A mask for performing an eye exam of a subject includes one or more optically transparent sections for transmitting an incident light beam therethrough and incident on the subject's eye. In some embodiments, the one or more optically transparent sections are coated with an anti-reflective coating configured to reduce reflection of the incident light beam by the one or more optically transparent sections. In some embodiments, the one or more optically transparent sections may have a portion thereof that is tilted with respect to the incident light beam when the mask is optically interfaced with the docking portion of an ophthalmic instrument, such that the incident light beam forms a finite angle of incidence with respect to the corresponding portion of the optically transparent sections.

Embodiments of the present invention provide a computer-implemented method of determining a parameter indicative of dark adaptation of an eye, comprising receiving threshold data from a dark adaptometer indicative of a perception threshold of the eye, fitting first and second models to the threshold data, wherein the first model is associated with a first dark adaptation mechanism and the second model is associated with the first dark adaption mechanism and a second dark adaptation mechanism, determining a confidence associated with the fitting of each of the first and second models to the received threshold data, iteratively repeating the steps of receiving the threshold data and fitting the first and second models in dependence on the determined confidence, and outputting an indication of one or more parameters indicative of dark adaptation of an eye associated with one or both of the first and second models.

The present specification describes methods and systems for modifying a media, such as Virtual Reality, Augmented Reality, or Mixed Reality (VR/AR/MxR) media based on a vision profile and a target application. In embodiments of the specification, a Sensory Data Exchange (SDE) is created that enables identification of various vision profiles for users and user groups. The SDE may be utilized to modify one or more media in accordance with each type of user and/or user group.

A method, a device, and a corresponding computer program product for calculating (optimizing) and producing a spectacle lens with the aid of a semi-personalized eye model. In one approach, the method includes providing personalized refraction data of at least one eye of the spectacles wearer; establishing a personalized eye model in which at least the parameters: shape of an anterior corneal surface of a model eye; a cornea-lens distance; parameters of the lens of the model eye; and lens-retina distance are established using personalized measured values for the eye of the spectacles wearer, and/or using standard values, and/or using the provided personalized refraction data, such that the model eye has the provided personalized refraction data, wherein at least the establishment of the lens-retina distance takes place via calculation.

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.

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.

A Progressive Lens Simulator comprises an Eye Tracker, for tracking an eye axis direction to determine a gaze distance, an Off-Axis Progressive Lens Simulator, for generating an Off-Axis progressive lens simulation and an Axial Power-Distance Simulator, for simulating a progressive lens power in the eye axis direction. The Progressive Lens Simulator can alternatively include an integrated Progressive Lens Simulator, for creating a Comprehensive Progressive Lens Simulation. The Progressive Lens Simulator can be Head-mounted, A Guided Lens Design Exploration System for the Progressive Lens Simulator can include a Progressive Lens Simulator, a Feedback-Control Interface, and a Progressive Lens Design processor, to generate a modified progressive lens simulation for the patient after a guided modification of the progressive lens design. A Deep Learning Method for an Artificial Intelligence Engine can be used for a Progressive Lens Design Processor, Embodiments include a multi-station system of Progressive Lens Simulators and a Central Supervision Station.

Accommodating ophthalmic devices including an ambient light sensor and an accommodation sensor and related methods of use are described. In an example, the accommodation sensor is configured to measure a biological accommodation signal of an eye on or in which the accommodating ophthalmic device is mounted. In an embodiment, the accommodating ophthalmic device is configured to measure the biological accommodation signals based on ambient light, such as based on an intensity or amount of ambient light, incident on the accommodating ophthalmic device. Such ambient light may be measured with the ambient light sensor.

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).

In certain embodiments, double-vision-related vision defects determinations or modifications may be facilitated. In some embodiments, a stimulus may be to be presented at a first time at a position on a first display for a deviating eye of a user (e.g., without a stimulus being presented on a second display of for a reference eye of the user) to cause the deviating eye to fixate on the position on the first display. A deviation measurement for the deviating eye may be determined based on an amount of movement of the deviating eye occurring upon the presentation on the first display for the deviating eye at the first time. In some embodiments, a modification profile associated with the user may be determined based on the deviation measurement, where the modification profile includes one or more modification parameters to be applied to modify an image for the user.