CN-122028839-A - Visual field system and method for diagnosing and detecting glaucoma by performing adaptive map inspection through head mounted display

Abstract

A system may include a head-mounted device and an average visual hills (HoV) model, and may also generate eye difference estimates relative to average healthy eye reference data obtained from a standard dataset. The eye difference estimate indicates the overall sensitivity or overall height relative to the reference data, and the difference in rate at which sensitivity decays with eccentricity. The system can generate a personalized HoV model based on the eye difference estimated value and the average HoV model, respectively display corresponding stimulation signals at a plurality of test positions of the head display device, store response data of the test object to the stimulation signals, and determine a sensitivity value corresponding to each of the plurality of test positions of the test object by analyzing the response data. The system may also determine a total bias value for the test object by subtracting the sensitivity value for each response in the plurality of test locations from the corresponding value for the personalized HoV model, analyze the total bias value, and provide feedback based on the analysis.

Inventors

• L. Al Asvard
• I. Marlin French

Assignees

• 愿景健康科技有限公司

Dates

Publication Date

20260512

Application Date

20241227

Priority Date

20231229

Claims (20)

1. 1. A Visual Field Analysis (VFA) system configured to diagnose and monitor glaucoma by performing adaptive map visual field inspection, the VFA system comprising: A head-mounted device (e.g., a Virtual Reality (VR) device) comprising a display screen positioned near the eyes or within a visual distance of a user, the head-mounted device communicatively coupled to one or more processors; An average visual hillock (HoV) model stored in computer memory, and Computing instructions stored on the computer memory that, when executed by the one or more processors, cause the one or more processors to: generating an eye difference estimate for the test subject relative to average healthy human eye reference data obtained from a standard dataset, the eye difference estimate being indicative of (1) a difference in rate of overall sensitivity or overall height (GH) and (2) sensitivity decay with eccentricity (distance from fovea) relative to the reference data; Generating a personalized HoV model based on the eye difference estimation value and the average HoV model; displaying corresponding stimulation signals to the test object at a plurality of test positions on the head display device; storing responses of the test subject to the corresponding stimulus signals displayed at the plurality of test locations into a computer memory; Analyzing the response data, and determining a sensitivity value corresponding to each of the plurality of test positions of the test object; Determining a total deviation value for the test object by subtracting the sensitivity value for each response in a plurality of test locations from the corresponding value for the personalized HoV model; Analyzing the total deviation value, and And providing feedback to the test object based on the analysis result.
2. 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: Determining a set of preliminary test locations; running a visual psychophysical algorithm at the preliminary test position of the head display device to obtain preliminary sensitivity data, and And based on the preliminary sensitivity data, determining the overall sensitivity or GH corresponding to the eye difference estimated value and the rate of the sensitivity decaying along with the eccentricity.
3. 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: Generating an initial estimate of the overall sensitivity or GH, and the sensitivity decay rate with eccentricity; Acquiring temporary responses of the test object to the corresponding stimulus signals displayed at the plurality of test positions; Analyzing the temporary response, adjusting the overall sensitivity or GH and the initial estimated value of the sensitivity along with the decay rate of the eccentricity, and obtaining a confidence interval; Adjusting the intensity of the corresponding stimulation signal displayed on the head display device based on the adjusted initial estimated value to obtain a sensitivity value of the test object corresponding to each of the plurality of test positions, and Based on the sensitivity value of the test object corresponding to each of the plurality of test positions, determining an overall sensitivity or GH corresponding to the eye difference estimate, and a rate at which sensitivity decays with eccentricity.
4. 4. The VFA system of claim 3, wherein the initial estimate comprises one or more of an age-corrected average normal value, a value obtained by analyzing one or more past visual field tests, or a value obtained by performing a preliminary test on the test subject.
5. 5. The VFA system of claim 1, wherein the response of the test subject to the corresponding stimulus signals displayed at the plurality of test locations constitutes visual field data, and wherein to generate the eye difference estimate, the computing instructions, when executed by one or more processors, cause the one or more processors to: Identifying a point of impairment in the field of view data; removing the damaged point location from the field of view data; And analyzing the residual point positions in the visual field data, generating the eye difference estimated value and fitting the personalized HoV model.
6. 6. The VFA system of claim 5, wherein the eye difference estimate is generated and fitted to the personalized HoV model using one of a least squares algorithm, a maximum likelihood algorithm, a bayesian algorithm, or a machine learning algorithm.
7. 7. The VFA system of claim 1, wherein the average HoV model comprises a combination of an intercept/age model, an eccentricity model, and a field of view asymmetry model.
8. 8. The VFA system of claim 7, wherein the intercept model describes a theoretical sensitivity of a0 year old subject at the visual center (fovea).
9. 9. The VFA system of claim 7, wherein the age model accounts for age-related differences due to subject age and distance (eccentricity) from the fovea.
