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.

A system and method for conducting a refractive examination of an eye of a patient, has a communication device with a communication module that connects to the internet, a processor that is programmed to connect to a remote computer via the communication module and which has a display screen, a microphone and a speaker. The remote computer has a data storage device that stores images of eye charts. The communication device is mounted in a virtual reality headset configured to be worn by the patient and has at least one screen through which the display screen of the communication device is viewable. The communication device displays images in the form of the eye charts to the patient, who communicates through the communication to a remote examiner who conducts the refractive examination using multiple different eye charts to determine the prescription of the patient.

Systems and methods according to present principles use touchscreen-based devices such as tablet computers or other computers incorporating touchscreens to both run the test and to receive input/output. It will be understood that any such device may be employed, so long as a display, means for user input, and means for eye tracking, are provided, and so long as its display screen is large enough to effectively test visual field.

Systems and methods according to present principles use touchscreen-based devices such as tablet computers or other computers incorporating touchscreens to both run the test and to receive input/output. It will be understood that any such device may be employed, so long as a display, means for user input, and means for eye tracking, are provided, and so long as its display screen is large enough to effectively test visual field.

The present disclosure relates to an apparatus (100) for passive scanning of an object. The apparatus comprises a distance sensing unit (110) adapted to measure distances to a plurality of points of the object, an orientation sensing unit (120) adapted to determine orientations of the distance sensing unit (110), and a processor (140) adapted to derive information about the object or a scene in which the object is used based on the measured distances and orientations of the distance sensing unit (110).

The present disclosure relates to an apparatus (100) for passive scanning of an object. The apparatus comprises a distance sensing unit (110) adapted to measure distances to a plurality of points of the object, an orientation sensing unit (120) adapted to determine orientations of the distance sensing unit (110), and a processor (140) adapted to derive information about the object or a scene in which the object is used based on the measured distances and orientations of the distance sensing unit (110).

System for customizing visual field (VF) tests uses a machine learning model (15) trained on retina images (12A, 12C, 12D), including optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), fundus, and/or fluorescein angiography images. In operation, in preparation for administering a specific VF test (13) to a patient, a retina image of the patient is submitted to the present machine model, which responds by synthesizing a VF prediction for the patient. The synthesized VF may be used to optimize the specific VF test prior to administering it to the patient.

System for customizing visual field (VF) tests uses a machine learning model (15) trained on retina images (12A, 12C, 12D), including optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), fundus, and/or fluorescein angiography images. In operation, in preparation for administering a specific VF test (13) to a patient, a retina image of the patient is submitted to the present machine model, which responds by synthesizing a VF prediction for the patient. The synthesized VF may be used to optimize the specific VF test prior to administering it to the patient.

Disclosed techniques evaluate the visual field of a patient's eye using deep learning techniques. A computer system obtains a plurality of cross-sectional scan images of a retina captured by an optical coherence tomography (OCT) device. The retina has an inner retinal layer. A retinal optical texture analysis (ROTA) map of the inner retinal layer is generated from the plurality of cross-sectional scan images. The ROTA map includes a plurality of pixels, and each pixel of the ROTA map corresponds to a respective optical texture signature value S providing information about tissue composition and optical density of the inner retinal layer at a respective retinal location. The computer system applies a machine learning model to process the ROTA map of the inner retinal layer to determine visual field sensitivity of the retina.

A method for performing digital measurements by obtaining a first video stream of a user at a first distance to a camera; using an element appearing in the first video stream to generate a transformation factor to convert pixel distance in the first video stream to actual physical distance in the real world; using the transformation factor to obtain a first digital measurement in the first video stream; obtaining a second video stream at a second distance, larger than the first distance; using the first digital measurement and an angular measurement to an item appearing in the second video stream to determine a measurement of the second distance.