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

Published

Journal Article

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 network to discriminate glaucomatous visual field loss from normal eyes. RESULTS: The DL predictions of global BMO-MRW from all optic disc photographs in the test set (mean ± standard deviation [SD]: 228.8 ± 63.1 μm) were highly correlated with the observed values from SDOCT (mean ± SD: 226.0 ± 73.8 μm) (Pearson's r = 0.88; R2 = 77%; P < .001), with mean absolute error of the predictions of 27.8 μm. The AUCs for discriminating glaucomatous from healthy eyes with the DL predictions and actual SDOCT global BMO-MRW measurements were 0.945 (95% confidence interval [CI]: 0.874-0.980) and 0.933 (95% CI: 0.856-0.975), respectively (P = .587). CONCLUSIONS: A DL network can be trained to quantify the amount of neuroretinal damage on optic disc photographs using SDOCT BMO-MRW as a reference. This algorithm showed high accuracy for glaucoma detection, and may potentially eliminate the need for human gradings of disc photographs.

Full Text

Duke Authors

Cited Authors

  • Thompson, AC; Jammal, AA; Medeiros, FA

Published Date

  • May 2019

Published In

Volume / Issue

  • 201 /

Start / End Page

  • 9 - 18

PubMed ID

  • 30689990

Pubmed Central ID

  • 30689990

Electronic International Standard Serial Number (EISSN)

  • 1879-1891

Digital Object Identifier (DOI)

  • 10.1016/j.ajo.2019.01.011

Language

  • eng

Conference Location

  • United States