Diagnosis of posterior staphyloma using the radius of steepest curvature among retinal pigment epithelium segmentation line measured by optic coherent tomography.

PURPOSE: To investigate a novel marker to diagnose posterior staphylomas by measuring the radius of the steepest curvature on the retinal pigment epithelium (RPE) segmentation line using optical coherence tomography (OCT). STUDY DESIGN: Retrospective Cross-sectional Study. METHODS: The authors developed a prototype software to measure the radius of curvature on the RPE segmentation line of OCT. Twelve images of 9-mm radial OCT scans were used. The radius of curvature was measured at the steepest area of the RPE segmentation line, and the macular curvature (MC) index was calculated based on its reciprocal. Based on the wide-field fundus findings, the study sample was divided into three groups: definite posterior staphyloma, no posterior staphyloma, and undetermined. The differences of MC index among the groups and the correlation between the MC index, age, and axial length were analyzed. RESULTS: The present study analyzed 268 eyes, with 54 (20.1%) with definite posterior staphyloma, 202 (75.4%) with no posterior staphyloma, and 12 (4.5%) with undetermined disease status. A maximum MC index of 37.5 was observed in the group with no posterior staphyloma, which was less than the minimum MC index of 42.7 observed in the group with definite posterior staphyloma. The MC index had strong correlations with the axial length and age in eyes with high myopia. CONCLUSIONS: Eyes with posterior staphyloma have a steeper curvature than those with radius 8.44 mm, while eyes without posterior staphyloma do not. MC index 40 (radius 8.44 mm) might act as a reference to distinguish between those with and those without posterior staphyloma.

Optic Nerve Head Curvature Flattening Is Associated with Central Visual Field Scotoma.

This study aimed to develop a new index, the average curvature ratio (ACR), to represent the optic nerve head (ONH) tilting and investigate its clinical relevance. Myopic eyes were included and divided into two subgroups: flat ONH (ACR < 1.0) and convex ONH (ACR ≥ 1.0). The occurrences of central and peripheral visual field (VF) defects were compared between the two groups. A total of 375 myopic eyes were recruited, and 231 and 144 eyes were included in the flat and convex ONH groups, respectively. Central scotoma occurred more frequently in the flat ONH group. According to the Patella-Anderson criteria, the number of eyes with central scotoma was 103 (44.6%) in the flat and 44 (30.6%) in the convex ONH groups (p = 0.009). According to Kook's criteria, the number of eyes with central scotoma was 122 (52.8%) in the flat and 50 (34.7%) in the convex ONH groups (p < 0.001). Peripheral scotoma was not significantly different between the groups. In the correlation analysis, the ACR was positively correlated with spherical equivalence, but not with axial length or central corneal thickness. The ACR reflects the degree of the ONH tilt and is a good index for estimating central VF damage in myopic eyes.

The choroidal microvasculature of the parapapillary area as a biomarker of glaucoma development in the fellow eye of patients with unilateral glaucoma.

PURPOSE: To investigate the factors associated with the development of glaucoma in the healthy eyes of unilateral glaucoma patients. MATERIALS AND METHODS: This was a retrospective observational case series study. All participants had unilateral primary open-angle glaucoma at the initial visit and were divided into two groups: one in which the fellow eyes developed glaucoma during the follow-up period and one in which the fellow eyes remained healthy. A complete ophthalmic examination, including best-corrected visual acuity testing, slit-lamp examination, intraocular pressure measurement, retinal nerve fiber layer and optic disk photographs, a 30-2 visual field test, and optical coherence tomography with angiography, was performed over a follow-up period of at least 3 years. RESULTS: A total of fifty-six patients were enrolled, and over the course of the study period, 11 patients developed glaucoma in the fellow eyes, while the fellow eyes of 45 patients remained healthy. At the baseline, the glaucomatous eye had a larger area of beta parapapillary atrophy, lower parapapillary choroidal vascular density (pCVD) within the area, and a lower prevalence of microvascular dropout than normal fellow eyes (P < 0.001, 0.013, 0.001, respectively). In the multivariate analysis, a reduced pCVD in the gamma parapapillary atrophy (γPPA) region was significantly associated with the development of glaucoma in normal eyes (odds ratio, 0.566; 95% CI, 0.342, 0.935; P = 0.026). CONCLUSIONS: The pCVD within the γPPA region at baseline is the risk factor for the development of glaucoma in the normal fellow eye of patients with unilateral glaucoma.