10. 10. The VFA system of claim 7, wherein the eccentricity model is used to record a linear decay law of sensitivity with eccentricity.
11. 11. The VFA system of claim 7, wherein the field of view asymmetry model employs a zernike polynomial to describe the asymmetry of the field of view between upper and lower regions and nasal and temporal regions.
12. 12. A VFA method for automatically evaluating a field of view test, the VFA method comprising: generating an eye difference estimate for the test subject relative to average healthy human eye reference data obtained from a standard dataset, the eye difference estimate being indicative of (1) a difference in rate of overall sensitivity or overall height (GH) and (2) sensitivity decay with eccentricity (distance from fovea) relative to the reference data; Generating a personalized HoV model based on the eye difference estimation value and an average HoV model stored in a computer memory; Displaying corresponding stimulus signals to the test object at a plurality of test positions, respectively, on a head-mounted display device, wherein the head-mounted display device (e.g., virtual Reality (VR) device) includes a display screen positioned near the eyes of a user or within a visual distance; storing responses of the test subject to the corresponding stimulus signals displayed at the plurality of test locations into a computer memory; Analyzing the response data, and determining a sensitivity value corresponding to each of the plurality of test positions of the test object; Determining a total deviation value for the test object by subtracting the sensitivity value for each response in a plurality of test locations from the corresponding value for the personalized HoV model; Analyzing the total deviation value, and And providing feedback to the test object based on the analysis result.
13. 13. The VFA method of claim 12, wherein generating the eye difference estimate comprises: Determining a set of preliminary test locations; running a visual psychophysical algorithm at the preliminary test position of the head display device to obtain preliminary sensitivity data, and And based on the preliminary sensitivity data, determining the overall sensitivity or GH corresponding to the eye difference estimated value and the rate of the sensitivity decaying along with the eccentricity.
14. 14. The VFA method of claim 12, wherein generating the eye difference estimate comprises: Generating an initial estimate of the overall sensitivity or GH, and the sensitivity decay rate with eccentricity; Acquiring temporary responses of the test object to the corresponding stimulus signals displayed at the plurality of test positions; Analyzing the temporary response, adjusting the overall sensitivity or GH and the initial estimated value of the sensitivity along with the decay rate of the eccentricity, and obtaining a confidence interval; Adjusting the intensity of the corresponding stimulation signal displayed on the head display device based on the adjusted initial estimated value to obtain a sensitivity value of the test object corresponding to each of the plurality of test positions, and Based on the sensitivity value of the test object corresponding to each of the plurality of test positions, determining an overall sensitivity or GH corresponding to the eye difference estimate, and a rate at which sensitivity decays with eccentricity.
15. 15. The VFA method of claim 14, wherein the initial estimate comprises one or more of an age-corrected average normal value, a value obtained by analyzing one or more past visual field tests, or a value obtained by performing a preliminary test on the test subject.
16. 16. The VFA method of claim 12, wherein the response of the test subject to generating the eye difference estimate comprises: Identifying a point of impairment in the field of view data; removing the damaged point location from the field of view data; And analyzing the residual point positions in the visual field data, generating the eye difference estimated value and fitting the personalized HoV model.
17. 17. The VFA method of claim 16, wherein the eye difference estimate is generated and fitted to the personalized HoV model using one of a least squares algorithm, a maximum likelihood algorithm, a bayesian algorithm, or a machine learning algorithm.
18. 18. The VFA method of claim 12, wherein the average HoV model comprises a combination of an intercept/age model, an eccentricity model, and a field of view asymmetry model.
19. 19. A tangible, non-transitory, computer-readable medium storing instructions for automatically evaluating a visual field test, the instructions when executed by one or more processors cause the one or more processors to: generating an eye difference estimate for the test subject relative to average healthy human eye reference data obtained from a standard dataset, the eye difference estimate being indicative of (1) a difference in rate of overall sensitivity or overall height (GH) and (2) sensitivity decay with eccentricity (distance from fovea) relative to the reference data; Generating a personalized HoV model based on the eye difference estimation value and an average HoV model stored in a computer memory; Displaying corresponding stimulus signals to the test object at a plurality of test positions, respectively, on a head-mounted display device, wherein the head-mounted display device (e.g., virtual Reality (VR) device) includes a display screen positioned near the eyes of a user or within a visual distance; storing responses of the test subject to the corresponding stimulus signals displayed at the plurality of test locations into a computer memory; Analyzing the response data, and determining a sensitivity value corresponding to each of the plurality of test positions of the test object; Determining a total deviation value for the test object by subtracting the sensitivity value for each response in a plurality of test locations from the corresponding value for the personalized HoV model; Analyzing the total deviation value, and And providing feedback to the test object based on the analysis result.
20. 20. The tangible, non-transitory computer-readable medium of claim 19, wherein the average HoV model comprises a combination of an intercept/age model, an eccentricity model, and a field of view asymmetry model.

