Visual field systems and methods for glaucoma diagnosis and monitoring by implementing adaptive map perimetry via head-mounted displays
A system may include a headset device and an average hill of vision model (HoV) and may generate an eye difference estimate relative to reference data for an average healthy eye obtained from a normative dataset. The eye difference estimate indicates a difference in overall sensitivity or general height and the rate of sensitivity decay with eccentricity relative to the reference data. The system may generate an individualized HoV model based on the eye difference estimate and the average HoV model, display, on the headset device, a respective stimulus at a plurality of test locations, store responses to the stimulus, and analyze the responses to determine a respective sensitivity value at each of the plurality of test locations. The system may determine total-deviation values by subtracting the respective sensitivity value from a corresponding value of the individualized HoV model, analyze the total-deviation values, and provide feedback based on the analysis.
1 . A visual field analysis (VFA) system configured for automatically assessing visual field testing, the VFA system comprising:
a headset device comprising a display screen positioned proximate to, or within a viewable distance from a user's eyes, the headset device communicatively coupled to one or more processors;
an average hill of vision model (HoV) saved in a computer memory; and
computing instructions stored on the computer memory and, when executed by one or more processors, cause the one or more processors to:
generate an eye difference estimate for a test subject relative to reference data for an average healthy eye obtained from a normative dataset, the eye difference estimate indicating a difference in (1) overall sensitivity or general height (GH) and (2) the rate of sensitivity decay with eccentricity (distance from the fovea) relative to the reference data;
generate an individualized HoV model based on the eye difference estimate and the average HoV model;
display, on the headset device, a respective stimulus to the test subject at a plurality of test locations;
store in computer memory responses of the test subject to the respective stimulus displayed at the plurality of test locations;
analyze the responses to determine a respective sensitivity value for the test subject at each of the plurality of test locations;
determine total-deviation values for the test subject by subtracting the respective sensitivity value for each of the plurality of test locations from a corresponding value of the individualized HoV model;
analyze the total-deviation values; and
provide feedback to the test subject based on the analysis.
2 . The VFA system of claim 1 wherein to generate the eye difference estimate the computing instructions, when executed by one or more processors, cause the one or more processors to:
determine a set of preliminary test locations;
display, on the headset device, a visual psychophysics algorithm at the set of preliminary test location to obtain preliminary sensitivities; and
determine the overall sensitivity or GH and the rate of sensitivity decay with eccentricity of the eye difference estimate based on the preliminary sensitivities.
3 . The VFA system of claim 1 wherein to generate the eye difference estimate the computing instructions, when executed by one or more processors, cause the one or more processors to:
generate initial estimates of the overall sensitivity or GH and the rate of sensitivity decay with eccentricity;
obtain interim responses of the test subject to the respective stimulus displayed at the plurality of test locations
analyze the interim responses to adjust the initial estimates of the overall sensitivity or GH and the rate of sensitivity decay with eccentricity and obtain confidence intervals;
adjust levels of the respective stimulus displayed on the headset device based on the adjusted initial estimates to obtain respective sensitivity value for the test subject at each of the plurality of test locations; and
determine the overall sensitivity or GH and the rate of sensitivity decay with eccentricity of the eye difference estimate based on the respective sensitivity value for the test subject at each of the plurality of test locations.
4 . The VFA system of claim 3 wherein the initial estimates include one or more of age-corrected mean normal values, values generated from analysis of one or more previous visual field tests, or values from a preliminary test administered to the test subject.
5 . The VFA system of claim 1 wherein the responses of the test subject to the respective stimulus displayed at the plurality of test locations comprise a visual field and to generate the eye difference estimate the computing instructions, when executed by one or more processors, cause the one or more processors to:
identify points in the visual field that are damaged;
remove the damaged points from the visual field;
analyze the remaining points within the visual field to generate the eye difference estimate and fit the individualized HoV model.
6 . The VFA system of claim 5 wherein one of a least-squares algorithm, maximum-likelihood algorithm, Bayesian algorithm, or Machine Learning algorithm are used to generate the eye difference estimate and fit the individualized HoV model.
7 . The VFA system of claim 1 wherein the average HoV model includes a combination of an intercept/age model, an eccentricity model, and a visual field asymmetry model.
8 . The VFA system of claim 7 wherein the intercept model describes a theoretical sensitivity of a 0-year-old patient at the center of vision (fovea).
9 . The VFA system of claim 7 wherein the age model documents eccentricity-dependent differences in age as a function of an subject age and distance from fovea (eccentricity).
