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Purpose of Review: Optical coherence tomography (OCT) is a widely used tool to diagnose and monitor glaucoma by objectively measuring the ganglion cell layer and the retinal nerve fiber layer (RNFL) thickness. The presence of RNFL thinning raises suspicion for glaucoma progression. Therefore, this review aims to discuss current approaches to using OCT for detecting glaucomatous change, limitations, and recent advancements., Recent Findings: Previously established approaches to determining glaucomatous progression on OCT include quantitative and qualitative methods. The most common quantitative methods include event-based and trend-based analysis. Decreasing RNFL thickness or loss of the ganglion cell layer are indicative of glaucomatous changes. However, interpretation of OCT scans is strongly impacted by artifacts, which can be because of epiretinal membrane or posterior vitreous detachment. Race and aging also may impact interpretation of RNFL progression. More recent research focuses on loss of the RNFL because of the effects of systemic conditions. Given the limitations in the current approaches, recent advancements indicate a promising role for artificial intelligence in determining true glaucomatous progression., Summary: This review highlights current approaches to identifying glaucoma progression on OCT, limitations to these approaches, and the potential role for artificial intelligence., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: To determine the prevalence of artifacts on segmented spectral-domain optical coherence tomography (SDOCT) images and assess their impact on the interpretation of glaucomatous progression in the retinal nerve fiber layer (RNFL) profile and macular thickness map., Design: Retrospective reliability analysis., Methods: Retrospective review of glaucoma and glaucoma suspect eyes imaged with SDOCT during a 1-month period. All cases had at least 4 sets of RNFL and macular images at 6-month intervals. SDOCT raw B-scans were examined to determine true progression and whether artifacts impacted the original interpretation of progression based on auto-segmented change maps. The co-prevalence of artifacts in the RNFL and macula was assessed, as well as the association of clinical factors with the likelihood of artifacts., Results: A total of 190 eyes with 760 sets of OCT RNFL and macular scans were included. Fifty percent (96/190) of eyes had artifacts, either in the circumpapillary RNFL (83/190; 43.68%) or the macula (57/190; 30.0%). Epiretinal membrane and vitreomacular traction were the most common artifacts. True progression was present on 39.5% (75/190) of scans overall. Among scans with artifacts, 23.9% (23/96) of artifacts masked true progression (ie, false-negative), 36.5% (35/96) led to an interpretation of false progression (ie, false-positive), and 39.6% (38/96) had no effect on the interpretation of progression. The presence of true progression on the RNFL scan was significantly associated with the presence of true progression on the macular scan (P < .001). Similarly, the presence of artifacts on the RNFL scan was significantly associated with artifacts on the macular scan (P < .001). In multivariable analysis, severe glaucoma, hypertension, and age were significantly associated with the presence of artifacts on RNFL (P < .05)., Conclusions: Artifacts are highly prevalent on both circumpapillary RNFL and macular scans on SDOCT images acquired in a glaucoma clinic. Artifacts can lead to false-positive and false-negative interpretation of progression when using only the auto-segmentation change maps. Thus, careful examination of the raw B-scan images of both the RNFL and macula is critical to identify artifacts and true glaucoma progression., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: To determine whether aging modifies the effect of intraocular pressure (IOP) on progressive glaucomatous retinal nerve fiber layer (RNFL) thinning over time., Methods: This was a retrospective cohort study involving patients with glaucoma or suspected of having glaucoma who were followed over time from the Duke Glaucoma Registry. Rates of RNFL loss from spectral-domain optical coherence tomography (SD-OCT) were used to assess disease progression. Generalized estimating equations with robust sandwich variance estimators were used to investigate the effects of the interaction of age at baseline and mean IOP on rates of RNFL loss over time. Models were adjusted for gender, race, diagnosis, central corneal thickness, follow-up time, and baseline disease severity., Results: The study included 85,475 IOP measurements and 60,026 SD-OCT tests of 14,739 eyes of 7814 patients. Eyes had a mean follow-up time of 3.5 ± 1.9 years. The average rate of change in RNFL thickness was -0.70 µm/year (95% confidence interval, -0.72 to -0.67). There was a significant interaction between age and mean IOP and the rate of RNFL loss (P = 0.001), with older eyes having significantly faster rates of RNFL loss than younger ones for the same level of IOP. The effect of IOP on rates of change was greater in the inferior and superior regions of the optic disc., Conclusions: Age is a significant modifier of the relationship between IOP and glaucomatous loss in RNFL thickness over time. Older patients may be more susceptible to glaucomatous progression than younger patients at the same level of IOP.

