Purpose: To develop an efficient approach to estimating visual field (VF) in patients with X-linked retinitis pigmentosa (RP) based on macular OCT scans.
Design: Retrospective analysis of patients who were enrolled in a natural history study at Moorfields Eye Hospital (London, United Kingdom).
Subjects: Male patients with genetically confirmed retinitis pigmentosa GTPase regulator (RPGR)-associated RP.
Methods: Visual field raw data were exported and analyzed including Visual Field Modeling and Analysis software. Retinal imaging consisted of OCT macular scans. Paired imaging and VF data acquired within a 1-month range were jointly analyzed. Artificial intelligence (AI) was used to automatically segment and quantify macular ellipsoid zone width (EZW), and ellipsoid zone area (EZA).
Main outcome measures: Functional parameters from static VF testing such as mean sensitivity (MS) and Hill of Vision analysis that included total volume (VTOT), volume of central 20° (V20), and volume of central 30° (V30) were predicted from EZW and EZA.
Results: Patient age ranged from 5 to 55 years old at baseline. A total of 332 OCT-VF pairs were analyzed. Ellipsoid zone area had the highest conditional R2 (R2c) and most significant associations with MS and V20. There were significant associations between MS and EZW (P = 0.00176), and MS with EZA (P = 0.0009).
Conclusions: This study showed that AI enables efficient acquiring of large amounts of structural OCT parameters, facilitating research and structure-function predictions. The cohort included patients with a wide range of disease severity and statistical significance was achieved with parameters representing a wide range of VF, proving that this method can be applied for patients with milder disease.
Financial disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Keywords: AI; Genetics; Retina; Retinitis pigmentosa; Visual field.
© 2025 by the American Academy of Ophthalmology.