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Open Access

Peer-reviewed

Research Article

Optical coherence tomography for glaucoma diagnosis: An evidence based meta-analysis

  • Vinay Kansal,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliation University of Saskatchewan, Department of Ophthalmology, Saskatoon, Canada

    ⨯
  • James J. Armstrong,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Western University Canada, Faculty of Medicine, London, Canada

    ⨯
  • Robert Pintwala,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Western University Canada, Faculty of Medicine, London, Canada

    ⨯
  • Cindy Hutnik

    Roles Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing

    cindy.hutnik@sjhc.london.on.ca

    Affiliations Western University Canada, Department of Ophthalmology, London, Canada, Ivey Eye Institute, St. Joseph’s Hospital, London, Canada

    ⨯

Abstract

Purpose

Early detection, monitoring and understanding of changes in the retina are central to the diagnosis of glaucomatous optic neuropathy, and vital to reduce visual loss from this progressive condition. The main objective of this investigation was to compare glaucoma diagnostic accuracy of commercially available optical coherence tomography (OCT) devices (Zeiss Stratus, Zeiss Cirrus, Heidelberg Spectralis and Optovue RTVue, and Topcon 3D-OCT).

Patients

16,104 glaucomatous and 11,543 normal eyes reported in 150 studies.

Methods

Between Jan. 2017 and Feb 2017, MEDLINE®, EMBASE®, CINAHL®, Cochrane Library®, Web of Science®, and BIOSIS® were searched for studies assessing glaucoma diagnostic accuracy of the aforementioned OCT devices. Meta-analysis was performed pooling area under the receiver operating characteristic curve (AUROC) estimates for all devices, stratified by OCT type (RNFL, macula), and area imaged.

Results

150 studies with 16,104 glaucomatous and 11,543 normal control eyes were included. Key findings: AUROC of glaucoma diagnosis for RNFL average for all glaucoma patients was 0.897 (0.887–0.906, n = 16,782 patient eyes), for macula ganglion cell complex (GCC) was 0.885 (0.869–0.901, n = 4841 eyes), for macula ganglion cell inner plexiform layer (GCIPL) was 0.858 (0.835–0.880, n = 4211 eyes), and for total macular thickness was 0.795 (0.754–0.834, n = 1063 eyes).

Conclusion

The classification capability was similar across all 5 OCT devices. More diagnostically favorable AUROCs were demonstrated in patients with increased glaucoma severity. Diagnostic accuracy of RNFL and segmented macular regions (GCIPL, GCC) scans were similar and higher than total macular thickness. This study provides a synthesis of contemporary evidence with features of robust inclusion criteria and large sample size. These findings may provide guidance to clinicians when navigating this rapidly evolving diagnostic area characterized by numerous options.

Citation: Kansal V, Armstrong JJ, Pintwala R, Hutnik C (2018) Optical coherence tomography for glaucoma diagnosis: An evidence based meta-analysis. PLoS ONE 13(1): e0190621. https://doi.org/10.1371/journal.pone.0190621

Editor: Patrice E. Fort, University of Michigan, UNITED STATES

Received: June 30, 2017; Accepted: December 18, 2017; Published: January 4, 2018

Copyright: © 2018 Kansal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Glaucoma is the leading cause of irreversible blindness worldwide[1]. As the population continues to age, and average life expectancies increase, the prevalence of this debilitating disease will grow. Glaucoma is one of the leading causes of blindness in working-age populations of industrialized nations, and is the most common cause of permanent vision loss in persons older than 40 years of age, after age-related macular degeneration[2–4].

Glaucoma is a multifactorial, chronic optic nerve neuropathy that is characterized by progressive loss of retinal ganglion cells (RGC), which leads to structural damage to the optic nerve head (ONH), retinal nerve fiber layer (RNFL), and consequent visual field defects[5]. Early diagnosis and treatment of glaucoma has been shown to reduce the rate of disease progression, and improve patients’ quality of life[6]. The currently accepted gold standards for glaucoma diagnosis are optic disc assessment for structural changes, and achromatic white-on-white perimetry to monitor changes in function[7]. However, imaging technologies such as optic coherence technology (OCT) are playing an increasing role in glaucoma diagnosis, monitoring of disease progress, and quantification of structural damage[8,9].

OCT is a non-invasive, non-contact imaging modality that provides high-resolution cross-sectional imaging of ocular tissues (retina, optic nerve, and anterior segment). Image acquisition is analogous to ultrasound, where light waves is used in lieu of sound waves. Low coherence infrared light is directed toward the tissue being imaged, from which it scatters at large angles. An interferometer (beam splitter) is used to record the path of scattered photons and create three-dimensional images[10–13]. OCT is highly reproducible, and is thus widely used as an adjunct in routine glaucoma patient management[14–16].

Peripapillary RNFL analysis is the most commonly used scanning protocol for glaucoma diagnosis[14–16], as it samples RGCs from the entire retina; however, it does suffer certain drawbacks related to inter-patient variability in ONH morphology[17,18]. To overcome some of these disadvantages, the macular thickness has been proposed as a means of glaucoma detection[19]– 50% of RGCs are found in the macula, and RGC bodies are thicker than their axons, thus are potentially easier to detect. The older time-domain (TD) OCT devices, such as Zeiss Stratus, were able to only measure total macular thickness, which had been shown to have poorer glaucoma diagnostic accuracy than RNFL thickness[20–22]. Spectral-domain (SD) OCT (Zeiss Cirrus, Heidelberg Spectralis, Optovue RTVue, Topcon 3D-OCT) allows for measurement of specific retinal layers implicated in the pathogenesis of glaucoma, namely: macular nerve fiber layer (mNFL), ganglion cell layer with inner plexiform layer (GCIPL), and ganglion cell complex (GCC) (composed of mNFL and GCIPL). Segmented analysis is purported to have better diagnostic ability for glaucoma than total retinal thickness[23,24], and may be comparable to RNFL thickness[23,25,26].

