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A bone model or anatomic model of a patient’s body or body part(s) may be generated by computing devices that analyze medical imaging images. Structures of a patient’s body can be determined using a process called segmentation. |
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"Trajectory guide" or "trajectory indicator" or "targeting guide" refers to any structure, apparatus, surface, device, system, feature, or aspect configured to indicate, identify, guide, place, position, or otherwise assist in marking or deploying a fastener or other structure along a desired trajectory for one or more subsequent steps in a procedure. |
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As used herein, a “guard” refers to an apparatus, instrument, structure, member, device, component, system, or assembly structured, organized, configured, designed, arranged, or engineered to prevent, limit, impede, stop, or restrict motion, action, or movement and/or operation of the another object, member, structure, component, part, apparatus, system, or assembly beyond a certain parameter such as a boundary. Said another way, a "guard" refers to an apparatus, instrument, structure, member, device, component, system, or assembly structured, organized, configured, designed, arranged, or engineered to retain, maintain, hold, keep, or restrict motion, action, or movement and/or operation of the another object, member, structure, component, part, apparatus, system, or assembly within or at one or more parameters such as a boundary. |
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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.) |
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As used herein, "segmentation" or "image segmentation" refers the process of partitioning an image into different meaningful segments. These segments may correspond to different tissue classes, organs, pathologies, bones, or other biologically relevant structures. Medical image segmentation accommodates imaging ambiguities such as by low contrast, noise, and other imaging ambiguities. |
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Certain computer vision techniques can be used or adapted for image segmentation. For example, the techniques and or algorithms for segmentation may include, but are not limited to: Atlas-Based Segmentation: For many applications, a clinical expert can manually label several images; segmenting unseen images is a matter of extrapolating from these manually labeled training images. Methods of this style are typically referred to as atlas-based segmentation methods. Parametric atlas methods typically combine these training images into a single atlas image, while nonparametric atlas methods typically use all of the training images separately. Atlas-based methods usually require the use of image registration in order to align the atlas image or images to a new, unseen image. |
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Image registration is a process of correctly aligning images; Shape-Based Segmentation: Many methods parametrize a template shape for a given structure, often relying on control points along the boundary. The entire shape is then deformed to match a new image. Two of the most common shape-based techniques are Active Shape Models and Active Appearance Models; Image-Based Segmentation: Some methods initiate a template and refine its shape according to the image data while minimizing integral error measures, like the Active contour model and its variations; Interactive Segmentation: Interactive methods are useful when clinicians can provide some information, such as a seed region or rough outline of the region to segment. An algorithm can then iteratively refine such a segmentation, with or without guidance from the clinician. Manual segmentation, using tools such as a paint brush to explicitly define the tissue class of each pixel, remains the gold standard for many imaging applications. Recently, principles from feedback control theory have been incorporated into segmentation, which give the user much greater flexibility and allow for the automatic correction of errors; Subjective surface Segmentation: This method is based on the idea of evolution of segmentation function which is governed by an advection-diffusion model. To segment an object, a segmentation seed is needed (that is the starting point that determines the approximate position of the object in the image). Consequently, an initial segmentation function is constructed. With the subjective surface method, the position of the seed is the main factor determining the form of this segmentation function; and Hybrid segmentation which is based on combination of methods. (Search "medical image computing" on Wikipedia.com June 24, 2021. CC-BY-SA 3.0 Modified. Accessed June 24, 2021.) |
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As used herein, "medical imaging" refers to a technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as 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. 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.) Data, including images, text, and other data associated with medical imaging is referred to as patient imaging data. As used herein, "patient imaging data" refers to data identified, used, collected, gathered, and/or generated in connection with medical imaging and/or 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. |
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As used herein, "medical image computing" or "medical image processing" 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 in the translation of models to the clinical setting with both diagnostic and therapeutic applications. (Id.) In certain embodiments, medical image computing can be used to generate a bone model, a patient-specific model, and/or a patent specific instrument from medical imaging and/or medical imaging data. |
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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). 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, “additive manufacturing” refers to a manufacturing process in which materials are joined together in a process that repeatedly builds one layer on top of another to generate a three-dimensional structure or object. Additive manufacturing may also be referred to using different terms including additive processes, additive fabrication, additive techniques, additive layer manufacturing, layer manufacturing, freeform fabrication, ASTM F2792 (American Society for Testing and Materials), and 3D printing. Additive manufacturing can build the three-dimensional structure or object using computer-controlled equipment that applies successive layers of the material(s) based on a three-dimensional model that may be defined using Computer Aided Design (CAD) software. Additive manufacturing can use a variety of materials including polymers, thermoplastics, metals, ceramics, biochemicals, and the like. Additive manufacturing may provide unique benefits, as an implant together with the pores and/or lattices can be directly manufactured (without the need to generate molds, tool paths, perform any milling, and/or other manufacturing steps). |
