Ophthalmic Surgery, Lasers and Imaging Retina

Clinical Science 

Digital Nullification of Visual Distortion to Quantify Metamorphopsia: A Pilot Study

Jacob A. Lifton, BA; Andrew A. Moshfeghi, MD, MBA

Abstract

BACKGROUND AND OBJECTIVE:

To evaluate a novel digital method of metamorphopsia quantification in patients with symptomatic visual distortion as determined by M-CHARTS.

PATIENTS AND METHODS:

In this institutional review board-approved, prospective, cross-sectional observational study, subjects were presented with an objectively straight dotted line that bisects a central fixation point. The subjects digitally altered the line's contour until it appeared straight. Calculation of the area between the original objectively straight line presented to the subject and their newly manipulated line was performed to determine the manipulated area under the curve (M-AUC).

RESULTS:

Sixty-two percent of test targets were fully or significantly straightened by affected subjects. M-AUC was significantly correlated with M-CHARTS scores in both the horizontal (P < .001) and vertical (P = .05) orientations.

CONCLUSIONS:

The deformation of an objectively straight line by a subject with metamorphopsia may be a viable way of both quantifying and spatially characterizing visual distortions.

[Ophthalmic Surg Lasers Imaging Retina. 2020;51:11–20.]

Abstract

BACKGROUND AND OBJECTIVE:

To evaluate a novel digital method of metamorphopsia quantification in patients with symptomatic visual distortion as determined by M-CHARTS.

PATIENTS AND METHODS:

In this institutional review board-approved, prospective, cross-sectional observational study, subjects were presented with an objectively straight dotted line that bisects a central fixation point. The subjects digitally altered the line's contour until it appeared straight. Calculation of the area between the original objectively straight line presented to the subject and their newly manipulated line was performed to determine the manipulated area under the curve (M-AUC).

RESULTS:

Sixty-two percent of test targets were fully or significantly straightened by affected subjects. M-AUC was significantly correlated with M-CHARTS scores in both the horizontal (P < .001) and vertical (P = .05) orientations.

CONCLUSIONS:

The deformation of an objectively straight line by a subject with metamorphopsia may be a viable way of both quantifying and spatially characterizing visual distortions.

[Ophthalmic Surg Lasers Imaging Retina. 2020;51:11–20.]

Introduction

Metamorphopsia, or visual distortion, is a pathological visual phenomenon seen commonly in diseases that deform normal macular architecture, such as age-related macular degeneration (AMD), central serous chorioretinopathy (CSCR), epiretinal membrane (ERM), vitreomacular traction (VMT), or full-thickness macular hole. To the affected patient, areas of vision may appear to be bulging outward or pinching inward, and objectively straight lines may seem wavy or bent, often in an irregular or asymmetric pattern. Increasing severity of metamorphopsia has been shown to have a proportionately negative effect on vision-related quality of life (VR-QoL).1–4 Nevertheless, testing for visual distortion in the clinic setting is typically rudimentary and is not conducted using a quantifiable metric. The Amsler grid, for instance, is still commonly used to detect and monitor metamorphopsia, yet the test — in its most commonly administered format — provides virtually no reliable quantitative or spatial information about visual distortions, as it is not interactive on the part of the user (apart from a binary “yes/no” response regarding the presence or absence of distortion) and is subject to inconstant fixation and cortical “filling-in” of scotomata.5–7 Furthermore, recent evidence suggests that the degree of metamorphopsia is not adequately captured by traditional metrics of visual quality, such as best-corrected visual acuity (BCVA),8 indicating the importance of developing and validating newer tools that can quantify visual distortions (Figure 1).

(A) Depiction of the objectively straight dotted line displayed on our software platform, which is centered on a central fixation point. (B) Schematic depiction of several foci where a subject may have deformed the straight line using the testing interface. The areas denoted in red represent the areas of space that are cumulatively summed to calculate manipulated area under the curve.

Figure 1.

