Evaluating the Role of Virtual Reality Visual Fields (VRF) in the Diagnosis and Management of Pediatric Neuro-Ophthalmic Conditions.
Saleem Zara, Wang Bo, Naithani Rizul, Alvarez Samuel, Freedman Sharon F, El-Dairi Mays
AI Summary
This study found virtual reality visual fields (VRF) are comparable to standard tests for pediatric neuro-ophthalmic conditions, but more children successfully completed VRF, making it a valuable, more accessible clinical tool.
Abstract
Objective
Virtual reality field testing (VRF) is feasible and reliable in adults and in children with normal and glaucomatous eyes, but its role in pediatric neuro-ophthalmic conditions remains unexplored.
Purpose
To evaluate the feasibility of using VRF vs. standard automated perimetry (HVF) in children with neuro-ophthalmic conditions.
Design
Prospective reliability and validity comparison of diagnostic approaches.
Participants
Children (<18 years of age) with a known or suspected neuro-ophthalmic condition.
Methods
Children presenting to one pediatric neuro-ophthalmologist at Duke Eye Center between July 2022 to March 2023 were recruited. Participants attempted both HVF and VRF. A neuro-ophthalmologist predicted expected visual field (VF) abnormalities based on history, examination, magnetic resonance imaging, and optical coherence tomography of the retina and optic nerve. A masked ophthalmologist graded HVFs and VRFs with respect to global indices and VF abnormalities.
Main outcome measures
Comparison between clinically-predicted and observed visual field defects and visual field global indices between virtual reality fields (VRF) and Humphrey visual fields (HVF).
Results
One-hundred twenty-nine children (253 eyes) were included, mean age 11.1 ± 3.9 years. Fewer children successfully completed HVF than VRF (191 vs 240 eyes, p < .0001). Comparing HVF to VRF, average mean deviation (MD) was similar (-5.0 ± 6.3 vs -5.3 ± 6.2 dB, respectively, p = .782). When comparing global indices between HVF and VRF, moderate correlation was observed for both MD and pattern standard deviation (PSD). Sensitivity of HVF vs VRF to detect any clinically-predicted VF defect was 86.5% vs 88.6%, respectively, p = .861; specificity was 30.8% vs 33.5%, respectively, p = .725. HVF vs VRF concordance for presence/absence of any clinically-predicted VF defect was 52.0% vs 53.0%, respectively (p = .849).
Conclusion
HVF and VRF were comparable, with relatively high sensitivity and low specificity for the detection of clinically-predicted VF defects in children with known or suspected neuro-ophthalmic disease. However, more children were able to successfully perform VRF than HVF, suggesting VRF may provide valuable clinical assessment in this challenging population.
MeSH Terms
Shields Classification
Key Concepts4
In a prospective reliability and validity comparison study of 129 children (253 eyes) with known or suspected neuro-ophthalmic conditions, fewer children successfully completed Humphrey visual fields (HVF) than virtual reality visual fields (VRF) (191 vs 240 eyes, p < .0001).
In a prospective reliability and validity comparison study of 129 children (253 eyes) with known or suspected neuro-ophthalmic conditions, the average mean deviation (MD) was similar between Humphrey visual fields (HVF) and virtual reality visual fields (VRF) (-5.0 ± 6.3 vs -5.3 ± 6.2 dB, respectively, p = .782).
In a prospective reliability and validity comparison study of 129 children (253 eyes) with known or suspected neuro-ophthalmic conditions, the sensitivity of Humphrey visual fields (HVF) vs virtual reality visual fields (VRF) to detect any clinically-predicted visual field defect was 86.5% vs 88.6%, respectively (p = .861), and specificity was 30.8% vs 33.5%, respectively (p = .725).
In a prospective reliability and validity comparison study of 129 children (253 eyes) with known or suspected neuro-ophthalmic conditions, Humphrey visual fields (HVF) vs virtual reality visual fields (VRF) concordance for presence/absence of any clinically-predicted visual field defect was 52.0% vs 53.0%, respectively (p = .849).
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