The exemplified systems and methods disclosed herein involve the contemporaneous and concurrent stimulation of both eyes of a patient with dissimilar visual scenes that substantially span an expected or normal visual field of both eyes. During an assessment via the exemplified system and methods, a test eye (i.e., eye being assessed) is presented a visual scene that is rich in contours (e.g., a scene with rich texture patterns) that substantially span an expected or normal visual field of a person while the non-tested eye is presented an impoverished visual scene (e.g., a contour-free or non-distinguishing-contour scene, e.g., with a homogeneous color, with respect to the contour-rich scene). Defects in the visual field can be detected by assessing for breaks or discontinuity in the observation of the contour-rich scene by the person.

A method for evaluating the visual acuity of a person using a mobile device having at least a front camera and a screen, the method including a user positioning step during which the user of a mobile device positions himself in front of a mirror at a predefined distance d/2, a mobile device positioning step during which the mobile device is positioned with the front camera of the mobile device facing the mirror, a distance measuring step during which a distance d between the front camera of the mobile device and a virtual image of the mobile device in the mirror is measured, a displaying step during which the screen of the mobile device displays optotypes, and an evaluation step during which the visual acuity of the user is evaluated.

In some implementations, methods for selecting a set of beacons that are to be monitored by a mobile device may be employed. Specifically, an optimal set of beacons to monitor may be provided to a mobile device depending on particular groups of beacons that are in proximity to the mobile device, the distance from the mobile device to each of the particular groups of beacons, and the mobile device's position/movements as provided by a tracking service such as GPS. These techniques may ensure that the mobile device is not blind to the closest and/or most relevant beacons.

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.

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.

In some embodiments, initial feedback indicating threshold characteristics (under which a user sees initial stimuli presented on a user interface) may be provided to a prediction model, and a set of predicted characteristics (for a set of locations of the user interface) and a set of confidence scores associated with the set of locations may be obtained via the prediction model. Based on the set of confidence scores, one or more locations may be selected to be tested during a visual test presentation. As an example, the locations may be selected over one or more other locations of the set of locations based on the set of confidence scores. Based on predicted characteristics associated with the selected locations, stimuli may be presented at the selected locations during the visual test presentation. Visual defect information for the user may be generated based on feedback from the visual test presentation.

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.

Methods and systems for dynamically determining stimuli characteristics for vision defect determination during a vision test. The methods and systems convert the feedback received from the binary suprathreshold test into a feature input that is provided to a prediction model. The prediction model may be trained to predict non-binary characteristics for sets of locations and confidence scores associated with the sets of locations based on feedback indicating binary characteristics under which users see one or more stimuli presented on user interfaces.

Methods and diagnostic tools are provided for assessing contrast sensitivity in a subject in the presence and absence of luminance noise by: i) presenting to the subject a series of scenes, each scene comprising at least a first target having a preselected level of contrast superimposed on a uniform background and a second target having a preselected level of contrast superimposed on a luminance noise background, wherein in each successively presented scene the first and second targets that are superimposed on the uniform background and on the luminance noise background, respectively, have contrast levels that are different from the contrast levels of the first and second targets superimposed on the uniform background and on the luminance noise background, respectively, in the previously presented scene; ii) monitoring responses by the subject to step i); and iii) evaluating the contrast sensitivity of the subject based on the monitored responses.

A computer implemented method for determining a level of certainty of a patient’s response to a stimulus perception of a subjective medical test, the method including detecting at least one physiological signal from the patient while the patient is providing a response to the stimulus perception, determining the level of certainty of a patient’s response to the stimulus perception from the at least one physiological signal, the at least one physiological signal being an input data to a machine learning model trained based on a set of training data, the set of training data comprising at least one physiological signal associated to a level of certainty of a patient’s response, the determined level of certainty being the output of the trained machine learning model.