(A) Depiction of the objectively straight dotted line displayed on our software platform, which is centered on a central fixation point. (B) Schematic depiction of several foci where a subject may have deformed the straight line using the testing interface. The areas denoted in red represent the areas of space that are cumulatively summed to calculate manipulated area under the curve.

More recently, methods such as M-CHARTS (Inami & Co., Ltd., Tokyo, Japan),9,10 D-CHARTS,11 shape discrimination hyperacuity,12 and preferential hyperacuity perimetry (PHP)13 have all been studied and used as means of quantifying metamorphopsia. However, with the exception of PHP testing devices,14 which are costly15 and only U.S. Food and Drug Administration-approved for monitoring of intermediate nonexudative AMD, none of the above tests can yield a spatial representation of patients' distortions that can be subsequently mapped onto corresponding regions of the macula. Some interest has been accruing as a result in methodologies predicated on the “negation” or “nullification” of existing distortion by the patient; in theory, if a patient were able to manipulate visual input (ie, an Amsler grid) in such a way that their distortion was no longer perceptible, the resulting image might indicate not only the retinotopic location of any perceived distortion, but also the quantity, directionality, and general shape. To our knowledge, so far only three studies have constructed such tests: two of these studies employed deformable Amsler grids,16–18 and one proposed a new test in which subjects would construct and straighten a “notional square” out of eight randomly distributed points.19 Each of these testing methodologies represents opposing extremes in terms of granularity. The deformable Amsler grid provides copious spatial information yet is a relatively cumbersome, time-consuming test, and the “notional square” method is largely a research tool that only tests the spatial alignment of eight foci in the creation of a distortion map.19

The aim of the present study is to assess the feasibility of a new software-based method of negating visual distortion by way of a deformable dotted line — a method that we believe will be both rapid in practice while remaining granular in output. We hypothesize that subjects with visual distortion will be able to manipulate an objectively straight dotted line at various points until it appears straight to them; the resulting changes made by the user would provide simple, linear “distortion maps” that could be superimposed onto corresponding macular anatomy. The degree of the manipulation might then serve as a useful metric of metamorphopsia that could be employed in a clinic or study setting.

Patients and Methods

Study Design and Recruitment

This was a prospective, institutional review board-approved, cross-sectional, observational study of 22 subjects who were recruited for participation in this study from the clinics of the Roski Eye Institute of the University of Southern California in Los Angeles, CA. Subjects older than 18 years of age of any biological sex and ethnicity were eligible to participate if they had been diagnosed with a maculopathy in one or both eyes and demonstrated at least one non-zero M-Charts score in the affected eye(s). As our primary interest was in testing whether a subject with visual distortion would be able to correct that distortion using our testing interface when presented with target lines appearing in only the horizontal and vertical orientations, each subject was also assessed using M-CHARTS to screen patients for demonstrable distortion in these meridians. Patients were excluded if they had a BCVA of less than 20/200 (Snellen) in the tested eye; if they had documented or demonstrable scotoma involving central vision; or if they had concurrent vitreoretinal disease, optic nerve disease, or other ocular conditions felt by the investigator to compromise the patient's ability to visualize the displayed line clearly and in its entirety, to fixate on the central fixation point, or to interact with the software interface. Demographic and clinical data collected for each patient included age, biological sex, diagnosis, and BCVA using Snellen acuity charts.

Description of Testing Software and Protocol

A computer program written in the C# programming language (Unity 2018.1.8f1; Unity Technologies, San Francisco, CA) was devised for the purposes of this study. The graphical user interface of this program presents the subject with an objectively straight dotted line that is oriented either vertically or horizontally and centered on a central fixation point. The dotted line is displayed in black on a white background, and dots are spaced 0.17° apart for a total line length of 8.9 cm (15.6°). Using the computer's arrow keys, the subject is able to move a small cursor — located adjacent to the line — along the line's length. At any point, the subject may incrementally adjust the segment of line adjacent to the cursor to swell upwards, downwards, left, or right. This can be repeated indefinitely, allowing for infinite adjustment of the line's coordinates at any given point. After this manipulation, the line itself will then be a graphical representation of which specific areas in a subject's vision are distorted along the axis of the line, in what direction (if inverted), and by how much.