Currently, several OCT devices are available on the market, each with unique technologies purported to provide better clinical information to the user. The technical features of these various systems have been described elsewhere[27,28]. Reichel et al. also provide images obtained from each of the OCT systems[27]. It is unclear however; which OCT device should be selected by practitioners when making referral or treatment decisions. The aim of this meta-analysis was to provide pooled estimates for the accuracy and detection capability of the most commonly used OCT imaging devices (Zeiss Cirrus OCT, Zeiss, Stratus OCT, Heidelberg Spectralis, Optovue RTVue, Topcon 3D-OCT) for glaucoma diagnosis and classification between patients and healthy individuals.

Methods

Overview of review methods

The main objective of this investigation was to compare the glaucoma diagnostic accuracy for each of the OCT devices commercially available, namely Zeiss Stratus, Zeiss Cirrus, Heidelberg Spectralis, Optovue RTVue and Topcon 3D-OCT. We compared diagnostic accuracies of RNFL and macular parameters obtained by these imaging devices. This review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement methodology[29]. A PRISMA flow diagram is used to illustrate the flow of records throughout this review (Fig 1).

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Fig 1. Study flow in this meta-analysis (PRISMA guidelines).

https://doi.org/10.1371/journal.pone.0190621.g001

Data sources and search strategy

The search strategy for this investigation was comprehensive, aiming to retrieve the largest possible number of relevant studies. An electronic search strategy was developed through consultation with an experienced ophthalmologist specializing in glaucoma management. The search end date was February 2017. There was no specified search start date. Any study providing information on area under receiver operating characteristic curve, sensitivity, specificity, negative predictive value, positive predictive value, likelihood ratio, or diagnostic odds ratio was included. Published and unpublished studies were considered.

The following bibliographic databases were searched: MEDLINE® (Ovid MEDLINE(R) Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily, Ovid MEDLINE and Versions(R)), EMBASE® (Embase Classic+Embase), CINAHL®, Cochrane Library® (Wiley Library), Web of Science®, and BIOSIS®. Specific keywords used in the search included terms for glaucoma, optical coherence tomography, imaging device manufacturer (ie. Zeiss, Heidelberg, RTVue, Topcon), and diagnostic testing including terms for diagnostic evaluative tests (ie. Area under receiver operating characteristic curve, etc.). Search strategies for each of the devices are available in S1 Table (Appendix 1).

Inclusion and exclusion criteria

All studies that assessed the diagnostic accuracy of OCT for detection of glaucoma were considered for inclusion in our review. As the goal of this investigation was to maximize generalizability and applicability to clinical practice, a broad gold standard was accepted for inclusion, ie. White on white automated perimetry, optic disc appearance (clinically or by photograph), or combination thereof. Accepting a wider gold standard more accurately reflects the reality of clinical practice, and allowed for inclusion of a larger number of articles, improving robustness of the quantitative meta-analysis. Only human, clinical studies published in English-language were accepted. Patient were 18 years of age or greater. No exclusions were made for patient ethnicity, or country where study was conducted. Included studies assessed at least one of five devices, namely Stratus OCT (Carl Zeiss Meditec, Jena, Germany), Cirrus OCT (Carl Zeiss Meditec), Spectralis OCT (Heidelberg Engineering Inc., Heidelberg, Deutschland), RTVue (Optovue Inc., Freemont, United States), and 3D-OCT (Topcon, Tokyo, Japan). These devices were included as they represent the newest or most widely utilized OCT devices available for glaucoma diagnosis at the time of this review. Studies of both RNFL and macular areas for glaucoma diagnosis were included.

During full-text screening, articles were included if they reported area under receiver operating characteristic curve (AUROC) statistics. Manuscripts that did not report standard error or confidence intervals for AUROC were excluded. Other exclusions were: duplicate manuscripts, non-diagnostic studies, studies of pediatric patients, studies without control participants, and investigations of OCT devices other than those previously specified.

Study selection

All studies included for consideration underwent two levels of screening by two independent reviewers. All records were uploaded to an online interface (Covidence, Veritas Health Innovation, Melbourne, Australia) to coordinate and support the screening process. First, a broad screen of titles, keywords and abstracts (Level 1) was performed. At this stage, studies were tagged as either “Relevant”, “Irrelevant” or “Maybe Relevant”. For all relevant studies, full text screening was performed (Level 2) using the stricter a priori inclusion criteria detailed previously.

After each level of screening, disagreements between article screeners were resolved through consultation with the primary author. Reasons for exclusion were documented and are reported in the review. The PRISMA flow chart of studies during screening is illustrated in Fig 1.

Data extraction and quality assessment

An electronic data extraction form specific to this meta-analysis was developed a priori. Data collected included study identification information (title, authors, journal and year of publication, study methodology (design, inclusion/exclusion criteria, gold standard type), patient variables (number of patients/controls, glaucoma diagnosis, age, gender), OCT device used, area imaged (RNFL, macula subtype), and AUROC (with SE/CI).

The quality assessment of diagnostic accuracy studies, version 2 (QUADAS-2)[30] was used to assess the risk of bias and applicability concerns of all manuscripts included in this review. This assessment tool comprises four key domains: 1) patient selection, 2) index test, 3) reference standard, and 4) flow of patients through the study and timing between index test and reference standard. Each domain was assessed in terms of risk of bias. The first three domains were assessed for their applicability to the research question being assessed by the review. Results of QUADAS-2 are summarized in Fig 2.

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Fig 2. Methodological quality of included studies using the QUADAS 2 tool.

https://doi.org/10.1371/journal.pone.0190621.g002

Data synthesis and statistical analysis

All statistical analyses were performed using MedCalc (Version 17.2, MedCalc Software, Ostend, Belgium). Meta-analysis for the AUROC was selected instead of other measures such as sensitivity and specificity. The AUROC is a commonly used metric for diagnostic accuracy of medical tests. It was found to be more consistently reported in the included studies. Whereas some studies reported a combination of parameters, others reported sensitivity values for particular specificity cut-offs, which, in turn, were not consistent across studies. AUROC reflects both the sensitivity and specificity of a diagnostic test, can be compared across studies, and can be combined between similar studies when measures of uncertainty (standard error (SE) or confidence interval (CI)) are provided[31].