Description

Visual field system and method for diagnosing and detecting glaucoma by performing adaptive map inspection through head mounted display Cross Reference to Related Applications The present application claims the benefit of U.S. provisional patent application No. 63/615,945, filed on day 29 of 12 of 2023. The entire contents of each of the foregoing prior applications are incorporated herein by reference. The present application claims priority from U.S. provisional patent application No. 63/673,383 filed on 7/19 at 2024, which is incorporated herein by reference. Technical Field The present disclosure relates generally to systems and methods for glaucoma diagnosis and monitoring, and more particularly to VR systems and methods for diagnosing and monitoring glaucoma through, for example, virtual Reality (VR) head display or adaptive Visual Field (VF) analysis on a head mounted display of other VR devices. Background Glaucoma is a chronic progressive ocular disease caused by optic nerve damage, which may result in loss of Visual Field (VF). One of the major risk factors is ocular tension. Abnormal ocular drainage system can cause aqueous humor to accumulate, thereby generating excessive pressure and damaging optic nerves. Glaucoma is a major cause of irreversible blindness and is difficult to diagnose, often resulting in too late and irreversible vision loss. For example, 300 ten thousand americans suffer from glaucoma. As the population ages, the prevalence is expected to increase to 630 tens of thousands of people for the next 30 years. Glaucoma is difficult to diagnose as an asymptomatic disease from early to mid stages. Early detection of vision changes is critical for preventing vision loss, however, the subjectivity of functional testing and limitations of existing diagnostic methods complicate diagnosis and disease monitoring. The primary purpose of visual field inspection is to measure the performance of the entire VF. Before the introduction of a computer that allows the automatic presentation of visual stimuli, this object is achieved by manually moving the small stimulus, finding a transition position between the visible area and the invisible area of the particular stimulus, called isoline of sight. These lines of sight generate a visual hilly terrain map of the patient. The test needs to be performed by a qualified clinician. By visual inspection, topographical abnormalities caused by pressed tumors of the optic nerve pathway, central nervous system vascular damage, glaucoma, and the like can be identified. Manual visual field inspection has several limitations. It requires trained testers to accurately perform the test, which is costly and limits scalability. This test is time consuming for both the tester and the patient, and variability between different testers tends to be large, especially over time, reducing the reliability and consistency of the data. These drawbacks lead to the replacement of manual dynamic visual field examination with static automatic visual field examination (static automated perimetry, SAP), which was originally intended as a supplement to dynamic visual field examination, in glaucoma diagnosis. SAP provides a more uniform approach, removing reliance on visual field inspectors. The uniformity of the test makes the data analysis and interpretation standardized, which is critical to the success of SAP replacement dynamic visual field inspection. However, due to the lack of spatial sampling, and the higher test-retest variability in estimating the contrast sensitivity threshold for the damaged area from a small number of stimulus presentations, SAP is less reliable in identifying the spatial pattern of damage and comparing with the retinal examination results than is dynamic visual field examination. Only in cases where advances in software and hardware in the 70 s and 80 s allowed the development of algorithm and computation intensive analysis tools, it was possible to transition to SAP, with limitations that were the product of the era. Since the 80 s of the 20 th century, visual field examination methods have not changed much, despite great advances in understanding the structural and anatomical changes that occur in glaucoma. For example, the test sites used are regularly grid-spaced too much and inconsistent with the results of structural testing and other imaging methods, which reduces our clinical confidence in lesion detection and disease progression assessment. For patients, this delay means irreversible vision loss. In addition, healthcare providers are plagued by inadequate reliability of the results, often requiring repeated VF checks at a later visit to make a therapeutic decision. VF examination results of 17% to 48% are reported to be unreliable due to higher fixation loss rate, false positive rate and false negative rate. Thus, the limitations of existing diagnostic methods present several problems or pain points. The currently most common mode of VF exami