ArticleScientific reports2025
Enhanced diplopia detection and binocular single vision assessment through virtual reality: A comprehensive study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aimed to develop a novel virtual reality (VR)-based binocular single vision (BSV) testing system for the quantitative assessment of diplopia and to evaluate its diagnostic accuracy and stability through clinical research. We first developed a VR-based BSV testing apparatus (VR-BSVT) using Oculus Quest 2 VR glasses and Unity software. The system provides three parameters for assessing subjects' binocular single vision function, and hence their diplopia: VR-BSVF (Virtual Reality-Based Binocular Single Vision Field area), VR-BSVD (Virtual Reality-Based Binocular Single Vision Distance), and VR-BAR (Virtual Reality-Based Binocular Single Vision Field area ratio). Subsequently, we conducted a clinical control study to systematically evaluate the accuracy and stability of VR-BSVT in the quantitative assessment of diplopia. In this comparative study, we recruited 31 visually healthy subjects and 35 patients diagnosed with diplopia. Each participant underwent two VR-BSVT assessments. The diagnostic accuracy of VR-BSVT in identifying diplopia was analyzed using receiver operating characteristic (ROC) curves, Spearman's rank correlation coefficient, and Bland-Altman analyses. Intraclass correlation coefficient (ICC) was employed to measure the diagnostic stability of VR-BSVT. Through human-computer interaction, VR-BSVT could rapidly detect diplopia and assess binocular single vision function, allowing for the detection of diplopia at different test distances. Among the 66 individuals who participated in the study, results from Intraclass correlation coefficient (ICC) for different test distances showed no significant differences in VR-BAR measurements at both near and far distances between healthy volunteers and patients with diplopia (P = 0.988), indicating good stability of VR-BSVT in diagnosing diplopia. Additionally, the VR-BSVF and VR-BSVD metrics were significantly reduced in the diplopia group compared to the healthy controls (P < 0.01). ROC analysis indicated that VR-BSVT could accurately discriminate patients with diplopia.The Bland-Altman plot revealed a 95% agreement range spanning from - 17.70 to 22.86. These results suggest that VR-BSVT has good precision in diagnosing diplopia. The VR-BSVT developed in this study achieves rapid, accurate, and stable detection and assessment of clinical diplopia, and utilizes virtual reality technology to detect diplopia over a larger visual space. With its compactness and portability, VR-BSVT holds promise for facilitating home healthcare and telemedicine in the future.
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