be the fusion of anatomical and functional information. Since the size and shape of structures vary across modalities, evaluating the alignment quality can be more challenging. Thus, similarity measures such as mutual information may be used; Characterizing a population of subjects. In contrast to intra-subject registration, a one-to-one mapping may not exist between subjects, depending on the structural variability of the organ of interest. Inter-subject registration may be used for atlas construction in computational anatomy. Here, the objective may be to statistically model the anatomy of organs across subjects; Computer-assisted surgery: in computer-assisted surgery pre-operative images such as CT or MRI may be registered to intra-operative images or tracking systems to facilitate image guidance or navigation. Image registration can be done using an intrinsic method or an extrinsic method or a combination of both. The extrinsic image registration method uses an outside object that is introduced into the physical space where the image was taken. The outside object may be referred to using different names herein such as a "reference," "visual reference," "visualization reference," "reference point," "reference marker," "patient reference," or "fiducial marker." The intrinsic image registration method uses information from the image of the patient, such as landmarks and object surfaces. There may be several considerations made when performing image registration: The transformation model. Common choices are rigid, affine, and deformable (i.e., nonlinear) transformation models. B-spline and thin plate spline models are commonly used for parameterized transformation fields. Non-parametric or dense deformation fields carry a displacement vector at every grid location; this may use additional regularization constraints. A specific class of deformation fields are diffeomorphisms, which are invertible transformations with a smooth inverse; The similarity metric. A distance or similarity function is used to quantify the registration quality. This similarity can be calculated either on the original images or on features extracted from the images. Common similarity measures are sum of squared distances (SSD), correlation coefficient, and mutual information. The choice of similarity measure depends on whether the images are from the same modality; the acquisition noise can also play a role in this decision. For example, SSD may be the optimal similarity measure for images of the same modality with Gaussian noise. However, the image statistics in ultrasound may be significantly different from Gaussian noise, leading to the introduction of ultrasound specific similarity measures. Multi-modal registration may use a more sophisticated similarity measure; alternatively, a different image representation can be used, such as structural representations or registering adjacent anatomy; The optimization procedure. Either continuous or discrete optimization is performed. For continuous optimization, gradient-based optimization techniques are applied to improve the convergence speed.(Search "medical image computing" on Wikipedia.com June 24, 2021. CC-BY-SA 3.0 Modified. Accessed June 25, 2021.)

As used herein, "patient specific osteotomy procedure" refers to an osteotomy procedure that has been adjusted, tailored, modified, or configured to specifically address the anatomy, physiology, condition, abnormalities, needs, or desires of a particular patient. In certain aspects, one patient specific osteotomy procedure may be useable in connection with only one patient. In other aspects, one patient specific osteotomy procedure may be useable with a number of patients having a particular class of characteristics. In certain aspects, a patient specific osteotomy procedure may refer to a non-patient specific osteotomy procedure that includes one or more patient specific implants and/or instrumentation. In another aspects, a patient specific osteotomy procedure may refer to a patient specific osteotomy procedure that includes one or more patient specific implants, patient specific surgical steps, and/or patient specific instrumentation. … more As used herein, "patient specific osteotomy procedure" refers to an osteotomy procedure that has been adjusted, tailored, modified, or configured to specifically address the anatomy, physiology, condition, abnormalities, needs, or desires of a particular patient. In certain aspects, one patient specific osteotomy procedure may be useable in connection with only one patient. In other aspects, one patient specific osteotomy procedure may be useable with a number of patients having a particular class of characteristics. In certain aspects, a patient specific osteotomy procedure may refer to a non-patient specific osteotomy procedure that includes one or more patient specific implants and/or instrumentation. In another aspects, a patient specific osteotomy procedure may refer to a patient specific osteotomy procedure that includes one or more patient specific implants, patient specific surgical steps, and/or patient specific instrumentation.

