BACKGROUND
Myopia, especially high myopia is a major risk factor for the presence of glaucoma. Due to the axial elongation-associated changes in the optic nerve and retina in high myopia, traditional methods like optic disc evaluation and visual field were not able to correctly differentiate the glaucomatous lesions. It has been a clinical emphasis and challenge to detect glaucoma in highly myopic eyes. Previous studies have shown that the distribution of retinal nerve fiber layer (RNFL) depended on axial length and other morphologic parameters. It was assumed that a neural network can transform the RNFL profile and make it thus comparable in eyes with varied axial length, thus improve the diagnosis of glaucoma with special emphasis on myopic and highly myopic eyes.
OBJECTIVE
To develop a neural network for adjusting the dependence of the peripapillary RNFL thickness (RNFLT) profile on age, gender and ocular biometric parameters, and to evaluate its performance in glaucoma diagnosis, especially in high myopia.
METHODS
RNFLT with 768 points at the circumferential 3.4 mm scan was measured using spectral-domain OCT. A fully connected network and a radial basis function network were trained for vertical (scaling) and horizontal (shift) transformation of the RNFLT profile with adjustment for age, axial length (AL), disc-fovea angle and distance, in a test group of 2223 non-glaucomatous eyes. The performance of RNFLT compensation was evaluated in an independent group of 254 glaucoma patients and 254 non-glaucomatous participants.
RESULTS
By applying the RNFL compensation algorithm, the AUROC in detecting glaucoma increased from 0.70 to 0.84, from 0.75 to 0.89, from 0.77 to 0.89, and from 0.78 to 0.87, for eyes in the highest 10% (mean: 26.0±0.9mm), 20% (25.3±1.0mm), and highest 30% (24.9±1.0mm) percentile subgroup of the AL distribution, and in eyes of any AL (23.5±1.2mm), in comparing with unadjusted RNFLT, respectively. The difference between uncompensated versus compensated RNFLT values increased with longer axial length, with enlargement of 19.8%, 18.9%, 16.2% and 11.3% in the highest 10%, 20% and 30% percentile subgroups, and in all eyes, respectively.
CONCLUSIONS
In a population-based study sample, an algorithm-based adjustment for age, gender and ocular biometric parameters improved the diagnostic precision of the RNFLT profile for glaucoma detection in particular in myopic and highly myopic eyes.
Retinal Nerve Fiber Layer Reflectance for Early Glaucoma Diagnosis
