VALIDATION OF A DEEP LEARNING-BASED ALGORITHM FOR SEGMENTATION OF THE ELLIPSOID ZONE ON OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AN USH2A-RELATED RETINAL DEGENERATION CLINICAL TRIAL.

ABSTRACT:

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, me

SUBMITTER: Loo J 

PROVIDER: S-EPMC9232868 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature