This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.

An ambulance-compatible magnetic resonance imaging (MRI) system for on-site emergency diagnosis includes a mid-field super-conducting head-only magnet including a bore and an active shield arranged relative to the magnet, a passive shield arranged relative to the magnet, the passive shield including a first flange arranged adjacent to a first side of the magnet bore, a second flange arranged adjacent to a second side of the magnet bore, wherein the first flange and the second flange are electrically connected to each other, and wherein the passive shield is operative to capture flux extending out from the magnet bore and return the flux to the magnet. An asymmetric head gradient assembly for generating magnetic gradient field in the mid-field super-conducting magnet is also provided, the magnetic gradient field being between 100-150 mT/m or having a slew rate between 400-800 T/m/s. The MRI system includes a receiver coil and a controller operatively coupled to the receive coil, the controller configured to produce an image based on data obtained from the receive coil. The MRI system is mountable in an ambulance vehicle.

The degree of progress of an atrial fibrillation which a subject suffers from is assessed and learned on the basis of a frequency histogram according to an occurrence frequency based on judgment results of a complex fractionated atrial electrogram for a specified period of time, which are obtained from the subject's electrocardiogram data.

A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Disclosed is a system and method for performing measurements on a biological subject, and in one particular example, to performing measurements of analytes in a biological subject by breaching a functional barrier of the subject using microstructures, wherein the one or more microstructures include an aptamer for binding one or more analytes.

Embodiments herein relate to systems, devices and methods for fitting hearing assistance devices. In a first aspect, a method of fitting a hearing assistance device is included, the method including providing an audio sample to a hearing assistance device wearer, receiving input from the hearing assistance device wearer regarding a preferred sound volume or perceived loudness, receiving input from the hearing assistance device wearer with the external device regarding a bass/treble balance, and determining a maximum power output of the hearing assistance device that does not exceed a loudness discomfort level (LDL). Other embodiments are also included herein.

A method and system for remote diagnosis of a congestive heart failure in humans is presented. The method includes receiving a facial image of a patient, wherein the facial image is retrieved from a data store and captured from the patient; extracting, from the facial image, at least one facial feature indicative of a heart condition; classifying the extracted at least one facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a plurality of scores indicating a stage of the heart condition; and determining a positive diagnosis and the stage of the heart condition of the patient based on the plurality of scores.

Methods and systems are provided for analyzing diffusion weighted images (DWI) using impeded diffusion fraction models for quantitative imaging diagnostic assay of cancer, such as glandular tissue cancers. The Impeded diffusion fraction models are tissue and cancer independent and generate a single score representative of multi-compartment diffusion fractions occurring within each voxel of a DWI image.