LAUSR

We validate the clinical applicability and generalizability of deep OCT atrophy detection, a deep learning-based algorithm originally developed for macular telangiectasia, to segment the ellipsoid zone on optical coherence tomography images of eyes with USH2A-related degeneration. The algorithm performed well on a diverse dataset from the large-scale, international, multicenter RUSH2A clinical trial (NCT03146078). Purpose: To assess the generalizability of a deep learning-based algorithm to segment the ellipsoid zone (EZ). Methods: The dataset consisted of 127 spectral-domain optical coherence tomography volumes from eyes of participants with USH2A-related retinal degeneration enrolled in the RUSH2A clinical trial (NCT03146078). The EZ was segmented manually by trained readers and automatically by deep OCT atrophy detection, a deep learning-based algorithm originally developed for macular telangiectasia Type 2. Performance was evaluated using the Dice similarity coefficient between the segmentations, and the absolute difference and Pearson's correlation of measurements of interest obtained from the segmentations. Results: With deep OCT atrophy detection, the average (mean ± SD, median) Dice similarity coefficient was 0.79 ± 0.27, 0.90. The average absolute difference in total EZ area was 0.62 ± 1.41, 0.22 mm2 with a correlation of 0.97. The average absolute difference in the maximum EZ length was 222 ± 288, 126 µm with a correlation of 0.97. Conclusion: Deep OCT atrophy detection segmented EZ in USH2A-related retinal degeneration with good performance. The algorithm is potentially generalizable to other diseases and other biomarkers of interest as well, which is an important aspect of clinical applicability.