A computing device for diffusion dictionary imaging (DDI) of microstructure and inflammation of a patient is provided. The DDI computing device is connected to other computing devices, such as a magnetic resonance imaging (MRI) scanner. The DDI computing device receives magnetic resonance (MR) signals from the MRI scanner. Once received, the DDI computing device records the one or more MR signals to a memory device. The DDI computing device computationally processes the one or more MR signals to reconstruct a diffusion MRI image using diffusion dictionary data. The MR signals include values that are used as input to algorithms of the DDI data to reconstruct the diffusion MRI image. A database is used to store DDI data, artificial intelligence (AI) data, diffusion dictionary data, and MR data.

A dynamic neuropsychological assessment tool according to an embodiment utilizes speech recognition, speech synthesis and machine learning to assess whether a patient is at risk for a neurological disorder, such as Alzheimer's disease. The dynamic neuropsychological assessment tool enables self-administration by a patient. The tool performs pre-test validation operations on the test environment, test equipment, and the patient's capability for performing the test at that time. The tool also enables dynamic modification of a questionnaire presented to the patient while the patient completes the questionnaire. Also provides the dynamic modification of which tests to present the patient with. The modification can be rule based or modified by a provider. The dynamic neuropsychological assessment tool enables providers and administrators to modify and improve tests and validate them using machine learning based on previously completed assessments and results.

A system and/or method for diagnosing a neurologic condition. The system comprises a wearable head orientation sensor, a wearable eye imaging module, and a display with a visual target. The head orientation sensor is responsive to pitch or yaw head orientation information. The eye imaging module is configured for imaging a characteristic of the retina, sclera, cornea, limbus, or pupil to determine an eye position or eye movement. An electronic circuit in the system determines an ocular parameter measurement in response to the head orientation sensor and the eye imaging module. The neurologic condition is diagnosed in response to the ocular parameter measurement.

Example systems, methods, and apparatus are provided for using data collected from the responses of an individual with the computerized tasks of a cognitive platform to derive performance metrics as an indicator of cognitive abilities, and applying predictive models to generate an indication of neurodegenerative condition. The example systems, methods, and apparatus also can be configured to adapt the computerized tasks to enhance the individual's cognitive abilities, and for using data collected from the responses of an individual with the adapted computerized tasks to derive performance metrics and applying predictive models to generate the indication of neurodegenerative condition.

Methods and systems useful for detecting neurological disorders and for measuring general cognitive performance, in particular by measuring eye movements and/or pupil diameter during eye-movement tasks.

An estimation system includes: a first sensor that detects a first amount of activity that is an amount of activity of a subject in a room; a second sensor that detects a second amount of activity that is an amount of activity of the subject on a bed in the room; and an estimation device that estimates at least one of a position and an action of the subject in association with a position in the room based on the first amount of activity detected by the first sensor and the second amount of activity detected by the second sensor, and outputs an estimation result obtained from the estimation. The first sensor and the second sensor are sensors other than two-dimensional image sensors.

A method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive impairment and predict its future change based on the at least one feature obtained.

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

A tactile stimulus presentation device includes a stage, a contact member, a drive mechanism, an information setting part, and a control part. The stage includes an opening and an arrangement surface, the arrangement surface on which a body part of a subject is arranged thereon so as to close the opening. The contact member is for giving a tactile stimulus to the subject by being in contact with the body part of the subject arranged on the arrangement surface of the stage through the opening. The drive mechanism is a mechanism for holding the contact member and moving the contact member in a direction substantially parallel to the arrangement surface and in a direction substantially perpendicular to the arrangement surface. The information setting part is configured to set information to be presented by gibing tactile stimulation to the subject by tactile stimulation. The control part is configured to control the drive mechanism to present the information set by the information setting part by giving tactile stimulation to the body part of the subject arranged in abutment on the arrangement surface.

A feature set for performing classification of datasets such as speech transcripts by a machine learning classifier model is constructed using identification of features of interest through classification of subsequences of the dataset. An anchor comprising a class-differentiating token is identified, and subsequences of different lengths comprising the anchors and surrounding tokens are generated. The subsequence length producing a best performing classifier is selected. A feature set is then generated using transcript-level aggregates of token-level features for tokens in the dataset within that subsequence lengths length. The feature set may be added to a previously defined feature set for the dataset.