Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

In a general aspect, a structured optical beam with position-dependent polarizations is prepared for human observation. In some examples, an optics method includes processing an optical beam to produce a structured optical beam for human observation. Processing the optical beam includes receiving the optical beam from a laser source; attenuating the optical beam to an exposure irradiance level that is safe for direct viewing by a human eye; expanding the optical beam to a size configured for a field of view of the human eye; and preparing the optical beam with a position-dependent polarization profile. The structured optical beam, which has the position-dependent polarization profile, is directed towards an observation region for human observation.

Disclosed is an eye examination apparatus that can be used in professional settings. The eye examination apparatus has a body having a first eye opening and a second eye opening for a user to see into the eye examination apparatus using two eyes. The eye examination apparatus also has a first camera coupled to the body and positioned to acquire ophthalmic images through the first eye opening, and a second camera coupled to the body and positioned to acquire ophthalmic images through the second eye opening. The eye examination apparatus also has at least one display coupled to the body and positioned to be viewable through the first eye opening and the second eye opening.

Disclosed is an eye examination apparatus that can be used in professional settings. The eye examination apparatus has a body having a first eye opening and a second eye opening for a user to see into the eye examination apparatus using two eyes. The eye examination apparatus also has a first camera coupled to the body and positioned to acquire ophthalmic images through the first eye opening, and a second camera coupled to the body and positioned to acquire ophthalmic images through the second eye opening. The eye examination apparatus also has at least one display coupled to the body and positioned to be viewable through the first eye opening and the second eye opening.

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.

Machine learning technologies are used to identify and separating abnormal and normal subjects and identifying possible disease types with images (e.g., optical coherence tomography (OCT) images of the eye), where the machine learning technologies are trained with only normative data. In one example, a feature or a physiological structure of an image is extracted, and the image is classified based on the extracted feature. In another example, a region of the image is masked and then reconstructed, and a similarity is determined between the reconstructed region and the original region of the image. A label (indicating an abnormality) and a score (indicating a severity) can be determined based on the classification and/or the similarity.

Disclosed is a method for determining an eye parameter of a user of a display device, the eye parameter relating to a dioptric parameter of an ophthalmic lens to be provided to the user, the method including: a display device providing step, during which a binocular display device is provided to the user,—an image display step, during which an image is displayed to the user when using the display device; a display parameter modifying step, during which at least one parameter of the display device is modified so as to modify the virtual display distance of the perceived image, wherein the display parameter modifying step is repeated until image subjective quality of the perceived image is perceived by the user as optimal; and an eye parameter determining step during which an eye parameter is determined based on the parameter of the display device.

Disclosed is a method for determining an eye parameter of a user of a display device, the eye parameter relating to a dioptric parameter of an ophthalmic lens to be provided to the user, the method including: a display device providing step, during which a binocular display device is provided to the user,—an image display step, during which an image is displayed to the user when using the display device; a display parameter modifying step, during which at least one parameter of the display device is modified so as to modify the virtual display distance of the perceived image, wherein the display parameter modifying step is repeated until image subjective quality of the perceived image is perceived by the user as optimal; and an eye parameter determining step during which an eye parameter is determined based on the parameter of the display device.

Systems and methods are disclosed for evaluating human eye tracking. One method includes receiving data representing the location of and/or information tracked by an individual's eye or eyes before, during, or after the individual performs a task; identifying a temporal phase or a biomechanical phase of the task performed by the individual; identifying a visual cue in the identified temporal phase or biomechanical phase; and scoring the tracking of the individual's eye or eyes by comparing the data to the visual cue.

An eyesight training device for cognition correction which includes a display unit; a user input unit; and a controller for controlling the display unit to overlap a recognition restraining image, which restrains a visual recognition of a recognition image, with the recognition image and to gradually change a concentration value of the recognition restraining image, and for storing, as recognition level information, information corresponding to a concentration value of the recognition restraining image as of a time when a visibility change confirmation input is input through the user input unit of the visibility change confirmation input is input.