Meta-analysis was completed using MedCalc (MedCalc, Version 17.2, MedCalc Software, Ostend, Belgium). The main outcome of this study was pooled AUROC for each of the following groups: all glaucoma patients, perimetric glaucoma, pre-perimetric, mild glaucoma, moderate to severe glaucoma, and myopic glaucoma. As there currently does not exist any international consensus on the definition of glaucoma severity, there was heterogeneity in the way that each study defined their patient groups. For consistency, we defined each group as follows: 1) Perimetric glaucoma–glaucoma based on abnormal visual field measurements; 2) Pre-perimetric glaucoma–glaucoma diagnosed based on optic disc appearance, with normal visual field measurements; 3) Mild glaucoma–perimetric glaucoma, defined as mean deviation of > -6.00 dB as per the Hodapp-Parrish-Anderson criteria[32]. Patients with normal visual fields were not included in this group; 4) Moderate to severe glaucoma–perimetric glaucoma, defined as mean deviation < -6.00 dB[32]; 5) Myopic glaucoma–any definition of myopia as defined by study authors, this could include dioptric definition (ex. Spherical equivalent < -6.0) or axial length definition (AL >25mm).

Individual measures of AUROC from each study were pooled into a weighted summary AUROC for each group using the methods described in Zhou et al.[31] Heterogeneity among included studies was tested by computing the I2, Z-value and χ2 statistics. An I2 value of less than 50% implies low heterogeneity and supports the use of a fixed-effect meta-analysis model. A value of greater than or equal to 50% implies high heterogeneity and supports the use of a random-effects model. Additionally, a high Z-value, a low p-value (<0.01) and a large χ2 value implies significant heterogeneity and supports the use of a random-effects model using DerSimonian and Laird methods. Forest plots were generated to visualize results. Publication bias was assessed through evaluation of funnels plots of included studies for each pooled AUROC.

Results

Search results and study characteristics

Study flow is summarized in Fig 1. After removal of duplicates, 1301 records underwent title and abstract (Level 1) screening. 825 were excluded as irrelevant. The remaining 477 records underwent full-text screening (Level 2). Of these, 327 articles were excluded as they did not meet the study inclusion criteria, or manuscript was unable to be obtained. At the end of screening, 150 articles were included for meta-analysis [21–24,33–178].

Characteristics of the 150 included studies are presented in S2 Table (Appendix 2). 67 (44.7%) of studies were case-control studies, 73 (48.7%) were cross-sectional studies, and 10 (6.7%) were cohort studies. 34 (22.7%) used visual field as a reference standard, 6 (4.0%) used disc appearance, 110 (73.3%) used a combination of structural and functional criteria. 55 studies examined the Zeiss Cirrus OCT, 49 studies assessed Zeiss Stratus OCT, 23 studies evaluated Heidelberg Spectralis, 38 studies examined Optovue RTVue, and 14 studies evaluated the Topcon 3D-OCT. There were 50.0% male, and 50.0% female glaucoma patients (reported in 150 studies). Controls were 46.5% male, 53.5% female (reported in 109 studies). The mean age of glaucoma patients was 58.8 ± 11.2, of controls was 54.1 ± 11.1 (Table 1).

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Table 1. Summarized study and patient characteristics.

https://doi.org/10.1371/journal.pone.0190621.t001

Study quality

A summary of the methodological quality assessment for included studies is provided in Fig 2. Overall methodological quality of all included studies was strong in terms of risk of bias and applicability to the research question. Of note, there was an unclear risk of bias in patient selection for 39.3% of studies. This was largely due to inadequate reporting of patient selection methods in these manuscripts; thus, risk of bias was unable to be ascertained.

Diagnostic accuracy of OCT for all glaucoma patients, RNFL and macular parameters

The diagnostic accuracy of OCT for all glaucoma patients stratified by imaged area and device is reported in Table 2, and displayed graphically in Fig 3. Pooled AUROC ranged from 0.632 to 0.915 depending on imaging device and area imaged. Overall, there were no statistically significance differences between devices for any particular area imaged. Within RNFL parameters, we found that AUROC for glaucoma diagnosis was higher for average (0.897, CI95% 0.887 to 0.906), superior (0.855, CI95% 0.844 to 0.866) and inferior (0.895, CI95% 0.886 to 0.905) areas than nasal (0.707, CI95% 0.692 to 0.721) and temporal (0.742, CI95% 0.727 to 0.757) parameters. For the Macular GCIPL scans, average (0.858, CI95% 0.835 to 0.880), inferior (0.860, CI95% 0.840 to 0.880), temporal (superotemporal (0.825, CI95% 0.796 to 0.854), inferotemporal (0.877, CI95% 0.853 to 0.902)) and minimum parameters had higher AUROC for glaucoma diagnosis than nasal (superonasal (0.757, CI95% 0.722 to 0.792, inferonasal (0.783, CI95% 0.754 to 0.812)) areas. By comparison, there were no major differences between areas for the macular GCC scans.

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Fig 3. Forest plot of diagnostic accuracies of RNFL and macular OCT parameters, all glaucoma patients.

https://doi.org/10.1371/journal.pone.0190621.g003

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Table 2. Pooled AUROCs of RNFL and macular OCT parameters for all glaucoma patients.

https://doi.org/10.1371/journal.pone.0190621.t002

Comparing the diagnostic efficacy between RNFL and macular thickness, we note that average RNFL (0.897, CI95% 0.887 to 0.906), average macula GCC (0.885, CI95% 0.869 to 0.901), and average macula GCIPL (0.858, CI95% 0.835 to 0.880) thicknesses have similar AUROC for glaucoma diagnosis. By comparison, AUROC of average macular total thickness (0.794, CI95% 0.754 to 0.834) is lower.