During this adjustment, the program calculates and displays the magnitude of the area between the original straight line and the newly manipulated line; this area will henceforth be referred to as the “manipulated area under the curve,” abbreviated as “M-AUC.” M-AUC is calculated in dimensionless units of the native coordinate system that the software program uses to measure and scale interface components. Whereas the manipulated line itself provides a graphical representation of distortions along the line's axis, the M-AUC represents a summative quantification of the subject's total distortion along the line. If multiple noncontiguous areas were manipulated, these M-AUCs are arithmetically summed.

During each subject's scheduled, standard-of-care clinic visit, qualified eyes were tested monocularly at a distance of 30 cm from a 2013 13-inch MacBook Pro Retina Display (Apple, Cupertino, CA) while opposite eyes were covered using an occluder. Subjects were instructed to maintain fixation on the central point bisected by the dotted horizontal or vertical line and then use the arrow keys as described previously to adjust segments that appeared to deviate from a straight line. The subjects were given unlimited time, and after their best effort, the examiner toggled between the original straight line and the newly manipulated line, which were identified for the subject. During this process, the subjects were asked whether the adjusted line appeared perfectly straight/significantly straighter than the original line, and whether or not the patients were able to achieve this goal was recorded. The subject's M-AUC was documented at the conclusion of the trial, regardless of whether or not they were able to fully straighten the line. This was conducted along both the horizontal and vertical axes.

M-CHARTS is a metric of metamorphopsia first described in 20039 that is gaining in popularity in clinical studies. The test consists of measuring the minimum distance between dots on either a horizontal or vertical dotted line at which the line appears straight to the subject. To do this, the subject is monocularly shown of a series of cards held 30 cm away, the first of which depicts a solid line with a central fixation point; if the subject deems the line does not appear straight while maintaining fixation, the subsequent line is shown — this time a dotted line with 0.2° of spacing between each dot. The spacing between the dots shown to the subject is continually increased until the line is no longer perceived as distorted. The inter-dot distance at which this occurs represents the subject's M-CHARTS score — or “M-score” — in the horizontal or vertical orientation. These steps were conducted for all eyes and results recorded.

Statistical Methods

Data were analyzed using Stata statistical software (StataCorp, College Station, TX). Continuous data were described by calculating the mean, standard deviation (SD), median, and interquartile range (IQR). Categorical data were tallied and percentages reported. Logistic regression analysis was used to test the relationship between a subject's ability to complete the task and their age and BCVA. M-AUC was preliminarily assessed as a metric of metamorphopsia by correlating horizontal M-AUC with horizontal M-CHARTS scores and vertical M-AUC with vertical M-CHARTS scores using linear regression analysis. The relationship between visual acuity and M-AUC was assessed using Pearson's correlation coefficient and linear regression analysis.

Results

Subject Characteristics

Recruitment yielded a total 25 eyes of 22 subjects who were included for analysis. The most common disease encountered in our population was ERM, followed by wet and dry AMD, respectively. All descriptive summaries and tabulations of demographic and clinical data can be found in Tables 1 and 2.

Descriptive Summary of Continuous Variables and Tabulation of Categorical Variables*

Table 1:

Descriptive Summary of Continuous Variables and Tabulation of Categorical Variables

Tabulation of Subjects' Diagnoses*

Table 2:

Tabulation of Subjects' Diagnoses

Assessing the Ability of Subjects to Complete the Software-Based Task

Out of the 25 horizontal and vertical lines displayed, participants identified distortion in 18 of 25 (72%) of the horizontal lines and 13 of 25 (52%) of the vertical lines, totaling 31 of 50 (62%) distorted lines.

Of those 31 lines that appeared distorted, 19 of 31 lines (61%) could be fully or significantly straightened by the subject, leaving 12 of 31 (39%) that could not. Common reasons cited by subjects for being unable to complete the task included “shifting” or “changing” distortions, as well as the perception of numerous subtle distortions along the entire length of the line that proved difficult to pinpoint while maintaining fixation.

