Focal Loss Analysis of Nerve Fiber Layer Reflectance for Glaucoma Diagnosis

Abstract

To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis.Methods: Participants were imaged with 4.5 × 4.5 mm volumetric disc scans using spectral-domain optical coherence tomography. The normalized NFL reflectance map was processed by an azimuthal filter to reduce directional reflectance bias caused by variation of beam incidence angle. The peripapillary area of the map was divided into 160 superpixels. Average reflectance was the mean of superpixel reflectance. Lowreflectance superpixels were identified as those with NFL reflectance below the fifth percentile normative cutoff. Focal reflectance loss was measured by summing loss in low-reflectance superpixels.Results: Thirty-five normal, 30 preperimetric, and 35 perimetric glaucoma participants were enrolled. Azimuthal filtering improved the repeatability of the normalized NFL reflectance, as measured by the pooled superpixel standard deviation (SD), from 0.73 to 0.57 dB (P < 0.001, paired t-test) and reduced the population SD from 2.14 to 1.78 dB (P < 0.001, t-test). Most glaucomatous reflectance maps showed characteristic patterns of contiguous wedge or diffuse defects. Focal NFL reflectance loss had significantly higher diagnostic sensitivity than the best NFL thickness parameter (from map or profile): 77% versus 55% (P < 0.001) in glaucoma eyes with the specificity fixed at 99%. Conclusions:Azimuthal filtering reduces the variability of NFL reflectance measurements. Focal NFL reflectance loss has excellent glaucoma diagnostic accuracy compared to the standard NFL thickness parameters. The reflectance map may be useful for localizing NFL defects.Translational Relevance: The high diagnostic accuracy of NFL reflectance may make population-based screening feasible.

Within‐eye and between‐subject variability for reflectance of the retinal nerve fibre layer

“…En face RNFL reflectance maps have been found to have potentially good diagnostic ability for glaucomatous damage, [12][13][14][15][16][17][18][19][20] and good agreement with patterns of perimetric loss. 20,21 Because measured reflectance varies with the amount of light hitting the retina, measures of AC have been developed by using the ratio of measured reflectances for different layers within the retina.…”

“…A related artefact from the choice of measurement is in en face RNFL thickness deviation maps, where arcuate defects occur in about 20% of eyes free of glaucoma 9 due to normal between-subject variability in the distance from the fovea to the arcades. 10,11 Several laboratories have reported that en face maps of RNFL reflectance show good potential for detecting glaucomatous damage, [12][13][14][15][16][17][18][19] and that by using fixed distances below the inner limiting membrane (ILM) the problems with normal between-subject variability in the distance from the fovea to the arcades can be avoided. 20,21 Measured reflectance will be affected by factors that reduce the amount of light that reaches the tissue, such as shadows from blood vessels or floaters and changes in the position of the eye, but not by factors that affect RNFL thickness such as axial length.…”

“…Most studies of RNFL reflectance have been performed near the optic disc. [12][13][14][15][16]19,24,25 Some key factors that affect variability in clinical measures used in patients with glaucoma are axial length and location of major blood vessels for RNFL thickness, age and within-subject variability for perimetry. Age has been found to cause a decline in AC, 15 and within-subject variability in AC has been noted for locations around the optic disc.…”

“…Age has been found to cause a decline in AC, 15 and within-subject variability in AC has been noted for locations around the optic disc. 15,19 This study compared within-eye and between-eye variability in a region temporal to the disc, and explored the effects of age and axial length. Effect sizes were evaluated in terms of logarithmic differences in means and SD, and the square of the correlation coefficient.…”

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

“…The metrics for the automatic segmentation methods of the main structures of the optic nerve head formed by the optic disc and the cupping are a process which can aid in tracking, identifying, and evaluating the progression of glaucomatous disease [116]. However, this is a complex process which requires consideration of the subtlety and variability of the anatomy of the nerve fiber layer, irregular contours due to glaucomatous damage to nerve fiber [117]. In addition, the presence of blood vessels can impair the excavation delineation or visualize the limits and excavation of the papilla by deflecting the regions of atrophy of the fiber layer [118].…”