Diagnostic accuracy of OCT for patient subgroups, RNFL and macular parameters

Perimetric glaucoma.

Diagnostic accuracy of OCT for patients with perimetric glaucoma is reported in Table 3, and represented in a forest plot in Fig 4. Findings largely mirror what was found for the overall glaucoma population, with AUROCs being higher. All devices performed relatively similarly for glaucoma diagnosis, with the Zeiss Cirrus OCT demonstrating highest AUROC for most RNFL and Macula GCIPL parameters. For the RNFL, average (0.905, CI95% 0.895 to 0.916), superior (0.870, CI95% 0.858 to 0.883), and inferior (0.907, CI95% 0.897 to 0.918) thicknesses had higher AUROC than nasal (0.730, CI95% 0.712 to 0.748) and temporal (0.760, CI95% 0.742 to 0.778) regions. Within macula GCIPL, the Macular GCIPL scans, average (0.864, CI95% 0.837 to 0.890), inferior (0.861, CI95% 0.836 to 0.886), temporal (superotemporal (0.835, CI95% 0.792 to 0.877), inferotemporal (0.879, CI95% 0.848 to 0.910)) and minimum (0.904, CI95% 0.875 to 0.933) parameters had higher AUROC for glaucoma diagnosis than nasal (superonasal (0.778, CI95% 0.727 to 0.829), inferonasal (0.789, CI95% 0.752 to 0.827)) areas. There were no notable differences in AUROC between different macular GCC areas.

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Fig 4. Forest plot of diagnostic accuracies of RNFL and macular OCT parameters, perimetric glaucoma.

https://doi.org/10.1371/journal.pone.0190621.g004

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Table 3. Pooled AUROCs of RNFL and macular OCT parameters for perimetric glaucoma patients.

https://doi.org/10.1371/journal.pone.0190621.t003

Average RNFL (0.905, CI95% 0.895 to 0.916), average macular GCIPL (0.864, CI95% 0.837 to 0.890), average macular GCC (0.895, CI95% 0.874 to 0.916) performed similarly well for glaucoma diagnosis. Conversely, average macular mNFL (0.799, CI95% 0.742 to 0.857) and average total macular thickness (0.792, CI95% 0.744 to 0.840) had lower AUROC. Across OCT devices, no major differences were noted for any of the parameters.

Pre-perimetric glaucoma.

Pooled AUROCs for pre-perimetric glaucoma patients are reported in Table 4, and illustrated in a forest plot (Fig 5). There were no major differences across devices for any of the RNFL or macular parameters. Across RNFL parameters, average (0.831, CI95% 0.808 to 0.854), inferior (0.828, CI95% 0.801 to 0.855) and superior (0.774, CI95% 0.740 to 0.809) had larger AUROC than nasal (0.645, CI95% 0.610 to 0.680) or temporal (0.667, CI95% 0.627 to 0.707). All parameters within both macula GCIPL and macula GCC scans performed similarly well.

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Fig 5. Forest plot of diagnostic accuracies of RNFL and macular OCT parameters, pre-perimetric glaucoma.

https://doi.org/10.1371/journal.pone.0190621.g005

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Table 4. Pooled AUROCs of RNFL and macular OCT parameters for pre-perimetric glaucoma patients.

https://doi.org/10.1371/journal.pone.0190621.t004

Overall, average RNFL (0.831, CI95% 0.808 to 0.854) had higher AUROC for glaucoma diagnosis than both average macula GCIPL (0.762, CI95% 0.708 to 0.816) and average macula GCC (0.797, CI95% 0.768 to 0.825).

Mild glaucoma.

The diagnostic capability of OCT for patients with mild glaucoma is summarized in Table 5, and illustrated in Fig 6. RTVue OCT demonstrated a smaller AUROC than the other reviewed OCT devices for RNFL average (0.847, CI95% 0.781 to 0.913), inferior (0.826, CI95% 0.763 to 0.890), and superior parameters (0.784, CI95% 0.725 to 0.843). Across RNFL parameters, again average (0.912, CI95% 0.892 to 0.932), superior (0.860, CI95% 0.834 to 0.865) and inferior (0.901, CI95% 0.881 to 0.921) areas have higher diagnostic value than nasal (0.700, CI95% 0.667 to 0.732) and temporal (0.732, CI95% 0.698 to 0.766) regions. For macular GCC scans, all areas performed similarly well. Overall, RNFL parameters had higher AUROC than macular GCC (average RNFL (0.912, CI95% 0.892 to 0.932), average macular GCC (0.861, CI95% 0.819 to 0.903)).

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Fig 6. Forest plot of diagnostic accuracies of RNFL and macular OCT parameters, mild glaucoma.

https://doi.org/10.1371/journal.pone.0190621.g006

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Table 5. Pooled AUROCs of RNFL and macular OCT parameters for mild glaucoma patients.

https://doi.org/10.1371/journal.pone.0190621.t005

Moderate to severe glaucoma.

AUROCs of OCT for patients with moderate to severe glaucoma are summarized in Table 6, and illustrated in Fig 7. Overall, all OCT devices performed similarly well for glaucoma diagnosis. All RNFL parameters reported—average (0.959, CI95% 0.946 to 0.972), superior (0.923, CI95% 0.905 to 0.941) and inferior (0.954, CI95% 0.935 to 0.972)–had similar AUROCs. Superior macular GCC (0.856, CI95% 0.837 to 0.876), performed worse than the remainder of the macular GCC parameters. RNFL and macular GCC have comparable AUROCs (average RNFL (0.959, CI95% 0.946 to 0.972), macula GCC (0.938, CI95% 0.911 to 0.965)).