Purpose: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs., Design: Evaluation of a machine learning algorithm., Methods: An M2M DL algorithm trained with RNFL thickness parameters from spectral-domain optical coherence tomography was applied to a subset of 490 fundus photos of 490 eyes of 370 subjects graded by 2 glaucoma specialists for the probability of glaucomatous optical neuropathy (GON), and estimates of cup-to-disc (C/D) ratios. Spearman correlations with standard automated perimetry (SAP) global indices were compared between the human gradings vs the M2M DL-predicted RNFL thickness values. The area under the receiver operating characteristic curves (AUC) and partial AUC for the region of clinically meaningful specificity (85%-100%) were used to compare the ability of each output to discriminate eyes with repeatable glaucomatous SAP defects vs eyes with normal fields., Results: The M2M DL-predicted RNFL thickness had a significantly stronger absolute correlation with SAP mean deviation (rho=0.54) than the probability of GON given by human graders (rho=0.48; P < .001). The partial AUC for the M2M DL algorithm was significantly higher than that for the probability of GON by human graders (partial AUC = 0.529 vs 0.411, respectively; P = .016)., Conclusion: An M2M DL algorithm performed as well as, if not better than, human graders at detecting eyes with repeatable glaucomatous visual field loss. This DL algorithm could potentially replace human graders in population screening efforts for glaucoma., (Copyright © 2019 Elsevier Inc. All rights reserved.)

This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as targets, forcing the DL algorithm to learn its own representation of RNFL. The algorithm was tested in three different sets: (1) images without segmentation errors or artefacts, (2) low-quality images with segmentation errors, and (3) images with other artefacts. In test set 1, segmentation-free RNFL predictions were highly correlated with conventional RNFL thickness (r = 0.983, P < 0.001). In test set 2, segmentation-free predictions had higher correlation with the best available estimate (tests with good quality taken in the same date) compared to those from the conventional algorithm (r = 0.972 vs. r = 0.829, respectively; P < 0.001). Segmentation-free predictions were also better in test set 3 (r = 0.940 vs. r = 0.640, P < 0.001). In conclusion, a novel segmentation-free algorithm to extract RNFL thickness performed similarly to the conventional method in good quality images and better in images with errors or other artefacts.