Notably, univariate logistic regression showed no significant relationship between logMAR BCVA and the ability to straighten horizontal (P = .44) or vertical (P = .63) lines; the subject's age was similarly unrelated (horizontal: P = .42; vertical: P = .2). An exemplar screenshot from one trial in which the subject was able to fully straighten the line is displayed as Figure 2.

Screenshot of the adjustments made to a vertical line by a subject with epiretinal membrane of the right eye, after which the subject reported the line appeared “perfectly straight” (manipulated area under the curve = 0.5, M-CHARTS score = 0.3). Red arrows are displayed to highlight the adjusted area.

Figure 2.

Screenshot of the adjustments made to a vertical line by a subject with epiretinal membrane of the right eye, after which the subject reported the line appeared “perfectly straight” (manipulated area under the curve = 0.5, M-CHARTS score = 0.3). Red arrows are displayed to highlight the adjusted area.

Metamorphopsia Testing

M-AUC ranged from 0 to 3.72 square units (mean = 0.54; SD = 1.04) horizontally and 0 to 1.89 square units (Mean 0.24, SD 0.41) vertically. M-CHARTS scores ranged from 0° to 2° (mean = 0.32°; SD = 0.35°) horizontally and 0° to 1.5° (mean = 0.39°; SD = 0.54°) vertically. All remaining descriptive statistics are available in Table 1.

LogMAR BCVA was not significantly correlated with AUC in either the horizontal (P = .198) or vertical (P = .892) orientations (Figure 3).

Scatterplot of vertical and horizontal manipulated area under the curve (M-AUC) versus logMAR visual acuity (VA). No significant relationship was found between subjects' VA and their M-AUC in either orientation. H = horizontal; V = vertical

Figure 3.

Scatterplot of vertical and horizontal manipulated area under the curve (M-AUC) versus logMAR visual acuity (VA). No significant relationship was found between subjects' VA and their M-AUC in either orientation. H = horizontal; V = vertical

Comparison of Manipulated-AUC With M-CHARTS Scores

When including all trials, regardless of the subject's ability to fully straighten the line, M-AUC was significantly correlated with M-CHARTS metamorphopsia scores for both horizontal (P < .001, R2 = 0.44; coefficient = 1.29) and vertical (P = .05; R2 = 0.16; coefficient = 0.47) lines (Figure 4).

Scatterplots showing the relationship between subjects' M-CHARTS scores and their calculated manipulated area under the curve (M-AUC) in both the horizontal (A) and vertical (B) directions when including all trials. H = horizontal; V = vertical

Figure 4.

Scatterplots showing the relationship between subjects' M-CHARTS scores and their calculated manipulated area under the curve (M-AUC) in both the horizontal (A) and vertical (B) directions when including all trials. H = horizontal; V = vertical

Excluding trials in which the subject was not able to fully straighten the line, this relationship remained significant (horizontal: P = .002; R2 = 0.47; coefficient = 2.06; vertical: P = .002; R2 = 0.42; coefficient = 1.17) (Figure 5).

Scatterplots showing the relationship between subjects' M-CHARTS scores and their calculated manipulated area under the curve (M-AUC) in both the horizontal (A) and vertical (B) directions when excluding trials in which the patient could not fully or significantly straighten the line. H = horizontal; V = vertical

Figure 5.

Scatterplots showing the relationship between subjects' M-CHARTS scores and their calculated manipulated area under the curve (M-AUC) in both the horizontal (A) and vertical (B) directions when excluding trials in which the patient could not fully or significantly straighten the line. H = horizontal; V = vertical

Discussion

This pilot study demonstrates that it does appear possible for a subject with metamorphopsia to electronically manipulate an objectively straight dotted line in a way that mitigates their distortion while maintaining central fixation. This is evidenced by the fact that 61% of distorted lines displayed to subjects were made either perfectly straight or significantly straighter, echoing findings from prior studies that found that the negation of distortion was possible using software-based interfaces.16,17,19 Importantly, the subjects' ability to complete the task did not appear to be influenced by their age or Snellen BCVA, although by excluding eyes with BCVA greater than 20/200 we have likely biased this particular relationship.

