Estimating Optical Coherence Tomography Structural Measurement Floors to Improve Detection of Progression in Advanced Glaucoma.
Summary
In advanced glaucoma, more GC-IPL tissue remains above the measurement floor compared with other measurements, suggesting GC-IPL thickness is the better candidate for detecting progression. Progression in SDOCT measurements is observable in advanced disease.
Abstract
PURPOSE
"Floor effects" in retinal imaging are defined as the points at which no further structural loss can be detected. We estimated the measurement floors for spectral-domain optical coherence tomography (SDOCT) measurements and compared global change over time in advanced glaucoma eyes.
DESIGN
Validity study to investigate measurement floors.
METHODS
A longitudinal "Variability group" of 41 eyes with moderate to advanced glaucoma (standard automated perimetry mean deviation ≤-8 dB) was used to estimate measurement floors. Minimum rim width (MRW), ganglion cell-inner plexiform layer thickness (GC-IPLT), and circumpapillary retinal nerve fiber layer thickness (cpRNFLT) were determined. Floors were defined as the average image area with a loss less than first-percentile confidence interval of the variability in this group. Global rate of change and percentage of the region of interest that did not reach the measurement floor at baseline were calculated in 87 eyes with advanced glaucoma (SAP MD ≤-12 dB).
RESULTS
Global change over time in longitudinal eyes was -1.51 μm/year for MRW, -0.21 μm/year for GC-IPL, and -0.36 μm/year cpRNFL (all P ≤ .03). The percentage of region of interest that did not reach the floor at baseline was 19% for MRW, 36% for GC-IPLT, and 14% for cpRNFLT. Average (± standard deviation) floors were 105 μm (± 15.9 μm) for MRW, 38 μm (± 3.4 μm) for GC-IPLT, and 38 μm (± 4.2 μm) for cpRNFLT.
CONCLUSIONS
In advanced glaucoma, more GC-IPL tissue remains above the measurement floor compared with other measurements, suggesting GC-IPL thickness is the better candidate for detecting progression. Progression in SDOCT measurements is observable in advanced disease.
More by Christopher Bowd
View full profile →Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.
Measurement Floors and Dynamic Ranges of OCT and OCT Angiography in Glaucoma.
Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.
Top Research in Optic Nerve & Disc
Browse all →Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.
Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma.
Inflammation in Glaucoma: From the back to the front of the eye, and beyond.
Discussion
Comments and discussion will appear here in a future update.