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Fig 7. Forest plot of diagnostic accuracies of RNFL and macular OCT parameters, moderate to severe glaucoma.

https://doi.org/10.1371/journal.pone.0190621.g007

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Table 6. Pooled AUROCs of RNFL and macular OCT parameters for moderate to severe glaucoma patients.

https://doi.org/10.1371/journal.pone.0190621.t006

Myopic patients.

AUROCs of OCT for glaucoma diagnosis in myopic patients are summarized in Table 7, and illustrated in Fig 8. All OCT devices performed relatively similarly for glaucoma diagnosis. Within RNFL, the average (0.917, CI95% 0.884 to 0.950), inferior (0.937, CI95% 0.920 to 0.955), superior (0.880, CI95% 0.855 to 0.906), and temporal (0.854, CI95% 0.822 to 0.886) parameters had improved AUROC compared to the nasal area (0.617, CI95% 0.556 to 0.679). For both macular GCIPL and macular GCC scans, diagnostic performance of all individual parameters was similar. In addition, there were no notable differences in AUROC for the average parameters of RNFL (0.917, CI95% 0.884 to 0.950), macular GCIPL (0.905, CI95% 0.859 to 0.952), and macular GCC (0.953, CI95% 0.936 to 0.971) scans.

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Fig 8. Forest plot of diagnostic accuracies of RNFL and macular OCT parameters, myopic patients.

https://doi.org/10.1371/journal.pone.0190621.g008

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Table 7. Pooled AUROCs of RNFL and macular OCT parameters for myopic patients.

https://doi.org/10.1371/journal.pone.0190621.t007

Evaluation of publication bias

Funnel plots were constructed to evaluate publication bias in the meta-analysis. Several funnel plots were created, one for each imaging parameter (average, superior, inferior etc.), of each area (RNFL, macula), for each OCT device, within each patient subgroup. No pattern was evident, ie. no one patient group, OCT device, or scan type/parameter was noted to be more likely to have publication bias.

Discussion

This meta-analysis demonstrates that OCT is a valuable adjunctive tool to aid in glaucoma diagnosis. Pooled estimates of diagnostic accuracy (AUROC) for the most commonly used OCT instruments (Zeiss Cirrus OCT, Zeiss, Stratus OCT, Heidelberg Spectralis, Optovue RTVue, Topcon 3D-OCT) were determined based upon their ability to differentiate between normal participants and glaucoma patients. A summary of the technical features of each device are outlined in Table 8.

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Table 8. Technical features of each of the OCT devices studied [27,180].

https://doi.org/10.1371/journal.pone.0190621.t008

The 150 studies included reported the diagnostic capability of several RNFL and macular parameters. Macular scans were further subdivided by retinal segmentation (GCC, GCIPL, mNFL or total retinal thickness). The AUROCs for average, superior and inferior RNFL parameters were larger than for nasal and temporal areas, a finding that was consistent for the overall patient group, as well as glaucoma subgroups. This finding is explained by the work of Traynis et al., 2014 who proposed a schematic of glaucomatous damage to the macula. Retinal ganglion cells (RCGs) in the regions of the macula most vulnerable to glaucomatous damage (inferior macula and region outside of the central 8 degrees of macula), project to the inferior and superior quadrants of the optic disc. Whereas RCGs in the less vulnerable regions (superior macula), project to the temporal region of the disc [179].

By comparison, in the macular GCIPL scans, we found that the inferonasal and superonasal parameters had poorer diagnostic efficacy than the average, superior, inferior, and temporal (infero- and superotemporal parameters). These differences between parameters were not found in the macular GCC scans.

Comparing between different scan types, RNFL thickness, macular GCIPL and macular GCC had similar diagnostic capability to differentiate between normal and glaucomatous eyes. Total macular thickness had lower AUROC for glaucoma diagnosis than these more specific scan types. Through stratification of patients by disease severity for sub-analysis, we also note that the diagnostic capability of OCT improves with increased disease severity.

One major question we wished to address through this review was whether there were instrument-dependent differences in diagnostic ability of OCT. It appears that for the majority of subgroups, there are no notable differences between devices.

Comparison with other reviews

Previous reviews on the diagnostic capability of OCT for glaucoma have been published[14,181–185]. The present review has some unique advantages over previous reports. First, as mentioned previously, a wide gold standard was accepted for inclusion, ie. White-on-white standard automated perimetry, optic disc appearance, elevated IOP, or any combination thereof. This wide gold standard more accurately reflects true clinical practice, where patients undergoing OCT to aid in glaucoma diagnosis may have undergone many of these other diagnostic modalities previously. The majority of previous reviews have limited inclusion criteria to those patients who have exclusively undergone standard automated perimetry. Our approach enabled the inclusion of 150 OCT studies, markedly larger than previous meta-analyses; a Cochrane review by Michelessi et al.[181] identified 63 OCT studies, Fallon et al.[185] identified 47 studies, Ahmed et al.[182] identified 84 studies, and Oddone et al.[183] identified 34 studies. The larger number of studies included enabled a more robust meta-analysis and the analyses of several patient subgroups.

Importantly, this meta-analysis provides pooled estimates of AUROCs, rather than sensitivity and specificity, as used in previous reviews. Only one other OCT review, by Chen et al.[184] identified reported pooled AUROCs; however, that review was limited to only 21 studies of Zeiss Stratus OCT. Reporting of AUROC is advantageous when describing the utility of a diagnostic test as it represents the diagnostic capability of the test regardless of specific cutoff used. We found that individual studies were inconstant in their reporting of sensitivity and specificity, with certain studies reporting sensitivities and particular specificity cutoffs, and others reporting the “optimal” sensitivity/specificity cutoff. Meta-analysis of such inconsistent data is difficult.

