Dave's PCF WIP: Paragraphs

Paragraphs

Actions	Application	Content	Paragraph Number	Notes	Modified
		Content		Paragraph Number	Notes	Any Field
View Edit Delete	PER-20	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.	57	Added by DJM Jan 2024	1/6/24, 10:06 PM
View Edit Delete	PER-20	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.)	58	Added by DJM Jan 2024	1/6/24, 10:06 PM
View Edit Delete	PER-20	As used herein, "segmentation" or "image segmentation" refers to 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.	59	Added by DJM Jan 2024	1/6/24, 10:06 PM
View Edit Delete	PER-20	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.	60	Added by DJM Jan 2024	1/6/24, 10:06 PM
View Edit Delete	PER-20	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.)	61	Added by DJM Jan 2024	1/6/24, 10:06 PM
View Edit Delete	PER-20	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.	62	Added by DJM Jan 2024	1/6/24, 10:06 PM
View Edit Delete	PER-20	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 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.	63	Added by DJM Jan 2024	1/6/24, 10:06 PM

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