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.

Purpose: To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them. Methods: Two deep learning models based on Inception-ResNet-v2 were trained to estimate 24-2 VF from SS-OCT and SD-OCT images. The estimation performance of the two models was evaluated by using the root mean square error between the actual and estimated VF. The performance was also compared among different glaucoma severities, Garway-Heath sectorizations, and central/peripheral regions. Results: The training dataset comprised images of 4391 eyes from 2350 subjects, and the test dataset was obtained from another 243 subjects (243 eyes). In all subjects, the global estimation errors were 5.29 ± 2.68 dB (SD-OCT) and 4.51 ± 2.54 dB (SS-OCT), and the estimation error of SS-OCT was significantly lower than that of SD-OCT (P < 0.001). In the analysis of sectors, SS-OCT showed better performance in all sectors except for the inferonasal sector in normal vision and early glaucoma. In advanced glaucoma, the estimation error of the central region was worsened in both OCTs, but SS-OCT was still significantly better in the peripheral region. Conclusions: Our deep learning model estimated the VF 24-2 better with a wide field image of SS-OCT than did with retinal nerve fiber layer and ganglion cell-inner plexiform layer images of SD-OCT. Translational Relevance: This deep learning method can help clinicians to determine the VF from OCT images. OCT manufacturers can equip this system to provide additional VF data.

Relationship between peripapillary vessel density and visual function based on Garway-Heath sectorization in open-angle glaucoma.

Purpose: To investigate the relationship between peripapillary vessel density (pVD) and visual field sensitivity (VFS) and between peripapillary retinal nerve fiber layer thickness (pRNFLT) and VFS, based on Garway-Heath sectorization in open-angle glaucoma patients. Methods: Sixty-six eyes of healthy subjects and 84 eyes of glaucoma subjects were included. All subjects underwent several eye examinations, including standard automated perimetry and optical coherence tomography angiography. Sectoral structure-function relationships based on the Garway-Heath sectorization were compared among normal subjects, the 'mild glaucoma,' and 'moderate-to-severe glaucoma' group. Multivariate analyses were performed for each sector to determine the factors related to VFS. The diagnostic abilities of vessel density parameters and RNFLT were evaluated by calculating the area under the receiver operating characteristic curves (AUROC). Results: The correlation between pVD-VFS and pRNFLT-VFS was statistically significant in the glaucoma group independent of the VFS sector. In the glaucoma group, VFS in the temportal sector was statistically related in a multivariate model to pVD, pRNFLT and age (R2 = 0.721; P = 0.007, < 0.001, .15, respectively). We found pRNFLT and age were significantly associated with VFS in glaucoma. The AUROC values of pVD in the inferotemporal sector of the total, mild, and moderate-to-severe glaucoma (0.843, 0.714, and 0.972, respectively) were comparable to pRNFLT in this sector (0.833, 0.718, 0.948, respectively). Conclusion: Since the relationship between pVD and VFS in the papillomacular area was significant, measuring pVD and RNFLT in the corresponding area will be valuable in expanding our pathophysiologic knowledge of the paracentral field defects in glaucoma.

Association between metabolic risk factors and optic disc cupping identified by deep learning method.

