The HRTII provides topographical measures of the optic disc and peripapillary retina and has been discussed in detail elsewhere.
1 Three scans centered on the optic disc were automatically obtained for each test eye, and a mean topography was created. Magnification errors were corrected by using patients’ corneal curvature measurements. The optic disc margin was outlined on the mean topography image by trained technicians while they viewed simultaneous stereoscopic photographs of the optic disc. All images included in the analysis were reviewed for adequate centration, focus, and illumination; all mean topography images had a standard deviation of <50 μm. The scans were obtained with HRT software version 1.5.9.0 or earlier, but were analyzed with the recently released software version 3.0.
HRT software version 3.0 includes improved alignment algorithms, a larger normative database, and the calculation of the GPS.
18 Two measures of peripapillary retinal nerve fiber layer shape (horizontal and vertical retinal nerve fiber layer curvature) and three measures of optic nerve head shape (cup depth, rim steepness, and cup size) are used as input into a relevance vector machine learning classifier to estimate the probability of having glaucoma as between 0% and 100%. Two mathematical functions are used to model the topography of the optic nerve head: (1) A Gaussian cumulative distribution function is used to model the optic disc, and (2) a quadratic (parabolic) surface is used to model the peripapillary retina. To parameterize the cup, a parabolic surface is fitted to the peripapillary region of each topograph. As outlined in Swindale et al.,
18 the parabolic surface, which serves as a reference plane for estimating the cup parameters, is then subtracted from the topography. The average location of the deepest points in the difference topograph is used to identify the cup center. In contrast to Swindale et al.,
18 the GPS constructs a cumulative Gaussian distribution of topograph heights to estimate the cup radius (
r) such that
p(radius ≤
r) = 0.5. The cup radius
r serves as a cup margin. Thus, the cup area is computed as the area of circle of radius
r and the mean cup depth is computed as the average height of the measurements inside the cup in the difference topograph. The rim steepness estimates are derived from the radial topograph–height gradients. No contour line or reference plane is used in the GPS calculation. GPS output is then automatically classified into three categories: outside normal limits (ONL; GPS > 64%), borderline (BL; GPS between 24% and 64%) and within normal limits (WNL; GPS < 24%).
The MRA compares measured rim area to predicted rim area adjusted for disc size, to categorize eyes as ONL, BL, or WNL.
9 It relies on a contour line and the standard reference plane (50 μm below the mean height of the contour in the temporal sector between 350° and 356°) for its measurements. By using the HRT 3.0 software, both the GPS and MRA classify eyes as within normal limits (WNL), borderline (BL), or outside normal limits (ONL), according to the same normative database of 700 eyes of whites and 200 eyes of African-Americans. For this analysis, the white normative database was used because most of the DIGS participants are of European descent. The comparison to the normative database is provided in six regions (superior temporal, inferior temporal, temporal, superior nasal, inferior nasal, and nasal), and as an overall global classification (if any of the six regions are ONL, then the eye is classified as ONL). For analysis using the MRA and GPS as categorical variables (ONL versus WNL), BL values were considered WNL for estimates of the sensitivity, specificity, and likelihood ratio.
In addition to estimating diagnostic accuracy by using the MRA and GPS as categorical variables, we evaluated the sensitivity of each at fixed specificities of 80% and 90%. We converted the MRA into a continuous variable by subtracting the predicted MRA from the actual MRA. This difference is used for determining whether the MRA is ONL. The difference between the predicted and actual MRA for each region was used to estimate sensitivity. For GPS the continuous variable used was the RVM output between 0 and 100.
The area under the receiver operator characteristic curve (AUROC) was calculated for both the three-level categorical variables (WNL, BL, and ONL) and for the MRA and GPS continuous variables MRA (predicted minus actual) and GPS (relevance vector machine output between 0%–100%).