Methods, systems and non-transitory computer readable storage media of electric field treatment planning of a target tissue. A method of electric field treatment planning of a target tissue site includes: (a) obtaining an image of the target tissue site, (b) determining volume and one or more electric properties of the target tissue site, (c) using the volume and the one or more electric properties to: (i) determine a number of electrodes to treat the target tissue site with electric fields, each electrode having one or more contacts, (ii) determine a placement of the number of electrodes within the target tissue site, and (iii) relative to one of the contacts in one of the number of electrodes at the placement determined in (ii), determine an electric field that results in a prescribed electric field coverage of the target tissue site.

A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.

A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.

A support device for an endoscope comprises a tubular member configured for removable attachment to an outer surface of the endoscope near, or at, its distal end and a plurality of projecting elements extending outward from the outer surface of the tubular member and circumferentially spaced from each other. The device includes an optically transparent cover coupled to the tubular member and configured for covering the distal end of the endoscope when the tubular member is attached to the outer surface of the endoscope. The projecting elements provide support for the endoscope, improve visualization and center the scope as it passes through a body lumen, such as the colon. In addition, the cover seals the distal end of the endoscope to protect the scope and its components from debris, fluid, pathogens and other biomatter.

A method for supplementing scan data by using library data according to the present invention comprises: a scan step of acquiring scan data of a subject including a structure; a step of selecting library model data corresponding to the structure; and a step of post-processing the scan data, wherein the library model data is added at the step of post-processing of the scan data and is post-processed together with the scan data. Accordingly, the scan data in which distortion or a data gap has occurred is supplemented by the library model data, and thus a user acquires a highly reliable three-dimensional model.

Systems and associated methods are provided for monitoring a user while operating a computing device and providing active feedback to said user regarding health and safety best practices associated with operating said computing device. The methods comprise obtaining user biometric data; converting said biometric data into actionable instances of health and safety user device operation use cases; and interacting with the user based on said actionable instances in order to improve or remedy any deviations from recommended health and safety user device operation practices.

A handwash monitoring system includes: an imaging device; and a processor. The processor detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging device before handwashing, and detects a second candidate abnormality existing in the hand of the user from a second image captured by the imaging device after the handwashing. The processor determines a type of an abnormality on the hand of the user based on a difference between a shape of the first candidate abnormality and a shape of the second candidate abnormality wherein the first candidate abnormality and the second candidate abnormality are detected from an identical region.

A method for providing a therapy plan includes receiving a dataset. The dataset maps an examination object including a first tissue area at at least one first timepoint. The first tissue area is identified in the dataset as a first state. A second state is specified for the first tissue area. A therapy plan for a therapy apparatus is determined by application of a model to input data. A change in the first tissue area between the at least one first timepoint and at least one second timepoint is createable by the control of the therapy apparatus in accordance with the therapy plan. The model maps the first state to the second state and/or the second state to the first state. The therapy plan is provided as output data of the model.

Embodiments of architecture, systems, and methods to develop a learning/evolving system to robotically perform and model one or more activities of a medical procedure where the medical procedure may include diagnosing a patient's medical condition(s), treating medical condition(s), and robotically diagnosing a patient's medical condition(s) and performing one or more medical procedure activities based on the diagnosis without User intervention where the activities may be performed in computer-based environment formed by the learning/evolving system, live, or a combination thereof.

Disclosed are a system and a method for ultrasound elastography and a method for dynamically processing frames in real time. The system includes an elasticity processing apparatus having an elasticity information detecting module for extracting elasticity information representing the elasticity of a target to be detected; a quality parameter calculating module for calculating at least a quality parameter reflecting quality of each elasticity image corresponding to the elasticity information; and a frame processing module for determining whether to output corresponding elasticity image based on the quality parameter of each elasticity image. When calculating a strain of consecutive images, the parameter reflecting the quality of each image is also computed, through which, the current elasticity image is determined whether to be displayed, thus avoiding the situation that colors of acquired successive elasticity images may vary greatly due to large difference existing in stress.