As used herein, "preoperative plan" refers to a plan for performing a surgical procedure. Depending on the complexity of a surgery, a preoperative plan can be very simple and generic or very detailed and specific to a particular surgical procedure. In one aspect, a preoperative plan may include very detailed and specific step by step instructions for the surgical procedure. The instructions may be ordered according to a specific order for accomplishing a desired outcome. In certain embodiments, a preoperative plan may indicate which instruments, machines, systems, test, and/or personnel to use for the surgical procedure. A preoperative plan can take many forms and formats based on the needs and desires of the users of the preoperative plan. In one embodiment, the preoperative plan is a report that is displayed on a screen or that can be printed onto paper. In another embodiment, the preoperative plan may include instructions for operation planning software. In another embodiment, the preoperative plan may include instructions for surgical rehearsal tools, including software. In another embodiment, the preoperative plan may include instructions for operation planning using virtual reality or augmented reality software. … more As used herein, "preoperative plan" refers to a plan for performing a surgical procedure. Depending on the complexity of a surgery, a preoperative plan can be very simple and generic or very detailed and specific to a particular surgical procedure. In one aspect, a preoperative plan may include very detailed and specific step by step instructions for the surgical procedure. The instructions may be ordered according to a specific order for accomplishing a desired outcome. In certain embodiments, a preoperative plan may indicate which instruments, machines, systems, test, and/or personnel to use for the surgical procedure. A preoperative plan can take many forms and formats based on the needs and desires of the users of the preoperative plan. In one embodiment, the preoperative plan is a report that is displayed on a screen or that can be printed onto paper. In another embodiment, the preoperative plan may include instructions for operation planning software. In another embodiment, the preoperative plan may include instructions for surgical rehearsal tools, including software. In another embodiment, the preoperative plan may include instructions for operation planning using virtual reality or augmented reality software.

As used herein, "medical imaging" refers to a technique and/or process of imaging the interior or exterior of a body for clinical analysis and medical intervention, as well as a visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging may be used to establish a database of normal anatomy and physiology to make possible identification of abnormalities. Medical imaging in its widest sense, is part of biological imaging and incorporates radiology, which uses the imaging technologies of X-ray radiography, magnetic resonance imaging, ultrasound, endoscopy, elastography, tactile imaging, thermography, medical photography, nuclear medicine functional imaging techniques as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). Another form of X-ray radiography includes computerized tomography (CT) scans in which a computer controls the position of the X-ray sources and detectors. Magnetic Resonance Imaging (MRI) is another medical imaging technology. Fluoroscopy is an imaging technique that uses X-rays to obtain real-time moving images of the interior of an object. In its primary application of medical imaging, a fluoroscope allows a physician to see the internal structure and function of a patient, so that the pumping action of the heart or the motion of swallowing, for example, can be watched. This is useful for both diagnosis and therapy and occurs in general radiology, interventional radiology, and image-guided surgery. (Search "medical imaging" on Wikipedia.com July 14, 2021. CC-BY-SA 3.0 Modified. Accessed Sept. 1, 2021.) Data analyzed, generated, manipulated, interpolated, collected, stored, reviewed, and/or modified in connection with medical imaging or medical image processing can be referred to herein as medical imaging data or medical image data. Measurement and recording techniques that are not primarily designed to produce images, such as electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), and others, represent other technologies that produce data susceptible to representation as a parameter graph vs. time or maps that contain data about the measurement locations. In certain embodiments bone imaging includes devices that scan and gather bone density anatomic data. These technologies may be considered forms of medical imaging in certain disciplines. (Search "medical imaging" on Wikipedia.com June 16, 2021. CC-BY-SA 3.0 Modified. Accessed June 23, 2021.) … more As used herein, "medical imaging" refers to a technique and/or process of imaging the interior or exterior of a body for clinical analysis and medical intervention, as well as a visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging may be used to establish a database of normal anatomy and physiology to make possible identification of abnormalities. Medical imaging in its widest sense, is part of biological imaging and incorporates radiology, which uses the imaging technologies of X-ray radiography, magnetic resonance imaging, ultrasound, endoscopy, elastography, tactile imaging, thermography, medical photography, nuclear medicine functional imaging techniques as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). Another form of X-ray radiography includes computerized tomography (CT) scans in which a computer controls the position of the X-ray sources and detectors. Magnetic Resonance Imaging (MRI) is another medical imaging technology. Fluoroscopy is an imaging technique that uses X-rays to obtain real-time moving images of the interior of an object. In its primary application of medical imaging, a fluoroscope allows a physician to see the internal structure and function of a patient, so that the pumping action of the heart or the motion of swallowing, for example, can be watched. This is useful for both diagnosis and therapy and occurs in general radiology, interventional radiology, and image-guided surgery. (Search "medical imaging" on Wikipedia.com July 14, 2021. CC-BY-SA 3.0 Modified. Accessed Sept. 1, 2021.) Data analyzed, generated, manipulated, interpolated, collected, stored, reviewed, and/or modified in connection with medical imaging or medical image processing can be referred to herein as medical imaging data or medical image data. Measurement and recording techniques that are not primarily designed to produce images, such as electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), and others, represent other technologies that produce data susceptible to representation as a parameter graph vs. time or maps that contain data about the measurement locations. In certain embodiments bone imaging includes devices that scan and gather bone density anatomic data. These technologies may be considered forms of medical imaging in certain disciplines. (Search "medical imaging" on Wikipedia.com June 16, 2021. CC-BY-SA 3.0 Modified. Accessed June 23, 2021.)