Limitations

One limitation of this study was the relatively large number of case-control studies that were captured in the inclusion criteria. The case-control design has been suggested to overestimate accuracy[186]. As the main purpose was to compare the diagnostic performance of the most common currently used OCT devices and none were found to be superior, this limitation unlikely introduced any significant bias. Another limitation may have resulted from choosing to the compare a number of macular parameters. Unlike RNFL scans, studies were quite heterogeneous in terms of which macular parameters were reported, ie. some reported GCIPL, GCC, mNFL, and total thickness. As such, these scan types had to be separated for meta-analysis, reducing sample sizes, and consequently increasing instability of AUROC estimates. Importantly, all studies included in the meta-analysis evaluated the ability to differentiate healthy controls from confirmed glaucoma patients, which does not reflect real clinical practice where many patients are undifferentiated.

Conclusion

The currently available OCT devices (Zeiss Cirrus, Zeiss Stratus, Heidelberg Spectralis, Optovue RTVue, Topcon 3D-OCT) demonstrated good diagnostic accuracy in their ability to differentiate glaucoma patients from normal controls. This ability increased with the severity of the glaucoma. There was no major device-related differences in diagnostic capacity. Within RNFL scans, the nasal and temporal parameters are more poorly diagnostic than the average, superior and inferior parameters. Across all macular GCIPL scans, the nasal (supero- and infero-nasal) parameters had lower AUROCs than the average, superior, inferior and temporal regions. The diagnostic capacity of RNFL is similar to segmented macular regions (GCIPL, GCC), and better than total macular thickness. As OCT technology continues to evolve at a faster pace than functional assessments of optic nerve health, future studies will be needed to fully understand its role in glaucoma management.

Supporting information

S1 Table. Appendix 1 –Search strategies.

https://doi.org/10.1371/journal.pone.0190621.s001

(PDF)

S2 Table. Appendix 2 –Individual study characteristics.

https://doi.org/10.1371/journal.pone.0190621.s002

(PDF)

S3 Table. PRISMA Checklist.

https://doi.org/10.1371/journal.pone.0190621.s003

(DOC)