Purpose: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)., Design: Cross-sectional study., Methods: A total of 9282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to the actual SDOCT measurements. The area under the receiver operating curve (AUC) was used to evaluate the ability of the network to discriminate glaucomatous visual field loss from normal eyes., Results: The DL predictions of global BMO-MRW from all optic disc photographs in the test set (mean ± standard deviation [SD]: 228.8 ± 63.1 μm) were highly correlated with the observed values from SDOCT (mean ± SD: 226.0 ± 73.8 μm) (Pearson's r = 0.88; R 2  = 77%; P < .001), with mean absolute error of the predictions of 27.8 μm. The AUCs for discriminating glaucomatous from healthy eyes with the DL predictions and actual SDOCT global BMO-MRW measurements were 0.945 (95% confidence interval [CI]: 0.874-0.980) and 0.933 (95% CI: 0.856-0.975), respectively (P = .587)., Conclusions: A DL network can be trained to quantify the amount of neuroretinal damage on optic disc photographs using SDOCT BMO-MRW as a reference. This algorithm showed high accuracy for glaucoma detection, and may potentially eliminate the need for human gradings of disc photographs., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Introduction: To evaluate the agreement between trend-based analysis and qualitative assessment of the retinal nerve fiber layer (RNFL) thickness for glaucomatous progression on spectral-domain optical coherence tomography (SDOCT). Methods: Retrospective review of 190 eyes from 103 patients with glaucoma or suspected glaucoma that underwent SDOCT imaging during four consecutive clinic visits. Trend-based progression was characterized by a significantly negative slope. Progression by qualitative analysis was determined by review of raw SDOCT B-scans. Results: The slope was significantly greater in those with progression than without progression for both trend-based and qualitative analysis (p < 0.001). However, the qualitative grading classified a significantly greater proportion of eyes as progressing compared to trend-based analysis in both the superotemporal (ST) (23.2% vs. 10.5%, p = 0.001) and inferotemporal (IT) RNFL (27.4% vs 8.4%, p < 0.001). The trend-based and qualitative classifications of progression showed poor agreement in both the ST (kappa = 0.0135) and IT RNFL (kappa = 0.1222). The agreement between trend-based and qualitative analysis was lower for eyes with artifacts (ST = 58.11%; IT = 68.7%) than those without artifacts (ST = 80.2%; IT = 74.8%). Moreover, among eyes with artifacts, there was no significant difference in slope between those qualitatively categorized as progressing versus not progressing (p > 0.05). Conclusions: Poor agreement was found between a trend-based and qualitative analysis of change in RNFL on SDOCT. Careful qualitative review of SDOCT imaging may identify specific areas of glaucoma progression not captured by trend-based methods, especially in the presence of artifacts. Such an approach may also prove useful for detecting glaucoma progression in a clinical setting when there are few data points available. [ABSTRACT FROM AUTHOR]

To investigate the impact of intraocular pressure (IOP) control on rates of change of spectral-domain OCT (SD-OCT) retinal nerve fiber layer (RNFL) thickness in a large clinical population. Retrospective cohort study. A total of 85 835 IOP measurements and 60 223 SD-OCT tests from 14 790 eyes of 7844 patients. Data were extracted from the Duke Glaucoma Registry, a large database of electronic medical records of patients with glaucoma and suspected disease followed over time at the Duke Eye Center and satellite clinics. All records from patients with a minimum of 6 months of follow-up and at least 2 good-quality SD-OCT scans and 2 clinical visits with Goldmann applanation tonometry were included. Eyes were categorized according to the frequency of visits with IOP below cutoffs of 21 mmHg, 18 mmHg, and 15 mmHg over time. Rates of change for global RNFL thickness were obtained using linear mixed models and classified as slow if change was slower than −1.0 μm/year; moderate if between −1.0 and −2.0 μm/year; and fast if faster than −2.0 μm/year. Multivariable models were adjusted for age, gender, race, diagnosis, central corneal thickness, follow-up time, and baseline disease severity. Rates of change in SD-OCT RNFL thickness according to levels of IOP control. Eyes had a mean follow-up of 3.5±1.9 years. Average rate of change in RNFL thickness was −0.68±0.59 μm/year. Each 1 mmHg higher mean IOP was associated with 0.05 μm/year faster RNFL loss (P < 0.001) after adjustment for potentially confounding variables. For eyes that had fast progression, 41% of them had IOP < 21 mmHg in all visits during follow-up, whereas 20% of them had all visits with IOP < 18 mmHg, but only 9% of them had all visits with IOP < 15 mmHg. Intraocular pressure was significantly associated with rates of progressive RNFL loss in a large clinical population. Eyes with stricter IOP control over follow-up visits had a smaller chance of exhibiting fast deterioration. Our findings may assist clinicians in establishing target pressures in clinical practice. [ABSTRACT FROM AUTHOR]

Several prior studies have described the co-prevalence of peripapillary RNFL retinoschisis in eyes with glaucoma [[2]-[5]], which may impact approximately 6% of glaucoma patients according to two recent cohort studies [[3]]. Thus, when reviewing serial SD-OCT in patients with glaucoma, it is critical to neither overestimate the RNFL thickness in eyes with retinoschisis, nor erroneously attribute the resolution of such retinoschisis to glaucomatous progression. Van der Schoot et al. detected focal peripapillary RNFL retinoschisis in 7 of 117 glaucomatous eyes and 0 of 91 healthy control eyes [[4]]. [Extracted from the article]