0000000000754983
AUTHOR
James A. Proudfoot
Macula structural and vascular differences in glaucoma eyes with and without high axial myopia
AbstractAimsTo assess the thickness of various retinal layers, and the superficial vessel density (sVD) in the macula of glaucomatous eyes and their associations with axial length (AL) and visual field mean deviation (VFMD) to identify parameters useful for glaucoma management in myopic eyes.Methods248 glaucoma patients (401 eyes) participating in the Diagnostic Innovations in Glaucoma Study observational cohort representing 3 axial myopia groups (non-myopia: n=146 eyes; mild myopia: n=208 eyes; high myopia (AL>26 mm): n=47 eyes) who completed macular OCT and OCT-Angiography imaging were included. The cross-sectional associations of AL and VFMD with the thickness of the ganglion cell inn…
Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms
Author(s): Christopher, Mark; Nakahara, Kenichi; Bowd, Christopher; Proudfoot, James A; Belghith, Akram; Goldbaum, Michael H; Rezapour, Jasmin; Weinreb, Robert N; Fazio, Massimo A; Girkin, Christopher A; Liebmann, Jeffrey M; De Moraes, Gustavo; Murata, Hiroshi; Tokumo, Kana; Shibata, Naoto; Fujino, Yuri; Matsuura, Masato; Kiuchi, Yoshiaki; Tanito, Masaki; Asaoka, Ryo; Zangwill, Linda M | Abstract: PurposeTo compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.MethodsTwo fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent…
Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT.
Purpose To develop deep learning (DL) systems estimating visual function from macula-centered spectral-domain (SD) OCT images. Design Evaluation of a diagnostic technology. Participants A total of 2408 10-2 visual field (VF) SD OCT pairs and 2999 24-2 VF SD OCT pairs collected from 645 healthy and glaucoma subjects (1222 eyes). Methods Deep learning models were trained on thickness maps from Spectralis macula SD OCT to estimate 10-2 and 24-2 VF mean deviation (MD) and pattern standard deviation (PSD). Individual and combined DL models were trained using thickness data from 6 layers (retinal nerve fiber layer [RNFL], ganglion cell layer [GCL], inner plexiform layer [IPL], ganglion cell-IPL […