PURPOSE: This study aims to investigate correlation between metabolic risk factors and optic disc cupping and the development of glaucoma. METHODS: This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photographs, Body Mass Index (BMI), waist circumference (WC), serum triglycerides, serum HDL cholesterol (HDL-C), serum LDL cholesterol (LDL-C), systolic blood pressure (BP), diastolic BP, and serum HbA1c were obtained to analyse correlation between metabolic risk factors and glaucoma. Eye with glaucomatous optic neuropathy(GON) was defined as having an optic disc with either vertical cup-to-disc ratio(VCDR) ≥ 0.7 or a VCDR difference ≥ 0.2 between the right and left eyes by measuring VCDR with deep learning approach. RESULTS: The study comprised 15,585 subjects and 877 subjects were diagnosed as GON. In univariate analyses, age, BMI, systolic BP, diastolic BP, WC, triglyceride, LDL-C, HbA1c, and IOP were significantly and positively correlated with VCDR in the optic nerve head. In linear regression analysis as independent variables, stepwise multiple regression analyses revealed that age, BMI, systolic BP, HbA1c, and IOP showed positive correlation with VCDR. In multivariate logistic analyses of risk factors and GON, higher age (odds ratio [OR], 1.054; 95% confidence interval [CI], 1.046-1.063), male gender (OR, 0.730; 95% CI, 0.609-0.876), more obese (OR, 1.267; 95% CI, 1.065-1.507), and diabetes (OR, 1.575; 95% CI, 1.214-2.043) remained statistically significant correlation with GON. CONCLUSIONS: Among the metabolic risk factors, obesity and diabetes as well as older age and male gender are risk factors of developing GON. The glaucoma screening examinations should be considered in the populations with these indicated risk factors.

Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms.

PURPOSE: To develop a deep learning method to predict visual field (VF) from wide-angle swept-source optical coherence tomography (SS-OCT) and compare the performance of three Google Inception architectures. METHODS: Three deep learning models (with Inception-ResNet-v2, Inception-v3, and Inception-v4) were trained to predict 24-2 VF from the macular ganglion cell-inner plexiform layer and the peripapillary retinal nerve fibre layer map obtained by SS-OCT. The prediction performance of the three models was evaluated by using the root mean square error (RMSE) between the actual and predicted VF. The performance was also compared among different glaucoma severities and Garway-Heath sectorizations. RESULTS: The training dataset comprised images of 2220 eyes from 1120 subjects, and the test dataset was obtained from another 305 subjects (305 eyes). In all subjects, the global prediction errors (RMSEs) were 4.44 ± 2.09 dB, 4.78 ± 2.38 dB, and 4.85 ± 2.66 dB for the Inception-ResNet-v2, Inception-v3, and Inception-v4 architectures, respectively, and the prediction error of Inception-ResNet-v2 was significantly lower than the other two (P < 0.001). As glaucoma progressed, the prediction error of all three architectures significantly worsened to 6.59 dB, 7.33 dB, and 7.79 dB, respectively. In the analysis of sectors, the nasal sector had the lowest prediction error, followed by the superotemporal sector. CONCLUSIONS: Inception-ResNet-v2 achieved the best performance, and the global prediction error (RMSE) was 4.44 dB. As glaucoma progressed, the prediction error became larger. This method may help clinicians determine VF, particularly for patients who are unable to undergo a physical VF test.

A deep learning approach to predict visual field using optical coherence tomography.

We developed a deep learning architecture based on Inception V3 to predict visual field using optical coherence tomography (OCT) imaging and evaluated its performance. Two OCT images, macular ganglion cell-inner plexiform layer (mGCIPL) and peripapillary retinal nerve fibre layer (pRNFL) thicknesses, were acquired and combined. A convolutional neural network architecture was constructed to predict visual field using this combined OCT image. The root mean square error (RMSE) between the actual and predicted visual fields was calculated to evaluate the performance. Globally (the entire visual field area), the RMSE for all patients was 4.79 ± 2.56 dB, with 3.27 dB and 5.27 dB for the normal and glaucoma groups, respectively. The RMSE of the macular region (4.40 dB) was higher than that of the peripheral region (4.29 dB) for all subjects. In normal subjects, the RMSE of the macular region (2.45 dB) was significantly lower than that of the peripheral region (3.11 dB), whereas in glaucoma subjects, the RMSE was higher (5.62 dB versus 5.03 dB, respectively). The deep learning method effectively predicted the visual field 24-2 using the combined OCT image. This method may help clinicians determine visual fields, particularly for patients who are unable to undergo a physical visual field exam.