However, it is also clear that our methodology, in its current state, presented significant challenges to many participants, as a sizeable portion of subjects were unable to carry the task to completion. The most common reports from patients as to why they were unable to complete the assigned task contained descriptions of either “changing” distortions or very subtle, widespread distortions that were difficult to pinpoint with the cursor while maintaining central fixation. Both of these issues appear to be related, at least in part, to lapses in patient fixation, particularly given that the test is interactive in nature. This first iteration of our testing platform did not include eye tracking technology. The deformable Amsler grid previously proposed by Kent Stevens (US Patent number 5,892,570; April 6, 1999) and later developed and analyzed by Gonzalez16 did not have a means of enforcing fixation compliance either, and, as a result, it could only be completed by five participants with macular disease. The lack of fixation fidelity is, of course, a commonality among the majority of metamorphopsia tests and is rightly a frequent point of criticism, as it effectively precludes precise anatomic correlation and hinders reproducibility.20 However, as our testing platform is digital, eye-tracking software could easily be implemented in future iterations of the test, and planning for this next iteration of testing is currently underway in our lab.

Despite the current limitations of our software interface, we offer that M-AUC is a pragmatic means of quantifying visual distortions, as it intuitively represents the numerical discrepancy between perceived and objective visual stimuli in a directional manner. The potential utility of M-AUC is supported by its relatively strong correlation with M-CHARTS, which is a prospectively validated clinical tool that is quickly becoming a commonplace metric in clinical trials.21–23 Interestingly, the relationship between the two metrics remained significant when including software trials in which the subject could not fully straighten the line, suggesting that patients who saw a more distorted line tended to deform that line more, even if they could not achieve perfect subjective linearity. Thus, one could argue that M-AUC may be valuable as a gross metric of distortion, even if the underlying task cannot be carried out to completion, as is often the case in the real-world clinical setting. Of course, this correlation between M-AUC and M-CHARTS is insufficient evidence of internal validity. However, we did not seek to validate our metric at this stage, but rather to propose and assess the viability of an evolving method. Future studies will be needed to more rigorously examine the sensitivity, specificity, and test-retest reliability of M-AUC.

Our testing platform offers several distinct advantages over M-CHARTS, the first of which is that it is both quantitative and qualitative: M-CHARTS was simply not designed to reflect the shape, number, or location of a patient's distortions, while our method was. Therefore, a patient with one locus of distortion or multiple equivalent loci of distortion would potentially have the same M-CHARTS score, whereas M-AUC sums the area under each individual focus, yielding a more accurate measure of the total amount of distortion along the line. This potentially has clinical implications as well; for instance, greater width (ie, lower frequency) of visual distortions in space have been linked to more advanced stages of epiretinal membrane.24 Therefore, the qualitative (ie, spatial) characterization of metamorphopsia may also prove to be an important aspect of prognostication or monitoring for changes in disease status.

The second advantage of M-AUC over M-CHARTS is flexibility. The prescribed testing protocol of M-CHARTS, and as such the way it is used in clinical studies, is to test only the horizontal and vertical meridians, effectively overlooking any distortions that do not encompass those axes; indeed, we have reported the case of one patient with zero horizontal or vertical distortion on M-CHARTS who was found to have measurable distortion when the M-CHARTS testing booklet was oriented at an oblique angle.25 The displayed line we use to calculate M-AUC, on the other hand, can currently be oriented at any angle, thereby capturing a wider swath of visual space and allowing the examiner to determine the subject's axis of maximal distortion. In the same way that the orientation of our software's dotted line can be precisely adjusted, so, too, can line length and dot spatial frequency, whereas the paper-based M-CHARTS test is rigid in its current form. In future validation studies of our platform we plan to determine the optimal test settings that maximize ease-of-use and sensitivity/specificity.

