A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

ABSTRACT: PURPOSE:To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT). DESIGN:Cross-sectional study. METHODS:A total of 9282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to the actual SDOCT measurements. The area under the receiver operating curve (AUC) was used to evaluate the ability of the netwo

SUBMITTER: Thompson AC 

PROVIDER: S-EPMC6884088 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature