An aspect of the present invention relates to a diagnostic assistance device for acquiring diagnostic assistance information by using a neural network model and based on an eye image, the diagnostic assistance device comprising: an eye image acquisition unit for acquiring a target eye image; and a processing unit for acquiring diagnostic assistance information by using a neural network model learned to acquire diagnostic assistance information and based on the target eye image. The neural network model includes first diagnostic assistance neural network model and second diagnostic assistance neural network model for acquiring second diagnostic assistance information. The first diagnostic assistance neural network model includes first common portion for acquiring first feature set and first individual portion for acquiring first diagnostic assistance information, and the second diagnostic assistance neural network model includes first common portion for acquiring first feature set and second individual portion for acquiring second diagnostic assistance information.

Systems and methods for a microfluidic biosensor patch and health monitoring system may include an iontophoresis module, a multi-inlet microfluidic sweat collection and sampling module, and a molecularly imprinted polymer (MIP) organic compound sensor module. An iontophoresis module may provide for stimulation of a biofluid sample. A biofluid may be a sweat sample. Stimulation may be achieved via electrostimulation and/or application of a stimulating agent. A microfluidic sweat collection and sample module may include several adhesive layers with carefully designed inlets, channels, a reservoir, and an outlet for the efficient collection and sampling of biofluid. A MIP sensor module may quickly and accurately identify concentrations of key metabolites present in a biofluid sample which may indicate certain health conditions.

The present application provides for use of a somatosensory evoked potential (SEP) during the hyperacute stroke phase as marker for predicting the outcome of endovascular treatment in a patient suffering from acute ischemic stroke, wherein when the SEP ipsilateral to the stroke site has an amplitude from 60% to 100% with respect to the corresponding SEP contralateral to the stroke site this is indicative of good outcome of the endovascular treatment, whereas when the SEP ipsilateral to the stroke site has an amplitude from 0% to 20% with respect to the corresponding SEP contralateral to the stroke site this is indicative of bad outcome of the endovascular treatment.

A set of information including manually entered health-related data for an individual or user, automatically collected health-related data for the individual or user, and test results for the individual or user may be received. In response to receiving the set of information, the manually entered health-related data for the individual or user, the automatically collected health-related data for the individual or user, and the test results for the individual or user may be integrated into a comprehensive health profile for the individual or user. Upon a request made on behalf of the individual or user, at least part of the comprehensive health profile may be provided to the individual or user.

A system and method for automated localization, enumeration, and diagnoses of a tooth/condition. The system detects a condition for at least one defined localized and enumerated tooth structure within a cropped image from a full mouth series based on any one of a pixel-level prediction, wherein said condition is detected by at least one of detecting or segmenting a condition on at least one of the enumerated tooth structures within the cropped image by a 2-D R-CNN.

An application for tracking disease, pain, and mental health symptom triggers including an input module for inputting variables from a user in electronic communication with an output variable module, an analysis module for analyzing input variables and output variables, and an output module for presenting results to the user. A method of tracking disease, pain, and mental health triggers, by a user inputting data about nutrition, medication, lifestyle, symptoms, pain, and user defined metrics in an application, performing an analysis on the data, and outputting a result from the data identifying daily activities that effect the user's disease/mental health and trigger symptoms. A method of preventing adverse events.

The present invention relates to a smart wound management system and to a method of medical treatment and management for wounds.

One aspect of the present disclosure relates a method of diagnosis and/or treatment of a solid tumor. The method includes directly measuring neural activity within a solid tumor for a time and determining a state of the solid tumor based on the neural activity. The diagnosis and/or treatment can be determined based on the state of the solid tumor. In some instances, the neural activity can be used in a closed loop to detect the neural activity, determine the state, determine the risk, apply treatment, check again for neural activity, and cease treatment when the neural activity is gone.

In accordance with one embodiment, a method for determining changes in health of a user is disclosed. The method includes sensing multi-dimensional motion of a body part of a user to generate a first multi-dimensional motion signal at a first time and date; in response to the first multi-dimensional motion signal, generating a first neuro-mechanical fingerprint; generating a first health measure in response to the first NFP and user calibration parameters; sensing multi-dimensional motion of the body part of the user to generate another multi-dimensional motion signal at another time and date; in response to the another multi-dimensional motion signal, generating another neuro-mechanical fingerprint; generating another health measure in response to the another NFP and the user calibration parameters; and comparing the first health measure with the another health measure to determine a difference representing the health degradation of the user.

The present disclosure relates to systems and methods for assessing or predicting the status of wounds such as burns. Systems can include at least one light detection element and one or more processors configured to receive a signal from the at least one light detection element representing light reflected from a tissue region, generate an image having a plurality of pixels depicting the tissue region, and determine a burn status of a subset of pixels of the image using one or more deep learning algorithms. Systems can further be configured to generate a classified image of the tissue region and/or determine a predictive score associated with healing of the wound.