References

  1. 1. Pascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol. 2012;96: 614–618. pmid:22133988
  2. 2. Quigley HA. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90: 262–267. pmid:16488940
  3. 3. Quigley HA. Number of people with glaucoma worldwide. Br J Ophthalmol. 1996;80: 389–393. pmid:8695555
  4. 4. Tham Y-C, Li X, Wong TY, Quigley HA, Aung T, Cheng C-Y. Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis. Ophthalmology. 2014;121: 2081–2090. pmid:24974815
  5. 5. Quigley HA. Glaucoma. Lancet Lond Engl. 2011;377: 1367–1377.
  6. 6. Quigley HA, Katz J, Derick RJ, Gilbert D, Sommer A. An Evaluation of Optic Disc and Nerve Fiber Layer Examinations in Monitoring Progression of Early Glaucoma Damage. Ophthalmology. 1992;99: 19–28. pmid:1741133
  7. 7. Medeiros FA, Zangwill LM, Bowd C, Sample PA, Weinreb RN. Use of Progressive Glaucomatous Optic Disk Change as the Reference Standard for Evaluation of Diagnostic Tests in Glaucoma. Am J Ophthalmol. 2005;139: 1010–1018. pmid:15953430
  8. 8. Greenfield DS, Weinreb RN. Role of Optic Nerve Imaging in Glaucoma Clinical Practice and Clinical Trials. Am J Ophthalmol. 2008;145: 598–603.e1. pmid:18295183
  9. 9. Sharma P, Sample PA, Zangwill LM, Schuman JS. Diagnostic Tools for Glaucoma Detection and Management. Surv Ophthalmol. 2008;53: S17–S32. pmid:19038620
  10. 10. Bowd C, Weinreb RN, Williams JM, Zangwill LM. The Retinal Nerve Fiber Layer Thickness in Ocular Hypertensive, Normal, and Glaucomatous Eyes With Optical Coherence Tomography. Arch Ophthalmol. 2000;118: 22–26. pmid:10636409
  11. 11. Hrynchak P, Simpson T. Optical coherence tomography: an introduction to the technique and its use. Optom Vis Sci Off Publ Am Acad Optom. 2000;77: 347–356.
  12. 12. Kee C, Cho C. Evaluation of retinal nerve fiber layer thickness in the area of apparently normal hemifield in glaucomatous eyes with optical coherence tomography. J Glaucoma. 2003;12: 250–254. pmid:12782844
  13. 13. Schuman JS, Hee MRM, Arya AV, Pedut-Kloizman T, Puliafito CA, Fujimoto JG, et al. Optical coherence tomography: A new tool for glaucoma diagnosis. Curr Opin Ophthalmol. 1995;6: 89–95. pmid:10150863
  14. 14. Bussel II, Wollstein G, Schuman JS. OCT for glaucoma diagnosis, screening and detection of glaucoma progression. Br J Ophthalmol. 2014;98: ii15–ii19. pmid:24357497
  15. 15. Sung KR, Kim JS, Wollstein G, Folio L, Kook MS, Schuman JS. Imaging of the retinal nerve fibre layer with spectral domain optical coherence tomography for glaucoma diagnosis. Br J Ophthalmol. 2011;95: 909–914. pmid:21030413
  16. 16. Leung CK, Cheung CYL, Weinreb RN, Qiu K, Liu S, Li H, et al. Evaluation of Retinal Nerve Fiber Layer Progression in Glaucoma: A Study on Optical Coherence Tomography Guided Progression Analysis. Invest Ophthalmol Vis Sci. 2010;51: 217–222. pmid:19684001
  17. 17. Reus NJ, Lemij HG, Garway-Heath DF, Airaksinen PJ, Anton A, Bron AM, et al. Clinical Assessment of Stereoscopic Optic Disc Photographs for Glaucoma: The European Optic Disc Assessment Trial. Ophthalmology. 2010;117: 717–723. pmid:20045571
  18. 18. Oddone F, Centofanti M, Tanga L, Parravano M, Michelessi M, Schiavone M, et al. Influence of Disc Size on Optic Nerve Head versus Retinal Nerve Fiber Layer Assessment for Diagnosing Glaucoma. Ophthalmology. 2011;118: 1340–1347. pmid:21474186
  19. 19. Zeimer R, Asrani S, Zou S, Quigley H, Jampel H. Quantitative detection of glaucomatous damage at the posterior pole by retinal thickness mapping. Ophthalmology. 1998;105: 224–231. pmid:9479279
  20. 20. Guedes V, Schuman JS, Hertzmark E, Wollstein G, Correnti A, Mancini R, et al. Optical coherence tomography measurement of macular and nerve fiber layer thickness in normal and glaucomatous human eyes. Ophthalmology. 2003;110: 177–189. pmid:12511364
  21. 21. Medeiros FA, Zangwill LM, Bowd C, Vessani RM, Susanna R, Weinreb RN. Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography. Am J Ophthalmol. 2005;139: 44–55. pmid:15652827
  22. 22. Leung CKS, Chan W-M, Yung W-H, Ng ACK, Woo J, Tsang M-K, et al. Comparison of macular and peripapillary measurements for the detection of glaucoma. Ophthalmology. 2005;112: 391–400. pmid:15745764
  23. 23. Mori S, Hangai M, Sakamoto A, Yoshimura N. Spectral-domain Optical Coherence Tomography Measurement of Macular Volume for Diagnosing Glaucoma: J Glaucoma. 2010;19: 528–534. pmid:20164794
  24. 24. Tan O, Chopra V, Lu AT-H, Schuman JS, Ishikawa H, Wollstein G, et al. Detection of Macular Ganglion Cell Loss in Glaucoma by Fourier-Domain Optical Coherence Tomography. Ophthalmology. 2009;116: 2305–2314.e2. pmid:19744726
  25. 25. Nakatani Y, Higashide T, Ohkubo S, Takeda H, Sugiyama K. Evaluation of macular thickness and peripapillary retinal nerve fiber layer thickness for detection of early glaucoma using spectral domain optical coherence tomography. J Glaucoma. 2011;20: 252–259. pmid:20520570
  26. 26. Girkin CA, Liebmann J, Fingeret M, Greenfield DS, Medeiros F. The effects of race, optic disc area, age, and disease severity on the diagnostic performance of spectral-domain optical coherence tomography. Invest Ophthalmol Vis Sci. 2011;52: 6148–6153. pmid:21421879
  27. 27. Reichel E, Ho J, Duker JS. OCT Units: Which One Is Right for Me? Rev Ophthalmol. 2009;
  28. 28. Kotowski J, Wollstein G, Ishikawa H, Schuman JS. Imaging of the optic nerve and retinal nerve fiber layer: An essential part of glaucoma diagnosis and monitoring. Surv Ophthalmol. 2014;59: 458–467. pmid:24388709
  29. 29. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151: 264–269. pmid:19622511
  30. 30. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155: 529–536. pmid:22007046
  31. 31. Zhou X-H, McClish DK, Obuchowski NA. Statistical methods in diagnostic medicine. New York: John Wiley & Sons, Ltd; 2009.
    • 32. Hodapp E, Parrish RI, Anderson D. Clinical decisions in glaucoma. St. Louis: The CV Mosby Co; 1993.
      • 33. Ajtony C, Balla Z, Somoskeoy S, Kovacs B. Relationship between Visual Field Sensitivity and Retinal Nerve Fiber Layer Thickness as Measured by Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2007;48: 258. pmid:17197541
      • 34. Akashi A, Kanamori A, Nakamura M, Fujihara M, Yamada Y, Negi A. The Ability of Macular Parameters and Circumpapillary Retinal Nerve Fiber Layer by Three SD-OCT Instruments to Diagnose Highly Myopic GlaucomaAbility of Macular Parameters. Invest Ophthalmol Vis Sci. 2013;54: 6025–6032. pmid:23908182