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of ophthalmology. This study compared the performance of various state-of-the-art deep-learning architectures for detecting the optic nerve head and vertical cup-to-disc ratio in fundus images. Three different architectures were compared: YOLO V3, ResNet, and DenseNet. We compared various aspects of performance, which were not confined to the accuracy of detection but included, as well, the processing time, diagnostic performance, effect of the graphic processing unit (GPU), and image resolution. In general, as the input image resolution increased, the classification accuracy, localization error, and diagnostic performance all improved, but the optimal architecture differed depending on the resolution. The processing time was significantly accelerated with GPU assistance; even at the high resolution of 832 × 832, it was approximately 170 ms, which was at least 26 times slower without GPU. The choice of architecture may depend on the researcher's purpose when balancing between speed and accuracy. This study provides a guideline to determine deep learning architecture, optimal image resolution, and the appropriate hardware.

Author Correction: Visual Field Prediction using Recurrent Neural Network.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Visual Field Prediction using Recurrent Neural Network.

Artificial intelligence capabilities have, recently, greatly improved. In the past few years, one of the deep learning algorithms, the recurrent neural network (RNN), has shown an outstanding ability in sequence labeling and prediction tasks for sequential data. We built a reliable visual field prediction algorithm using RNN and evaluated its performance in comparison with the conventional pointwise ordinary linear regression (OLR) method. A total of 1,408 eyes were used as a training dataset and another dataset, comprising 281 eyes, was used as a test dataset. Five consecutive visual field tests were provided to the constructed RNN as input and a 6th visual field test was compared with the output of the RNN. The performance of the RNN was compared with that of OLR by predicting the 6th visual field in the test dataset. The overall prediction performance of RNN was significantly better than OLR. The pointwise prediction error of the RNN was significantly smaller than that of the OLR in most areas known to be vulnerable to glaucomatous damage. The RNN was also more robust and reliable regarding worsening in the visual field examination. In clinical practice, the RNN model can therefore assist in decision-making for further treatment of glaucoma.

The Relationship Between Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness and a New Index Using a Neural Network.

PURPOSE: We evaluate the relationship between Bruch's membrane opening minimum rim width (BMO-MRW) and peripapillary retinal nerve fiber layer thickness (pRNFLT) and develop a new parameter combining BMO-MRW and pRNFLT using a neural network to maximize their compensatory values. METHODS: A total of 402 subjects were divided into two groups: 273 (validation group) and 129 (neural net training) subjects. Linear quadratic and broken-stick regression models were used to explore the relationship between BMO-MRW and pRNFLT. A multilayer neural network was used to create a combined parameter, and diagnostic performances were compared using area under the receiver operating characteristic curves (AUROCs). RESULTS: Regression analyses between BMO-MRW and pRNFLT revealed that the broken-stick model afforded the best fit. Globally, the tipping point was a BMO-MRW of 226.5 µm. BMO-MRW and pRNFLT were correlated significantly with visual field. When differentiating normal from glaucoma subjects, the neural network exhibited the largest AUROC. When differentiating normal from early glaucoma subjects, the overall diagnostic performance decreased, but the neural network still exhibited the largest AUROC. CONCLUSIONS: The optimal relationship between BMO-MRW and pRNFLT was revealed using the broken-stick model. Considerable BMO-MRW thinning preceded pRNFLT thinning. The neural network significantly improved diagnostic power by combining BMO-MRW and pRNFLT. TRANSLATIONAL RELEVANCE: A combined index featuring BMO-MRW and pRNFLT data can aid clinical decision-making, particularly when individual parameters yield confusing results. Our neural network effectively combines information from separate parameters.

Macular Vessel Density and Ganglion Cell/Inner Plexiform Layer Thickness and Their Combinational Index Using Artificial Intelligence.

