Methods and devices determine an eye strain indicator. In one aspect, an augmented reality (AR) device wearable by a user includes an image sensor and a processor coupled to the image sensor. The processor receives image data from the image sensor, determine that a display is within a field of view (FOV) of the AR device, determine an eye strain indicator based on the determination that the display is within the FOV of the AR device, and provide the eye strain indicator to the user.

Provided is an image processing apparatus configured to process an image of a fundus of an eye to accurately measure thicknesses of membranes that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points; and a measuring position acquiring unit configured to identify a measuring position regarding the wall of the blood vessel based on a position of the identified cell.

A slit lamp microscope of an aspect example includes a scanner and a data processor. The scanner is configured to scan an anterior segment of a subject's eye with slit light to collect a plurality of cross sectional images. The data processor is configured to generate opacity distribution information that represents a distribution of an opaque area in a crystalline lens, based on the plurality of cross sectional images collected by the scanner.

A system for displaying an intraoperative user interface for a surgical procedure is disclosed. The system comprises an arthroscopic imaging device, a display device, an input device, a processor, and a non-transitory, computer-readable medium. The arthroscopic imaging device is configured to capture one or more images of a patient anatomy within a joint of a patient. The input device is configured to receive input from a user. The processor is configured to receive the images from the arthroscopic imaging device and display a user interface on the display device that includes the images from the arthroscopic imaging device. The processor is also configured to receive the input from the input device, display one or more visual overlays over the one or more images based on the input, and apply one or more digital markers in a fixed location on the one or more images based on the input.

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.

Examples relate to a surgical microscope system, and to a system, a method and a computer program for a surgical microscope system. The system comprises one or more processors and one or more storage devices. The system is configured to obtain intraoperative sensor data of at least a portion of an eye from a Doppler-based imaging sensor of the surgical microscope system. The system is configured to process the intraoperative sensor data to determine information on a blood flow within the eye. The system is configured to generate a visualization of the blood flow. The system is configured to provide a display signal to a display device of the surgical microscope system based on the visualization of the blood flow within the eye.

A fundus imager includes a handheld housing that supports a lighting unit configured to illuminate an eye fundus. The lighting unit includes one or more light-emitting diodes. The housing further supports a camera configured to capture one or more images of the eye fundus, and a display configured to display the one or more images of the eye fundus. The fundus imager captures at least one multispectral fundus image using the camera, and displays the at least one multispectral fundus image on the display.

A processor of an ophthalmologic image processing device acquires an ophthalmologic image photographed by an ophthalmologic image photographing device. The processor inputs the ophthalmologic image into a mathematical model trained by a machine learning algorithm to acquire a result of an analysis relating to at least one of a specific disease and a specific structure of a subject eye. The processor acquires information of a distribution of weight relating to an analysis by a mathematical model, as supplemental distribution information, for which an image area of the ophthalmologic image input into the mathematical model is set as a variable. The processor sets a part of the image area of the ophthalmologic image, as an attention area, based on the supplemental distribution information. The processor acquires an image of a tissue including the attention area among a tissue of the subject eye and displays the image on a display unit.

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 ophthalmic image processing apparatus includes a processor. The processor acquires a first image as a color fundus image, and corrects a pixel value of at least any color component in the first image, based on color gamut information for specifying a predetermined color gamut to be applied to a color fundus image, to generate a color gamut-corrected image.