Abstract: Data account management systems and methods are described. A server is in communication with one or more information source systems, at least one user device associated with at least one user and one or more electronic data accounts associated with the user. The server links the electronic data accounts, monitors data among the information source systems and the linked accounts for any changes in activity associated with the user, predicts a behavioral activity of the user via machine learning based on at least one of the monitored data and historical data associated with the user, identifies at least one notification instruction specific to the user based on the predicted behavioral activity, and generates an interactive graphical user interface (GUI) for display on the user device. The interactive GUI presents one or more of the notification instruction and a user profile of the at least one user among the linked accounts.

Abstract: Disclosed are systems and methods for classifying brain lesions based on single point in time imaging, methods for training a machine learning model for classifying brain lesions, and a method of predicting formation of brain lesions based on single point in time imaging. A method of classifying brain lesions based on single point in time imaging can include; accessing patient image data from a single point in time; providing the patient image data as an input to a brain lesion classification model; generating a classification for each of one or more lesions identified in the patient image data; and providing the classification for each of the one or more lesions for display on one or more display devices; wherein the brain lesion classification model is trained using subject image data for a plurality of subjects, the subject image data being captured at two or more points in time.

Abstract: Methods of the disclosure may include obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third brain volume (BV) values at the first time point based on two or more images from the first set of medical images and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first, second, third, or fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

Abstract: A telescoping assembly such as an endobronchial ultrasound needle (EBUS) assembly includes a motor coupled to a biopsy needle within a telescoping housing to rotate (such as to oscillate) the needle during tissue harvest to improve harvesting.

Abstract: A telescoping assembly such as an endobronchial ultrasound needle (EBUS) assembly includes a motor coupled to a biopsy needle within a telescoping housing to rotate (such as to oscillate) the needle during tissue harvest to improve harvesting.

Abstract: Methods of the disclosure may include obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third brain volume (BV) values at the first time point based on two or more images from the first set of medical images and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first, second, third, or fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

Abstract: A brain volume (BV) calculation method includes obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third BV values at the first time point based on two or more images from the first set of medical images, and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first to fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

Abstract: A telescoping assembly such as an endobronchial ultrasound needle (EBUS) assembly includes a motor coupled to a biopsy needle within a telescoping housing to rotate (such as to oscillate) the needle during tissue harvest to improve harvesting.

Abstract: A telescoping assembly such as an endobronchial ultrasound needle (EBUS) assembly includes a motor coupled to a biopsy needle within a telescoping housing to rotate (such as to oscillate) the needle during tissue harvest to improve harvesting.

Abstract: A telescoping assembly such as an endobronchial ultrasound needle (EBUS) assembly includes a motor coupled to a biopsy needle within a telescoping housing to rotate (such as to oscillate) the needle during tissue harvest to improve harvesting.

Abstract: Systems and methods are provided for automated segmentation of lesions within a region of interest of a patient. At least one magnetic resonance imaging (MRI) image of the region of interest is produced. At least one probability map is generated from the at least one MRI image. A given probability map represents, for each of a plurality of pixels, a likelihood that a lesion is present at the location represented by the pixel given the at least one MRI image of the region of interest. The at least one probability map is combined with a plurality of additional probability maps to provide a composite probability map. Lesions are identified from the composite probability map.

Abstract: Methods of the disclosure may include obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third brain volume (BV) values at the first time point based on two or more images from the first set of medical images and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first, second, third, or fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

Abstract: Systems and methods are provided for automated segmentation of lesions within a region of interest of a patient. At least one magnetic resonance imaging (MRI) image of the region of interest is produced. At least one probability map is generated from the at least one MRI image. A given probability map represents, for each of a plurality of pixels, a likelihood that a lesion is present at the location represented by the pixel given the at least one MRI image of the region of interest. The at least one probability map is combined with a plurality of additional probability maps to provide a composite probability map. Lesions are identified from the composite probability map.

Abstract: A leash assembly includes a body with a handle and a cord which has an end captive in the body. The assembly is adjustable whereby the tension in the cord can be caused automatically to increase and decrease respectively causing a shudder in the cord. Optionally, a noise my accompany the change in tension in the cord. The leash will be attached in use to a collar or harness worn, for example, by a dog and the shudder is used to assist in training the animal.

Abstract: A method of analyzing medical image data to measure brain atrophy and determine the severity and progression of multiple sclerosis or other condition which leads to neurodegeneration or axonal damage is provided. Magnetic resonance image data representing a brain of a subject is provided and a brain surface contour is automatically identified by separating the brain from other connected structures in the image data. A total volume within the brain surface contour is calculated. A brain volume is then determined within the surface contour which excludes fluid filled regions and accounts for partial volume effects. The brain volume is then normalized by the total contour volume which generates a brain parenchymal fraction. The brain parenchymal fraction serves as a reliable measurement of brain atrophy and assists in determining the severity and progression of multiple sclerosis or other condition which leads to neurodegeneration or axonal damage.

Abstract: A system for monitoring debris in a fluid by detecting electrostatic charges associated with the fluid. Both the electrostatic characteristic of the fluid and of the debris are sensed and then separated by a processor that compares their mobility. The monitoring system may be used to detect debris in any fluid capable of being electrostatically charged, such as oil and superheated steam.