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"Repository" refers to any data source or dataset that includes data or content. In one embodiment, a repository resides on a computing device. In another embodiment, a repository resides on a remote computing or remote storage device. A repository may comprise a file, a folder, a directory, a set of files, a set of folders, a set of directories, a database, an application, a software application, content of a text, content of an email, content of a calendar entry, and the like. A repository, in one embodiment, comprises unstructured data. A repository, in one embodiment, comprises structured data such as a table, an array, a queue, a look up table, a hash table, a heap, a stack, or the like. A repository may store data in any format including binary, text, encrypted, unencrypted, a proprietary format, or the like. |
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As used herein, "registration" or " 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 either or both an image of a structure or object and 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. |
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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. There may be several considerations made when performing image registration: The transformation model. Common choices are rigid, affine, and deformable 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. |
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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.) |
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As used herein, a "resection" refers to a method, procedure, or step that removes tissue from another anatomical structure or body. A resection is typically performed by a surgeon on a part of a body of a patient. (Search "surgery" on Wikipedia.com May 26, 2021. CC-BY-SA 3.0 Modified. Accessed May 26, 2021.) Resection may be used as a noun or a verb. In the verb form, the term is "resect" and refers to an act of performing, or doing, a resection. Past tense of the verb resect is resected. |
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"Bone condition" refers to any of a variety of conditions of bones of a patient. Generally, a bone condition refers to an orientation, position, and/or alignment of one or more bones of the patient relative to other anatomical structures of the body of the patient. Bone conditions may be caused by or result from deformities, misalignment, malrotation, fractures, joint failure, and/or the like. A bone condition includes, but is not limited to, any angular deformities of one or more bone segments in either the lower or upper extremities (for example, tibial deformities, calcaneal deformities, femoral deformities, and radial deformities). Alternatively, or in addition, “bone condition” can refer to the structural makeup and configuration of one or more bones of a patient. Thus bone condition may refer to a state or condition of regions, a thickness of a cortex, bone density, a thickness and/or porosity of internal regions (e.g. whether it is calcaneus or solid) of the bone or parts of the bone such as a head, a base, a shaft, a protuberance, a process, a lamina, a foramen, and the like of a bone, along the metaphyseal region, epiphysis region, and/or a diaphyseal region. "Malrotation" refers to a condition in which a part, typically a part of a patient's body has rotated from a normal position to an unnormal or uncommon position. |
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As used herein, a "guide" refers to a part, component, member, or structure designed, adapted, configured, or engineered to guide or direct one or more other parts, components, or structures. A guide may be part of, integrated with, connected to, attachable to, or coupled to, another structure, device, or instrument. In one embodiment, a guide may include a modifier that identifies a particular function, location, orientation, operation, type, and/or a particular structure of the guide. Examples of such modifiers applied to a guide, include, but are not limited to, "pin guide" that guides or directs one or more pins, a "cutting guide" that guides or directs the making or one or more cuts, a placement, deployment, or insertion guide that guides or directs the placement, positioning, orientation, deployment, installation, or insertion of a fastener and/or implant, a "cross fixation guide" that guides deployment of a fastener or fixation member, an "alignment guide" that guides the alignment of two or more objects or structures, a "resection guide" that serves to guide resection of soft or hard tissue, such as in an osteotomy, a "reduction guide" can serve to guide reduction of one or more bone segments or fragments, an "placement guide" that serves to identify how an object can be placed in relation to another object or structure, and the like. Furthermore, guides may include modifiers applied due to the procedure or location within a patient for which the guide is to be used. For example, where a guide is used at a joint, the guide may be referred to herein as an “arthrodesis guide”. |
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As used herein, "feature" refers to a distinctive attribute or aspect of something. (Search "feature" on google.com. Oxford Languages, 2021. Web. 20 Apr. 2021.) A feature may include one or more apparatuses, structures, objects, systems, sub-systems, devices, or the like. A feature may include a modifier that identifies a particular function or operation and/or a particular structure relating to the feature. Examples of such modifiers applied to a feature, include, but are not limited to, "attachment feature," "securing feature," "placement feature," "protruding feature," "engagement feature," "disengagement feature," “resection feature”, “guide feature”, and the like. |
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"Cortical bone" refers to a type of bone tissue. Cortical bone is a type of bone tissue typically found between an external surface of a bone and an interior area of the bone. Cortical bone is more dense and typically stronger structurally than other types of bone tissue. “Cortical surface” refers to a surface of cortical bone. |
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"Transosseous placement feature" refers to a placement feature that extends through one or more bones and that enables, or facilitates, placement of another device, apparatus, or instrument. |
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