Virtual Reality-Based and Conventional Visual Field Examination Comparison in Healthy and Glaucoma Patients

Abstract

Purpose: Clinically evaluate the noninferiority of a custom virtual reality (VR) perimetry system when compared to a clinically and routinely used perimeter on both healthy subjects and glaucoma patients.

Methods: We use a custom-designed VR perimetry system tailored for visual field testing. The system uses Oculus Quest VR headset (Facebook Technologies, LLC, Bern, Switzerland), that includes a clicker for participant response feedback. A prospective, single center, study was conducted at the Department of Ophthalmology of the Bern University Hospital (Bern, Switzerland) for 12 months. Of the 114 participants recruited 70 subjects (36 healthy and 34 glaucoma patients with early to moderate visual field loss) were included in the study. Participants underwent perimetry tests on an Octopus 900 (Haag-Streit, Köniz, Switzerland) as well as on the custom VR perimeter. In both cases, standard dynamic strategy (DS) was used in conjunction with the G testing pattern. Collected visual fields (VFs) from both devices were then analyzed and compared.

Results: High mean defect (MD) correlations between the two systems (Spearman, ρ ≥ 0.75) were obtained. The VR system was found to slightly underestimate VF defects in glaucoma subjects (1.4 dB). No significant bias was found with respect to eccentricity or subject age. On average, a similar number of stimuli presentations per VF was necessary when measuring glaucoma patients and healthy subjects.

Conclusions: This study demonstrates that a clinically used perimeter and the proposed VR perimetry system have comparable performances with respect to a number of perimetry parameters in healthy and glaucoma patients with early to moderate visual field loss.

Translational relevance: This suggests that VR perimeters have the potential to assess VFs with high enough confidence, whereby alleviating challenges in current perimetry practices by providing a portable and more accessible visual field test.

Figure 1.

Oculus Quest VR headset with the Bluetooth connected clicker.

Figure 2.

Screen calibration measurements of the VR headset shows relationship of RGB values to luminance and dB values, respectively. The vertical lines indicate the values that are used for the dynamic units of the testing strategy.

Figure 3.

Example of sensitivity threshold values for 55-year-old subjects. Normative values decrease towards the periphery, showing the “hill of vision,” as in other perimetry devices.

Figure 4.

Mean defect correlations between all (left), healthy (middle), and glaucoma (right) subjects. Spearman's rank correlation coefficient ρ values with the corresponding P values are given on each plot. Red dotted line corresponds to best fit line.

Figure 5.

Estimation bias of MD measurements for all (left), healthy (middle), and glaucoma (right) patients. Mean and standard deviations (SD) are given in each plot.

Figure 6.

Estimation bias on MD estimation with respect to the age of the patient for healthy (left) and glaucoma (right) patients. P values are provided on each plot (Kruskal-Wallis test).

Figure 7.

Estimation bias on total deviation with respect to the eccentricity of the patient for healthy (left) and glaucoma (right) subjects. P values are provided on each plot (Kruskal-Wallis test).

Figure 8.

Mean (top row) and standard deviation (bottom row) of TD differences with respect to the spatial location for healthy (left) and glaucoma (right) subjects.

Figure 9.

Bland-Altman agreement graphs between Octopus 900 and VR device MD measurements. The black dotted line corresponds to the mean difference and red dotted lines correspond to 95% limits of agreements (mean ± 1.96).

Figure 10.

Distributions of examination duration for VR perimetry and Octopus 900 presented for all (left), healthy (middle), and glaucoma patients (right). For each subplot, P values are provided (Kruskal-Wallis test).

Figure 11.

Distributions of number of presented stimuli for VR perimetry and Octopus 900 for all (left), healthy (middle), and glaucoma patients (right). For each subplot, P values are provided (Kruskal-Wallis test).

Figure 12.

Two healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). The black circle corresponds to the blind spot. Blue colors reflect higher deviations, that is, deeper defects.

Figure 13.

Two glaucomatous VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). The black circle corresponds to the blind spot. Blue colors reflect higher deviations, that is, deeper defects.

Figure 14.

The differences between individual total deviation values with respect to the gradient measure ∆_l given for healthy and glaucoma subjects separately. A high ∆_l value indicates that the corresponding locations are inside a heterogeneous region, which is more difficult to accurately measure.

Figure 15.

Distributions of the reliability indices, namely false positive (left)/negative (right) response rates of each device. For each subplot, P values are provided (Mann-Whitney U test).

Figure 16.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 17.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 18.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 19.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 20.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 21.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 22.

Five healthy VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 23.

One healthy VF example. It compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 24.

Five glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 25.

Five glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 26.

Five glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 27.

Five glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 28.

Five glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 29.

Five glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.

Figure 30.

Four glaucoma VF examples. Each row compares acquisitions by VR perimetry (left) and by Octopus 900 (right). Black circle corresponds to the blind spot. Bluish colors reflect higher deviations, that is, deeper defects.