The present disclosure relates to systems and methods for characterizing a behavior change of a process. A behavior model that can include a set of behavior parameters can be generated based on behavior data characterizing a prior behavior change of a process. A stimulus parameter for a performance test can be determined based on the set of behavior parameters. An application of the performance test to the process can be controlled based on the stimulus parameter to provide a measure of behavior change of the process. Response data characterizing one or more responses associated with the process during the performance test can be received. The set of behavior parameters can be updated based on the response data to update the behavior model characterizing the behavior change of the process. In some examples, the behavior model can be evaluated to improve or affect a future behavior performance of the process.

The present invention relates to a cognitive function test server, including a communication interface, a memory; and a processor which is operably connected to the communication interface and the memory, and the processor is configured to provide a first sequence to acquire brainwave data of a user in a resting state by means of an HMD device, acquire baseline brainwave data of the user based on the first sequence, provide at least one second sequence related to a cognitive function by means of the HMD device, acquire input data and activated brainwave data based on the second sequence from the HMD device and an input device connected thereto, and generate a cognitive evaluation result of the user based on at least one of the reference brainwave data, the activated brainwave data, and the input data of the user.

Provided herein are devices (e.g., electrochemical sensors useful for detecting volatile organic compounds associated with certain diseases or conditions and/or diagnosing certain diseases or conditions). The devices comprise one or more layers of metal on a layer of silicon, and a layer of molecularly imprinted polymer in electrical communication with the one or more layers of metal, wherein the one or more layers of metal are each independently selected from a layer of chromium, platinum, gold, nickel, cobalt, tungsten, rhodium, iridium, silver, tin, titanium or tantalum, or an alloy thereof. Methods of using the devices (e.g., to detect one or more analytes in a sample, to detect and/or diagnose a disease or condition in a subject), and methods of making the devices are also provided.

The present disclosure provides systems and methods for diagnosing disease. In some aspects, an imaging system is provided that includes a light source configured to illuminate a retina of the eye with light, one or more imaging devices configured to receive light returned from the retina to generate one or more spatial-spectral images of the retina, and a computing device configured to receive the one or more spatial-spectral images of the retina, evaluate the one or more spatial-spectral images, and identify one or more biomarkers indicative of a neurogenerative pathology.

The present disclosure provides methods and an apparatus for imaging and analysing images of presumed protein deposits in the retina, retinal tissue or retinal structures and discloses methods differentiating or classifying these deposits and other optical signals from retinal structures into 1) whether they contain or do not contain classes, of proteins or protein deposits called amyloids or other proteins and/or protein deposits related to neurodegenerative eye and brain disease(s); 2) which type(s) of amyloid or other proteins or protein deposits they contain, as well as 3) whether the form and/or properties of the deposit are associated with a class of diseases or with one or another specific condition(s) (or disease(s)); whether or not this is a disease or class of disease associated with the retina or more generally with the nervous system, including the brain or 4) classified as associated with one or another level of severity of condition(s), or disease(s).

A brain imaging system and a brain imaging method are provided. The brain imaging system includes a first imaging device, a second imaging device and a processor. The first imaging device captures a first brain image set by scanning a patient, and the second imaging device captures a second brain image set. The processor is configured to: pre-process and enhance first and second brain image sets; select first features that are optimal for estimating cerebral perfusion and select second features that are optimal for brain lesion identification; obtain, by performing calculations on first features, a plurality of brain perfusion indices; and identify, by inputting the second features to a third deep learning model having been trained, position information and volume information of one or more target brain lesions in the brain of the patient.

A computer-implemented cognitive assessment tool is provided for assessing cognitive ability of an individual while multi-tasking. In one embodiment, a computer processing system on which the tool is implemented may receive form the individual first responses to a first task and second responses to a second task, where the first task and the second task are presented to the individual simultaneously. The system may determine that the first task and the second task are performed by the individual based on the first responses and the second responses, and compute a cognitive measure using one or both of the first responses and the second responses. Further, computing the cognitive measure may be based on performance measures of one or both of the first responses and the second responses. Based on the cognitive measure, the system may output a cognitive assessment to the individual.

An information processing device (14) extracts a feature value which is an acoustic parameter from audio data. The information processing device (14) generates a spectrogram image of the audio data. The information processing device (14) calculates, on the basis of the feature value and a calculation model, a first score which indicates the extent of a user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) inputs the spectrogram image to a learned model, and calculates a second score which indicates the extent of the user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) combines the first score and the second score to calculate a combined score which indicates the extent of the user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) estimates whether the user has any of the disorders or symptoms according to the combined score.

In order to determine a degree of dementia of a user, contents are output through a user terminal, a voice of the user for a content acquired by a microphone of the user terminal is received, a spectrogram image is generated by visualizing the voice, and the degree of dementia of the user is determined by means of a convolutional neural network (CNN) and a deep neural network (DNN) based on the spectrogram image.

A brain image analysis apparatus (2000) acquires input data (40) including a structural brain image (42) and a functional brain image (44) for a subject (10). The brain image analysis apparatus (2000) obtains analysis data (20) by inputting the input data (40) into an analysis model (2020). The analysis model (2020) has been trained in advance so as to output the analysis data (20) representing information about brain dysfunction in response to an input of the input data (40). The brain image analysis apparatus (2000) outputs, based on the analysis data (20), output data (30) representing information about the brain dysfunction of the subject (10).