Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.

Purpose: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients.

Methods: A deep neural network was used to calculate RADAR and DDLS for each optic disc photograph (ODP). Patient demographics, diagnosis, intraocular pressure (IOP), and mean deviation (MD) from perimetry were analyzed as risk factors for faster progression of RADAR.

Phenotypic expressions of the optic disc in primary open-angle glaucoma.

Background: Which phenotypes are we able to recognize in the optic nerve of patients with primary open angle glaucoma?

Methods: Retrospective interventional case series. 885 eyes from 885 patients at an outpatient tertiary care centre who met specified criteria for POAG were included. Disc photographs were classified by three glaucoma specialists into the following phenotypes according to their predominant characteristics: (1) concentric rim thinning, (2) focal rim thinning, (3) acquired pit of the optic nerve (APON), (4) tilted, (5) extensive peripapillary atrophy (PPA), and (6) broad rim thinning.

The Success Rate of Glaucoma Drainage Device Revision.

Prcis: We report the survival of surgical revision to glaucoma drainage devices for several indications in a large cohort of patients, with an overall success rate of 45% at 36 months.

Purpose: To evaluate the outcomes of surgical revision for complications of glaucoma drainage devices.

Methods: Three hundred thirty-five eyes of 318 patients who underwent tube revision or removal at University of California Los Angeles (UCLA) Jules Stein Eye Institute between 1997 and 2019 were included.

A Neural Network for Automated Image Quality Assessment of Optic Disc Photographs.

This study describes the development of a convolutional neural network (CNN) for automated assessment of optic disc photograph quality. Using a code-free deep learning platform, a total of 2377 optic disc photographs were used to develop a deep CNN capable of determining optic disc photograph quality. Of these, 1002 were good-quality images, 609 were acceptable-quality, and 766 were poor-quality images.