Systems and methods may be used to predict an applicable a hearing assistance device shell or model. For example, a method may include obtaining patient information, determining, using a machine learning trained model, a correlation between an input vector and each of a plurality of feature vectors corresponding to a plurality of hearing assistance device models, and ranking the plurality of hearing assistance device models based on respective correlations to the input vector. Information corresponding to a highest ranked hearing assistance device model may be output.

Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.

Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.

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 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.

Provided are computer-implemented methods and systems for generating and/or utilizing data analysis algorithm(s) for predicting and/or detecting a clinical condition related to insertion of a medical instrument toward a target in a body of a patient based, inter alia, on data related to an automated medical device and/or to operation thereof.

The disclosure provides methods that facilitate disease management by providing for early detection of metabolic changes that differentiate critically ill-COVID-19 patients under mechanical ventilation at the intensive care unit (ICU) who are likely to exhibit faster recovery.

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.

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.

A method includes, inserting a catheter into a cavity of a patient organ for performing a medical procedure, the cavity is of a given cavity type. A partial anatomical mapping of the cavity is performed by visiting one or more anatomical points on a surface of the cavity, using the catheter. Based on the partial anatomical mapping, a Graphical User Interface (GUI) template is selected, which is specified for applying the medical procedure to the given cavity type. The selected GUI template is presented to a user for performing the medical procedure in the cavity.