PURPOSE: To evaluate the relationship between macular vessel density and ganglion cell to inner plexiform layer thickness (GCIPLT) and to compare their diagnostic performance. We attempted to develop a new combined parameter using an artificial neural network. METHODS: A total of 173 subjects: 100 for the test and 73 for neural net training. The test group consisted of 32 healthy, 33 early, and 35 advanced glaucoma subjects. Macular GCIPLT and vessel density were measured using Spectralis optical coherence tomography and Topcon swept-source optical coherence tomography, respectively. Various regression models were used to investigate the relationships between macular vessel density and GCIPLT. A multilayer neural network with one hidden layer was used to determine a single combined parameter. To compare diagnostic performance, we used the area under the receiver operating characteristic curve (AUROC). RESULTS: Correlation analyses in all subjects showed a significant correlation between macular vessel density and GCIPLT in all sectors (r=0.27 to 0.56; all Ps≤0.006). The fitness of linear, quadratic, and exponential regression models showed clinically negligible differences (Akaike's information criterion=714.6, 713.8, and 713.3, respectively) and were almost linear. In differentiating normal and early glaucoma, the diagnostic power of macular GCIPLT (AUROC=0.67 to 0.81) was much better than that of macular vessel density (AUROC=0.50 to 0.60). However, when vessel density information was incorporated into GCIPLT using the neural network, the combined parameter (AUROC=0.87) showed significantly enhanced diagnostic performance than all sectors of macular vessel density and GCIPLT (all Ps≤0.043). CONCLUSIONS: Macular vessel density was significantly decreased in glaucoma patients and showed an almost linear correlation with macular GCIPLT. The diagnostic performance of macular vessel density was much lower than that of macular GCIPLT. However, when incorporated into macular GCIPLT using an artificial neural network, the combined parameter showed better performance than macular GCIPLT alone.

Bruch's membrane opening-minimum rim width and visual field loss in glaucoma: a broken stick analysis.

AIM: To determine the Bruch's membrane opening-minimum rim width (BMO-MRW) tipping point where corresponding visual field (VF) damages become detectable. METHODS: A total of 85 normal subjects and 83 glaucoma patients (one eye per participant) were recruited for the study. All of the patients had VF examinations and spectral-domain optical coherence tomography to measure the BMO-MRW. Total deviation values for 52 VF points were allocated to the corresponding sector according to the Garway-Heath distribution map. To evaluate the relationship between VF loss and BMO-MRW measurements, a "broken-stick" statistical model was used. The tipping point where the VF values started to sharply decrease as a function of BMO-MRW measurements was estimated and the slopes above and below this tipping point were compared. RESULTS: A 25.9% global BMO-MRW loss from normative value was required for the VF loss to be detectable. Sectorally, substantial BMO-MRW thinning in inferotemporal sector (33.1%) and relatively less BMO-MRW thinning in the superotemporal sector (8.9%) were necessary for the detection of the VF loss. Beyond the tipping point, the slopes were close to zero throughout all of the sectors and the VF loss was unrelated to the BMO-MRW loss. The VF loss was related to the BMO-MRW loss below the tipping point. The difference between the two slopes was statistically significant (P≤0.002). CONCLUSION: Substantial BMO-MRW loss appears to be necessary for VF loss to be detectable in patients with open angle glaucoma with standard achromatic perimetry.

Relationship between corneal biomechanical properties and structural biomarkers in patients with normal-tension glaucoma: a retrospective study.

BACKGROUND: We evaluated the relationships between corneal biomechanical properties and structural parameters in patients with newly diagnosed, untreated normal-tension glaucoma (NTG). METHODS: All subjects were evaluated using an Ocular Response Analyzer (ORA) measuring corneal hysteresis (CH) and the corneal resistance factor (CRF). Central corneal thickness (CCT), Goldmann applanation tonometric (GAT) data, axial length, and the spherical equivalent (SE), were also measured. Confocal scanning laser ophthalmoscopy was performed with the aid of a Heidelberg retina tomograph (HRT III). We sought correlations between HRT parameters and different variables including CCT, CH, and the CRF. Multiple linear regression analysis was performed to identify significant associations between corneal biomechanical properties and optic nerve head parameters. RESULTS: We enrolled 95 eyes of 95 NTG patients and 93 eyes of 93 normal subjects. CH and the CRF were significantly lower in more advanced glaucomatous eyes (P = 0.001, P = 0.008, respectively). The rim area, rim volume, linear cup-to-disc ratio (LCDR), and mean retinal nerve fiber layer (RNFL) thickness were significantly worse in more advanced glaucomatous eyes (P < 0.001, P < 0.001, P < 0.001, and P = 0.001). CH was directly associated with rim area, rim volume, and mean RNFL thickness (P = 0.012, P = 0.028, and P = 0.043) and inversely associated with LCDR (P = 0.015), after adjusting for age, axial length, CCT, disc area, GAT data, and SE. However, in normal subjects, there were no significant associations between corneal biomechanical properties and HRT parameters. CONCLUSIONS: A lower CH is significantly associated with a smaller rim area and volume, a thinner RNFL, and a larger LCDR, independent of disc size, corneal thickness, intraocular pressure, and age.