Metamorphopsia, as a whole, remains a poorly understood phenomenon, with conflicting viewpoints about the microstructural changes that lead to visual distortions. Historically, studies have implicated retinal structural abnormalities as the primary drivers of visual distortion,26–28 often doing so by correlating anatomic parameters on OCT with M-CHARTS scores.29–34 More recently, it has been suggested that cortical processing also influences the final perception of distortion,19 and the severity and nature of metamorphopsia perceived by the viewer during any given testing method depend on the nature of the stimulus being presented.19 The digital correction of distortions by the subject, such as that employed by our testing platform, has the potential to yield further insights into the interaction between cortical and retinal elements, particularly if a variety of patterns of visual stimuli are presented for manipulation. Doing this via en face retinal projection of our testing platform (ie, with a scanning laser ophthalmoscope) would guarantee precise localization of the visual stimulus while also obtaining live cross-sectional and en face imaging of the underlying retina, allowing for more precise correlation between structure and function. Ultimately, understanding the relationship between retinal structure and visual function would allow for clearer indications for intervention, as well as more comprehensive counseling of patients with macular pathology.

The ability to alter the shape or contour of visual input to prescriptively “correct” perceived visual distortions in visual space may also someday have profound therapeutic implications for patients with metamorphopsia, theoretically allowing for symptom management (ie, via a head-mounted display) without the need for invasive medical or surgical interventions. This idea has been introduced in theory before35 but never trialed on patients, let alone successfully implemented. Issues regarding cortical effects on perceived metamorphopsia may make such a task near-impossible, but upon refinement of our testing protocol we eventually aim to devise and study the feasibility of a way to digitally correct a patients metamorphopsia in both still images and live-feed video using an augmented-reality type of platform.

In summary, by tasking subjects with the negation of distortion along an objectively straight line, we believe that we have introduced a fast, intuitive, and information-rich method of characterizing metamorphopsia. In the future, this method will be enhanced with additional features and rigorously validated with the goal of using it to more closely examine the relationship between visual quality and underlying retinal structural features.

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Descriptive Summary of Continuous Variables and Tabulation of Categorical Variables*

Continuous VariableMean (SD)Median (IQR)
Age (years)73.5 (12.2)74 (65–85)
BCVA (logMAR)0.39 (0.31)0.3 (0.18–0.69)
M-Charts score (horizontal)0.39 (0.54)0.2 (0–0.5)
M-Charts score (vertical)0.32 (0.35)0.2 (0.2–0.4)
M-AUC (horizontal)0.54 (1.04)0.02 (0–0.46)
M-AUC (vertical)0.24 (0.41)0 (0–0.31)
Categorical variableN (%)
Male14 (63.6%)
Female8 (36.4%)
Total22 (100%)

Tabulation of Subjects' Diagnoses*

DiagnosisNumber of Subjects
ERM10 (Two postoperative subjects)
Wet AMD7
Dry AMD6
CSCR3
Lamellar macular hole2
Full-thickness macular hole2 (Two postoperative subjects with closed macular holes)
VMT1
DME1
Authors

From Keck School of Medicine, University of Southern California, Los Angeles (JAL); and USC Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine University of Southern California, Los Angeles (AAM).

Supported in part by an unrestricted grant to the Department of Ophthalmology from Research to Prevent Blindness, New York.

Presented, in part, at the 42nd Annual Meeting of the Macula Society in Bonita Springs, FL, and at the Annual Meeting of the Association for Research and Vision in Ophthalmology in Vancouver, Canada, in 2019.

The authors report no relevant financial disclosures.

Address correspondence to Andrew A. Moshfeghi, MD, MBA, USC Roski Eye Institute, 1450 San Pablo St., Los Angeles, CA 90033; email: moshfega@med.usc.edu.

Received: May 27, 2019
Accepted: July 29, 2019

10.3928/23258160-20191211-02

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