As used herein, "patient imaging data" refers to data identified, used, collected, gathered, and/or generated in connection with medical imaging for a particular patient. Patient imaging data is one type of medical imaging data. Patient imaging data can be shared between users, systems, patients, and professionals using a common data format referred to as Digital Imaging and Communications in Medicine (DICOM) data. DICOM data is a standard format for storing, viewing, retrieving, and sharing medical images. … more As used herein, "patient imaging data" refers to data identified, used, collected, gathered, and/or generated in connection with medical imaging for a particular patient. Patient imaging data is one type of medical imaging data. Patient imaging data can be shared between users, systems, patients, and professionals using a common data format referred to as Digital Imaging and Communications in Medicine (DICOM) data. DICOM data is a standard format for storing, viewing, retrieving, and sharing medical images.

As used herein, “anatomic mapping” refers to a process of determining one or more points, landmarks, or features of an anatomic structure of a patient. In certain aspects, an anatomic mapping can generate a set of anatomic data representative of a structure of a patient. Anatomic mapping may be performed on structures of a patient, of a physical model of an anatomical structure, or on computer model of an anatomical structure. … more As used herein, “anatomic mapping” refers to a process of determining one or more points, landmarks, or features of an anatomic structure of a patient. In certain aspects, an anatomic mapping can generate a set of anatomic data representative of a structure of a patient. Anatomic mapping may be performed on structures of a patient, of a physical model of an anatomical structure, or on computer model of an anatomical structure.

As used herein, a "fixator" refers to an apparatus, instrument, structure, device, component, member, system, assembly, or module structured, organized, configured, designed, arranged, or engineered to connect two bones or bone fragments or a single bone or bone fragment and another fixator to position and retain the bone or bone fragments in a desired position and/or orientation. Fixators can also serve to redistribute load and stresses experienced by bone(s) and/or body parts and can serve to reduce relative motion of one part relative to others. Examples of fixators include both those for external fixation as well as those for internal fixation and include, but are not limited to pins, wires, Kirschner wires, screws, anchors, bone anchors, plates, bone plates, intramedullary nails or rods or pins, implants, interbody cages, fusion cages, and the like. … more As used herein, a "fixator" refers to an apparatus, instrument, structure, device, component, member, system, assembly, or module structured, organized, configured, designed, arranged, or engineered to connect two bones or bone fragments or a single bone or bone fragment and another fixator to position and retain the bone or bone fragments in a desired position and/or orientation. Fixators can also serve to redistribute load and stresses experienced by bone(s) and/or body parts and can serve to reduce relative motion of one part relative to others. Examples of fixators include both those for external fixation as well as those for internal fixation and include, but are not limited to pins, wires, Kirschner wires, screws, anchors, bone anchors, plates, bone plates, intramedullary nails or rods or pins, implants, interbody cages, fusion cages, and the like.

As used herein, "artificial intelligence" refers to intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between artificial intelligence and natural intelligence categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as artificial general intelligence (AGI) while attempts to emulate 'natural' intelligence have been called artificial biological intelligence (ABI). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of achieving its goals. The term "artificial intelligence" can also be used to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving". (Search "artificial intelligence" on Wikipedia.com June 25, 2021. CC-BY-SA 3.0 Modified. Accessed June 25, 2021.) … more As used herein, "artificial intelligence" refers to intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between artificial intelligence and natural intelligence categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as artificial general intelligence (AGI) while attempts to emulate 'natural' intelligence have been called artificial biological intelligence (ABI). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of achieving its goals. The term "artificial intelligence" can also be used to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving". (Search "artificial intelligence" on Wikipedia.com June 25, 2021. CC-BY-SA 3.0 Modified. Accessed June 25, 2021.)

