Method and system for a non-invasive assessment of a relation between an intracranial pressure and an intraocular pressure. The method comprising the steps of recording a plurality of images of a retina part of an eye of a person, identifying at least one vein, determining a first plurality of characteristic vein diameters for the identified vein at a first vein location, determining whether the at least one vein has experienced a vein collapse during the first time period, and determining a relation between intraocular pressure and intracranial pressure during the first time period.

Method and system for a non-invasive assessment of a relation between an intracranial pressure and an intraocular pressure. The method comprising the steps of recording a plurality of images of a retina part of an eye of a person, identifying at least one vein, determining a first plurality of characteristic vein diameters for the identified vein at a first vein location, determining whether the at least one vein has experienced a vein collapse during the first time period, and determining a relation between intraocular pressure and intracranial pressure during the first time period.

System for integrally measuring clinical parameters of the visual function including a display unit (20) for representing a scene with a 3D object having variable characteristics such as virtual position and virtual volume of the 3D object within the scene; movement sensors (60) for detecting the user head position and distance from the display unit (20); tracking sensors (10) for detecting the user pupils position and pupillary distance; an interface (30) for the user interaction on the scene; processing means (42,44) for analysing the user response based on the data coming from sensors (60,10) and the interface (30), with the characteristics variations of the 3D object; and based on the estimation of a plurality of clinical parameters of the visual function related to binocularity, accommodation, ocular motility and visual perception.

In described embodiments, a device and method for diagnosing brain and neurological issues is provided. The device measures the performance of Convergence, Divergence, and binocular tracking capabilities of a subject's eyes, which can be used to determine whether a subject has experienced a brain or other neurological event.

Disclosed is a method of identifying dementia using at least one processor of a device according to some embodiments of the present disclosure. More particularly, the method may include performing a first task of causing for a user terminal to display a first screen including a sentence; performing a second task of causing for the user terminal to acquire an image including user’s eyes in association with displaying a moving object instead of the first screen; and performing a third task of causing for the user terminal to acquire a recording file in association with displaying a second screen in which the sentence is hidden, wherein the first task includes a sub-task of causing color of at least one word constituting the sentence included in the first screen to be sequentially changed.

Disclosed is a method of identifying dementia using at least one processor of a device according to some embodiments of the present disclosure. More particularly, the method may include performing a first task of causing for a user terminal to display a first screen including a sentence; performing a second task of causing for the user terminal to acquire an image including user’s eyes in association with displaying a moving object instead of the first screen; and performing a third task of causing for the user terminal to acquire a recording file in association with displaying a second screen in which the sentence is hidden, wherein the first task includes a sub-task of causing color of at least one word constituting the sentence included in the first screen to be sequentially changed.

In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

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 invention relates to an examination device (1), a method for an automated examination of at least one eye (4, 4′) of a person, a computer program product and the various uses of the examination device (1).

The invention relates to an examination device (1), a method for an automated examination of at least one eye (4, 4′) of a person, a computer program product and the various uses of the examination device (1).