Reproducibility of Bruch Membrane Opening-Minimum Rim Width Measurements With Spectral Domain Optical Coherence Tomography.

PURPOSE: To evaluate the reproducibility of Bruch membrane opening-minimum rim width (BMO-MRW) measurements obtained with Spectralis optical coherence tomography (OCT) in normal and glaucoma subjects. MATERIALS AND METHODS: In total, 123 eyes from 123 subjects (65 healthy, 58 glaucoma subjects) were included. BMO-MRW measurements were repeated 3 times during the same visit using Spectralis OCT. The BMO points and internal limiting membrane were identified with automated software and corrected manually when necessary. The intravisit repeatability, coefficient of variation (CV), and intraclass correlation coefficient were analyzed for each sector and global BMO-MRW. The Spearman rank correlation coefficient was used to estimate correlations between CV and multiple variables. Multiple linear regression analysis was used to identify significant associations. RESULTS: The intravisit repeatability ranged from 2.97 µm (global) to 10.25 µm (inferotemporal sector) in healthy subjects and from 3.31 µm (global) to 12.09 µm (inferonasal sector) in glaucoma subjects. The CVs ranged from 1.17% (global) to 3.56% (inferotemporal sector) in healthy subjects and from 2.57% (global) to 6.46% (superotemporal and inferotemporal sector) in glaucoma subjects. Intraclass correlation coefficients ranged from 0.974 (superotemporal sector) to 0.997 (nasal sector) in normal subjects and from 0.988 (temporal sector) to 0.997 (global and nasal sector) in glaucoma subjects. Multiple regression analysis showed that the CV in global BMO-MRW measurements was inversely associated with global BMO-MRW and visual field mean deviation (P=0.001 and 0.002, respectively). CONCLUSIONS: The Spectralis SD-OCT showed excellent reproducibility in BMO-MRW measurements in both normal and glaucoma subjects. The measurements variability was worse in more advanced glaucoma.

Correction: Measurement of macular structure-function relationships using spectral domain-optical coherence tomography (SD-OCT) and pattern electroretinograms (PERG).

[This corrects the article DOI: 10.1371/journal.pone.0178004.].

Measurement of macular structure-function relationships using spectral domain-optical coherence tomography (SD-OCT) and pattern electroretinograms (PERG).

BACKGROUND: Retinal ganglion cell (RGC) death is a common cause of loss of vision during glaucoma. Pattern electroretinogram (PERG) is an objective measure of the central retinal function that correlates with macular GCL thickness. The aim of this study is to determine possible relationships between the N95 amplitude of pattern electroretinogram (PERGamp) and macular ganglion cell/inner plexiform layer thickness (GCIPLT). METHODS AND FINDINGS: This was a retrospective and comparative study including 74 glaucoma patients (44 early stage and 30 advanced stage cases) and 66 normal control subjects. Macular GCIPLT was measured using Cirrus spectral domain-optical coherence tomography. Standard automated perimetry and pattern ERGs were used in all patient examinations. Three types of regression analysis (broken stick, linear regression, and quadratic regression) were used to evaluate possible relationships between PERGamp and GCIPLT. Correlations between visual field parameters and GCIPLT were evaluated according to glaucoma severity. The best fit model for the relationship between PERGamp and GCIPLT was the linear regression model (r2 = 0.22; P < 0.001). The best-fit model for the relationship between visual field parameters and GCIPLT was the broken stick model. During early glaucoma, macular GCIPLT was positively correlated with PERGamp, but not with visual field loss. In advanced glaucoma, macular GCIPLT was positively correlated with both PERGamp and visual field loss. CONCLUSIONS: PERGamp was significantly correlated with macular GCIPT in early glaucoma patients, while visual field performance showed no correlation with GCIPLT. PERGamp can therefore assist clinicians in making an early decision regarding the most suitable treatment plan, especially when GCIPLT is thinning with no change in visual field performance.