Deep Learning-Based SD-OCT Layer Segmentation Quantifies Outer Retina Changes in Patients With Biallelic RPE65 Mutations Undergoing Gene Therapy.

Purpose: To quantify outer retina structural changes and define novel biomarkers of inherited retinal degeneration associated with biallelic mutations in RPE65 (RPE65-IRD) in patients before and after subretinal gene augmentation therapy with voretigene neparvovec (Luxturna).

Methods: Application of advanced deep learning for automated retinal layer segmentation, specifically tailored for RPE65-IRD. Quantification of five novel biomarkers for the ellipsoid zone (EZ): thickness, granularity, reflectivity, and intensity. Estimation of the EZarea in single and volume scans was performed with optimized segmentation boundaries. The control group was age similar and without significant refractive error. Spherical equivalent refraction and ocular length were evaluated in all patients.

Results: We observed significant differences in the structural analysis of EZ biomarkers in 22 patients with RPE65-IRD compared with 94 healthy controls. Relative EZ intensities were already reduced in pediatric eyes. Reductions of EZ local granularity and EZ thickness were only significant in adult eyes. Distances of the outer plexiform layer, external limiting membrane, and Bruch's membrane to EZ were reduced at all ages. EZ diameter and area were better preserved in pediatric eyes undergoing therapy with voretigene neparvovec and in patients with a milder phenotype.

Conclusions: Automated quantitative analysis of biomarkers within EZ visualizes distinct structural differences in the outer retina of patients including treatment-related effects. The automated approach using deep learning strategies allows big data analysis for distinct forms of inherited retinal degeneration. Limitations include a small dataset and potential effects on OCT scans from myopia at least -5 diopters, the latter considered nonsignificant for outer retinal layers.