10 . The VFA system of claim 7 wherein the eccentricity model documents linear decay of sensitivity with eccentricity.
11 . The VFA system of claim 7 wherein the visual field asymmetry model documents asymmetries between superior and inferior and nasal and temporal parts of a visual field using Zernike polynomials.
12 . A visual field analysis (VFA) method for automatically assessing visual field testing, the VFA method comprising:
generating an eye difference estimate for a test subject relative to reference data for an average healthy eye obtained from a normative dataset, the eye difference estimate indicating a difference in (1) overall sensitivity or general height (GH) and (2) the rate of sensitivity decay with eccentricity (distance from the fovea) relative to the reference data;
generating an individualized HoV model based on the eye difference estimate and an average HoV model saved in a computer memory;
displaying, on a headset device, a respective stimulus to the test subject at a plurality of test locations, wherein the headset device comprises a display screen positioned proximate to, or within a viewable distance from a user's eyes;
storing in computer memory responses of the test subject to the respective stimulus displayed at the plurality of test locations;
analyzing the responses to determine a respective sensitivity value for the test subject at each of the plurality of test locations;
determining total-deviation values for the test subject by subtracting the respective sensitivity value for each of the plurality of test locations from a corresponding value of the individualized HoV model;
analyzing the total-deviation values; and
providing feedback to the test subject based on the analysis.
13 . The VFA method of claim 12 wherein to generating the eye difference estimate includes:
determining a set of preliminary test locations;
displaying, on the headset device, a visual psychophysics algorithm at the set of preliminary test location to obtain preliminary sensitivities; and
determining the overall sensitivity or GH and the rate of sensitivity decay with eccentricity of the eye difference estimate based on the preliminary sensitivities.
14 . The VFA method of claim 12 wherein to generating the eye difference estimate includes:
generating initial estimates of the overall sensitivity or GH and the rate of sensitivity decay with eccentricity;
obtaining interim responses of the test subject to the respective stimulus displayed at the plurality of test locations
analyzing the interim responses to adjust the initial estimates of the overall sensitivity or GH and the rate of sensitivity decay with eccentricity and obtain confidence intervals;
adjusting levels of the respective stimulus displayed on the headset device based on the adjusted initial estimates to obtain respective sensitivity value for the test subject at each of the plurality of test locations; and
determining the overall sensitivity or GH and the rate of sensitivity decay with eccentricity of the eye difference estimate based on the respective sensitivity value for the test subject at each of the plurality of test locations.
15 . The VFA method of claim 14 wherein the initial estimates include one or more of age-corrected mean normal values, values generated from analysis of one or more previous visual field tests, or values from a preliminary test administered to the test subject.
16 . The VFA method of claim 12 wherein the responses of the test subject to generating the eye difference estimate includes:
identifying points in the visual field that are damaged;
removing the damaged points from the visual field;
analyzing the remaining points within the visual field to generate the eye difference estimate and fit the individualized HoV model.
17 . The VFA method of claim 16 wherein one of a least-squares algorithm, maximum-likelihood algorithm, Bayesian algorithm, or Machine Learning algorithm are used to generate the eye difference estimate and fit the individualized HoV model.
18 . The VFA method of claim 12 wherein the average HoV model includes a combination of an intercept/age model, an eccentricity model, and a visual field asymmetry model.
19 . A tangible, non-transitory computer-readable medium storing instructions for automatically assessing visual field testing, that when executed by one or more processors cause the one or more processors to:
generate an eye difference estimate for a test subject relative to reference data for an average healthy eye obtained from a normative dataset, the eye difference estimate indicating a difference in (1) overall sensitivity or general height (GH) and (2) the rate of sensitivity decay with eccentricity (distance from the fovea) relative to the reference data;
generate an individualized HoV model based on the eye difference estimate and an average HoV model saved in a computer memory;
display, on a headset device, a respective stimulus to the test subject at a plurality of test locations, wherein the headset device comprises a display screen positioned proximate to, or within a viewable distance from a user's eyes;
store in computer memory responses of the test subject to the respective stimulus displayed at the plurality of test locations;
analyze the responses to determine a respective sensitivity value for the test subject at each of the plurality of test locations;
determine total-deviation values for the test subject by subtracting the respective sensitivity value for each of the plurality of test locations from a corresponding value of the individualized HoV model;
analyze the total-deviation values; and
provide feedback to the test subject based on the analysis.
20 . The tangible, non-transitory computer-readable medium of claim 19 wherein the average HoV model includes a combination of an intercept/age model, an eccentricity model, and a visual field asymmetry model.