A method of determining a predisposition to contact lens discomfort in a patient is provided, comprising determining a detection threshold of the patient by delivering a cool mechanical stimulus to the cornea of the patient, optionally applying a series of cool mechanical stimuli to the cornea of the patient, and classifying the patient as being predisposed to contact lens discomfort if the detection threshold is at or below a cut-off value predetermined to be associated with predisposition to contact lens discomfort and/or the patient does not adapt to the series of cool mechanical stimuli.

The present disclosure provides systems and methods for characterizing thin films, such as, for example, diagnosis of dry eye syndrome, thin-film optical metrology testing, etc. The present disclosure may be embodied as a tearscope, which is sometimes referred to herein as a Macroscopic Imaging Ellipsometer (MIE) because it may be considered to be an imaging ellipsometer with, for example, a field-of-view on the order of tens of millimeters or more (i.e., macroscopic), compared with conventional imaging ellipsometers, which usually employ microscope objectives and have fields-of-view of up to several hundred microns.

A method, a computer program and a device for evaluating a tear fluid layer non-invasively. The method, computer program, and device include a step of detecting a time when color information in a predetermined region in a tear fluid layer interference fringe image changes by a threshold value or more as an eyelid opening time, a step of extracting an image at the time when the predetermined time has passed from the eyelid opening time as an extracted image from the tear fluid interference fringe images, a determination value calculation step of calculating a determination value from the color information in a pixel in a local region by scanning a predetermined target region, a step of detecting a corresponding pixel by comparing the calculated determination value and a threshold value, and an evaluation step of evaluating tear fluid layer breakdown by comparing the detected pixel and a threshold value.

An ophthalmologic apparatus includes: an ophthalmologic apparatus body having: an objective lens that faces a subject's eye; a first illumination optical system that irradiates a cornea of the subject's eye with illumination light emitted from a first illumination light source along an optical axis overlapping an optical axis center of the objective lens; an interference image capturing camera that takes an image of a corneal reflection light through the objective lens and outputs an imaging signal; and a calculation unit that calculates, based on a corneal reflection image, of a corneal reflection light, input from the interference image capturing camera, a thickness of a tear fluid film at each position on the corneal surface; and a guide rail that supports the ophthalmologic apparatus body. The guide rail supports the ophthalmologic apparatus body in a rotatable manner such that an optical axis center of the objective lens is positioned obliquely with respect to a horizontal direction orthogonal to a gravity direction.

Devices and approaches for activating cross-linking within corneal tissue to stabilize and strengthen the corneal tissue following an eye therapy treatment. A feedback system is provided to acquire measurements and pass feedback information to a controller. The feedback system may include an interferometer system, a corneal polarimetry system, or other configurations for monitoring cross-linking activity within the cornea. The controller is adapted to analyze the feedback information and adjust treatment to the eye based on the information. Aspects of the feedback system may also be used to monitor and diagnose features of the eye 1. Methods of activating cross-linking according to information provided by a feedback system in order to improve accuracy and safety of a cross-linking therapy are also provided.

Apparatus and methods are described for performing tear film structure measurement on a tear film of an eye of a subject. A broadband light source (100) is configured to generate broadband light. A spectrometer (250) is configured to measure a spectrum of light of the broadband light that is reflected from at least one spot on the tear film, the spot having a diameter of between 100 microns and 240 microns. A computer processor (28) is coupled to the spectrometer and configured to determine a characteristic of the tear film based upon the spectrum of light measured by the spectrometer. Other applications are also described.

Eyelid illumination systems and methods for imaging meibomian glands for meibomian gland analysis are disclosed. In one embodiment, a patient's eyelid is IR trans-illuminated with an infrared (IR) light. A trans-illumination image of the patient's eyelid is captured, showing meibomian glands in dark outlined areas, whereas non-gland material is shown in light areas. This provides a high contrast image of the meibomian glands that is X-ray-like. The lid trans-illumination image of the meibomian glands can be analyzed to determine to diagnose the meibomian glands in the patient's eyelid. The eyelid may be trans-illuminated by a lid-flipping device configured to grasp and flip the eyelid for imaging the interior surface of the eyelid. Also, an IR surface meibography image of the meibomian glands may also be captured and combined with the trans-illumination image of the meibomian glands to provide a higher contrast image of the meibomian glands.

In classifying images by machine learning, provided are an image classification method, device, and program for classifying the image from which the feature difference is hardly detected, in particular, classifying the interference fringe image of tear fluid layer by the dry eye types. The method includes a step of acquiring a feature value from an interference fringe image of tear fluid layer for learning, a step of constructing a model for classifying an image from the feature value acquired from the interference fringe image of tear fluid layer for learning, a step of acquiring the feature value from an interference fringe image of tear fluid layer for testing, and a step of performing classification processing for classifying the interference fringe image of tear fluid layer for testing by types of dry eye using the model and the feature value acquired from the interference fringe image of tear fluid layer.

Ocular surface interferometry devices, systems, and methods are disclosed for imaging an ocular tear film. An imaging device can be focused on the lipid layer of the tear film to capture optical wave interference interactions of specularly reflected light from the tear film combined with a background signal(s) in a first image, wherein the specularly reflected light may be produced from various portions of the ocular tear film by obliquely illuminating various portions of the ocular tear film with a multi-wavelength light source, such as in a tiling pattern(s). The imaging device can also be focused on the lipid layer to capture a second image containing the background signal(s) present in the first image. The second image can be subtracted from the first image to reduce and/or eliminate the background signal(s) in the first image to produce a resulting image, which can used to measure a tear film layer thickness.

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.