Abstract
BackgroundDue to the axial elongation–associated changes in the optic nerve and retina in high myopia, traditional methods like optic disc evaluation and visual field are not able to correctly differentiate glaucomatous lesions. It has been clinically challenging to detect glaucoma in highly myopic eyes.ObjectiveThis study aimed to develop a neural network to adjust for the dependence of the peripapillary retinal nerve fiber layer (RNFL) thickness (RNFLT) profile on age, gender, and ocular biometric parameters and to evaluate the network’s performance for glaucoma diagnosis, especially in high myopia.MethodsRNFLT with 768 points on the circumferential 3.4-mm scan was measured using spectral-domain optical coherence tomography. 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 nonglaucomatous eyes. The performance of RNFLT compensation was evaluated in an independent group of 254 glaucoma patients and 254 nonglaucomatous participants.ResultsBy applying the RNFL compensation algorithm, the area under the receiver operating characteristic curve for 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% percentile subgroup of the AL distribution (mean 26.0, SD 0.9 mm), highest 20% percentile subgroup of the AL distribution (mean 25.3, SD 1.0 mm), highest 30% percentile subgroup of the AL distribution (mean 24.9, SD 1.0 mm), and any AL (mean 23.5, SD 1.2 mm), respectively, in comparison with unadjusted RNFLT. The difference between uncompensated and compensated RNFLT values increased with longer axial length, with enlargement of 19.8%, 18.9%, 16.2%, and 11.3% in the highest 10% percentile subgroup, highest 20% percentile subgroup, highest 30% percentile subgroup, and all eyes, respectively.ConclusionsIn 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 particularly in myopic and highly myopic eyes.
Highlights
Glaucoma, as one of the most common causes of irreversible vision impairment and blindness, is diagnosed by the morphometric analysis of the optic nerve head including the peripapillary retinal nerve fiber layer (RNFL) and by psychophysical techniques such as perimetry [1,2,3]
In a population-based study sample, an algorithm-based adjustment for age, gender, and ocular biometric parameters improved the diagnostic precision of the RNFL thickness (RNFLT) profile for glaucoma detection in myopic and highly myopic eyes
The prevalence of glaucomatous or glaucoma-like optic neuropathy increases with longer axial length, especially beyond an axial length of 26.5 mm, with odds ratios ranging from 1.6 to 3.75 for all myopic eyes and from 3.3 to 4.6 for highly myopic eyes [12,13,14]. These findings show the need to further improve the available methods to refine the diagnosis of glaucomatous optic neuropathy in myopic eyes
Summary
As one of the most common causes of irreversible vision impairment and blindness, is diagnosed by the morphometric analysis of the optic nerve head including the peripapillary retinal nerve fiber layer (RNFL) and by psychophysical techniques such as perimetry [1,2,3]. Due to irregularities in the refractive error and shape of the posterior part of the globe and due to high myopia-associated morphological changes in the macular region, perimetric defects lose their specificity for glaucoma and can have a multitude of causes, in addition to glaucomatous optic nerve damage [6] Morphometric methods such as assessment of the neuroretinal rim of the optic disc and measurement of the peripapillary RNFL thickness (RNFLT) become more limited with a greater axial length of the eyes [7,8,9,10,11]. It has been clinically challenging to detect glaucoma in highly myopic eyes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