As used herein, “robotic surgical assistance tool” refers to a tool, system, device, component, or apparatus configured and designed to assist a surgeon in performing a surgical procedure and/or more or more steps of a surgical procedure. The robotic surgical assistance tool may include a computing device, a computer memory device, and a set of one or more instruments that may be wholly or partically controlled by a computing device and/or a surgeon. … more As used herein, “robotic surgical assistance tool” refers to a tool, system, device, component, or apparatus configured and designed to assist a surgeon in performing a surgical procedure and/or more or more steps of a surgical procedure. The robotic surgical assistance tool may include a computing device, a computer memory device, and a set of one or more instruments that may be wholly or partically controlled by a computing device and/or a surgeon.

As used herein, “manufacturing tool” or "fabrication tool" refers to a manufacturing or fabrication process, tool, system, or apparatus which creates an object, device, apparatus, feature, or component using one or more source materials. A manufacturing tool or fabrication tool can use a variety of manufacturing processes, including but not limited to additive manufacturing, subtractive manufacturing, forging, casting, and the like. The manufacturing tool can use a variety of materials including polymers, thermoplastics, metals, biocompatible materials, biodegradable materials, ceramics, biochemicals, and the like. A manufacturing tool may be operated manually by an operator, automatically using a computer numerical controller (CNC), or a combination of these techniques. … more As used herein, “manufacturing tool” or "fabrication tool" refers to a manufacturing or fabrication process, tool, system, or apparatus which creates an object, device, apparatus, feature, or component using one or more source materials. A manufacturing tool or fabrication tool can use a variety of manufacturing processes, including but not limited to additive manufacturing, subtractive manufacturing, forging, casting, and the like. The manufacturing tool can use a variety of materials including polymers, thermoplastics, metals, biocompatible materials, biodegradable materials, ceramics, biochemicals, and the like. A manufacturing tool may be operated manually by an operator, automatically using a computer numerical controller (CNC), or a combination of these techniques.

As used herein, "bone-facing surface" refers to a surface of an object, instrument, or apparatus, such as an implant that is oriented toward or faces one or more bones of a patient. In one aspect, the bone-facing surface may abut, touch, or contact a surface of a bone. In another aspect, the bone-facing surface or parts of the bone-facing surface may be close to, but not abut, touch, or contact a surface of the bone. … more As used herein, "bone-facing surface" refers to a surface of an object, instrument, or apparatus, such as an implant that is oriented toward or faces one or more bones of a patient. In one aspect, the bone-facing surface may abut, touch, or contact a surface of a bone. In another aspect, the bone-facing surface or parts of the bone-facing surface may be close to, but not abut, touch, or contact a surface of the bone.

As used herein, "instrument model" refers to a model, either physical or digital, that represents an instrument, tool, apparatus, or device. Examples, of an instrument model can include a cutting guide model, a patient specific cutting guide model, and the like. … more As used herein, "instrument model" refers to a model, either physical or digital, that represents an instrument, tool, apparatus, or device. Examples, of an instrument model can include a cutting guide model, a patient specific cutting guide model, and the like.

As used herein, "template cutting guide" refers to a guide configured, designed, and/or engineered to serve as a template for creating, generating, or fabricating a patient specific cutting guide. In one aspect, the template cutting guide may be used, as-is, without any further changes, modifications, or adjustments and thus become a patient specific cutting guide. In another aspect, the template cutting guide may be modified, adjusted, or configured to more specifically address the goals, objectives, or needs of a patient or a surgeon and by way of the modifications become a patient specific cutting guide. The patient specific cutting guide can be used by a user, such as a surgeon, to guide making one or more resections of a structure, such as a bone for a procedure. … more As used herein, "template cutting guide" refers to a guide configured, designed, and/or engineered to serve as a template for creating, generating, or fabricating a patient specific cutting guide. In one aspect, the template cutting guide may be used, as-is, without any further changes, modifications, or adjustments and thus become a patient specific cutting guide. In another aspect, the template cutting guide may be modified, adjusted, or configured to more specifically address the goals, objectives, or needs of a patient or a surgeon and by way of the modifications become a patient specific cutting guide. The patient specific cutting guide can be used by a user, such as a surgeon, to guide making one or more resections of a structure, such as a bone for a procedure.