      • 35. Akashi A, Kanamori A, Nakamura M, Fujihara M, Yamada Y, Negi A. Comparative Assessment for the Ability of Cirrus, RTVue, and 3D-OCT to Diagnose Glaucoma. Investig Opthalmology Vis Sci. 2013;54: 4478. pmid:23737470
      • 36. Akashi A, Kanamori A, Ueda K, Inoue Y, Yamada Y, Nakamura M. The Ability of SD-OCT to Differentiate Early Glaucoma With High Myopia From Highly Myopic Controls and Nonhighly Myopic Controls. Investig Opthalmology Vis Sci. 2015;56: 6573. pmid:26567476
      • 37. Aptel F, Sayous R, Fortoul V, Beccat S, Denis P. Structure–Function Relationships Using Spectral-Domain Optical Coherence Tomography: Comparison With Scanning Laser Polarimetry. Am J Ophthalmol. 2010;150: 825–833.e1. pmid:20851372
      • 38. Arifoglu HB, Simavli H, Midillioglu I, Berk Ergun S, Simsek S. Comparison of Ganglion Cell and Retinal Nerve Fiber Layer Thickness in Pigment Dispersion Syndrome, Pigmentary Glaucoma, and Healthy Subjects with Spectral-domain OCT. Semin Ophthalmol. 2017;32: 204–209. pmid:26291741
      • 39. Arintawati P, Sone T, Akita T, Tanaka J, Kiuchi Y. The Applicability of Ganglion Cell Complex Parameters Determined From SD-OCT Images to Detect Glaucomatous Eyes: J Glaucoma. 2013;22: 713–718. pmid:22668975
      • 40. Asaoka R, Hirasawa K, Iwase A, Fujino Y, Murata H, Shoji N, et al. Validating the Usefulness of the “Random Forests” Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography. Am J Ophthalmol. 2017;174: 95–103. pmid:27836484
      • 41. Badalà F, Nouri-Mahdavi K, Raoof DA, Leeprechanon N, Law SK, Caprioli J. Optic Disk and Nerve Fiber Layer Imaging to Detect Glaucoma. Am J Ophthalmol. 2007;144: 724–732. pmid:17868631
      • 42. Baskaran M, Ong E-L, Li J-L, Cheung CY, Chen D, Perera SA, et al. Classification Algorithms Enhance the Discrimination of Glaucoma from Normal Eyes Using High-Definition Optical Coherence TomographyClassification Algorithms and Glaucoma Diagnosis. Invest Ophthalmol Vis Sci. 2012;53: 2314–2320. pmid:22427583
      • 43. Begum VU, Addepalli UK, Yadav RK, Shankar K, Senthil S, Garudadri CS, et al. Ganglion Cell-Inner Plexiform Layer Thickness of High Definition Optical Coherence Tomography in Perimetric and Preperimetric GlaucomaDiagnostic Ability of GCIPL Parameters in Glaucoma. Invest Ophthalmol Vis Sci. 2014;55: 4768–4775. pmid:25015361
      • 44. Bertuzzi F, Benatti E, Esempio G, Rulli E, Miglior S. Evaluation of Retinal Nerve Fiber Layer Thickness Measurements for Glaucoma Detection: GDx ECC Versus Spectral-domain OCT. J Glaucoma. 2014;23: 232–239. pmid:23970337
      • 45. Bizios D, Heijl A, Bengtsson B. Integration and fusion of standard automated perimetry and optical coherence tomography data for improved automated glaucoma diagnostics. BMC Ophthalmol. 2011;11: 20. pmid:21816080
      • 46. Blumberg DM, Dale E, Pensec N, Cioffi GA, Radcliffe N, Pham M, et al. Discrimination of Glaucoma Patients From Healthy Individuals Using Combined Parameters From Spectral-domain Optical Coherence Tomography in an African American Population: J Glaucoma. 2016;25: e196–e203. pmid:26066503
      • 47. Blumberg DM, De Moraes CG, Liebmann JM, Garg R, Chen C, Theventhiran A, et al. Technology and the Glaucoma Suspect. Investig Opthalmology Vis Sci. 2016;57: OCT80. pmid:27409509
      • 48. Bourne RR, Medeiros FA, Bowd C, Jahanbakhsh K, Zangwill LM, Weinreb RN. Comparability of retinal nerve fiber layer thickness measurements of optical coherence tomography instruments. Invest Ophthalmol Vis Sci. 2005;46: 1280–1285. pmid:15790891
      • 49. Brusini P, Salvetat ML, Zeppieri M, Tosoni C, Parisi L, Felletti M. Comparison between GDx VCC scanning laser polarimetry and Stratus OCT optical coherence tomography in the diagnosis of chronic glaucoma. Acta Ophthalmol Scand. 2006;84: 650–655. pmid:16965496
      • 50. Burgansky-Eliash Z, Wollstein G, Chu T, Ramsey JD, Glymour C, Noecker RJ, et al. Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study. Investig Opthalmology Vis Sci. 2005;46: 4147. pmid:16249492
      • 51. Calvo P, Ferreras A, Abadia B, Ara M, Figus M, Pablo LE, et al. Assessment of the Optic Disc Morphology Using Spectral-Domain Optical Coherence Tomography and Scanning Laser Ophthalmoscopy. BioMed Res Int. 2014;2014: 1–6. pmid:25110668
      • 52. Cho JW, Sung KR, Hong JT, Um TW, Kang SY, Kook MS. Detection of Glaucoma by Spectral Domain-scanning Laser Ophthalmoscopy/Optical Coherence Tomography (SD-SLO/OCT) and Time Domain Optical Coherence Tomography: J Glaucoma. 2011;20: 15–20. pmid:20436370
      • 53. Choi YJ, Jeoung JW, Park KH, Kim DM. Glaucoma Detection Ability of Ganglion Cell-Inner Plexiform Layer Thickness by Spectral-Domain Optical Coherence Tomography in High Myopia. Investig Opthalmology Vis Sci. 2013;54: 2296. pmid:23462754
      • 54. Choi YJ, Jeoung JW, Park KH, Kim DM. Clinical Use of an Optical Coherence Tomography Linear Discriminant Function for Differentiating Glaucoma From Normal Eyes: J Glaucoma. 2016;25: e162–e169. pmid:25580887
      • 55. Dave P, Shah J. Diagnostic accuracy of posterior pole asymmetry analysis parameters of spectralis optical coherence tomography in detecting early unilateral glaucoma. Indian J Ophthalmol. 2015;63: 837. pmid:26669335
      • 56. DeLeo´n-Ortega JE, Arthur SN, McGwin G, Xie A, Monheit BE, Girkin CA. Discrimination between Glaucomatous and Nonglaucomatous Eyes Using Quantitative Imaging Devices and Subjective Optic Nerve Head Assessment. Investig Opthalmology Vis Sci. 2006;47: 3374. pmid:16877405
      • 57. Fang Y, Pan Y, Li M, Qiao R, Cai Y. Diagnostic capability of Fourier-Domain optical coherence tomography in early primary open angle glaucoma. Chin Med J (Engl). 2010;123: 2045–2050.
      • 58. Garudadri CS, Rao HL, Parikh RS, Jonnadula GB, Selvaraj P, Nutheti R, et al. Effect of Optic Disc Size and Disease Severity on the Diagnostic Capability of Glaucoma Imaging Technologies in an Indian Population: J Glaucoma. 2012;21: 475–480. pmid:21522023
      • 59. Gunvant P, Zheng Y, Essock EA, Parikh RS, Prabakaran S, Babu JG, et al. Comparison of Shape-based Analysis of Retinal Nerve Fiber Layer Data Obtained From OCT and GDx-VCC: J Glaucoma. 2009;18: 464–471. pmid:19680055
      • 60. Gunvant P, Zheng Y, Essock EA, Parikh RS, Prabakaran S, Babu JG, et al. Application of shape-based analysis methods to OCT retinal nerve fiber layer data in glaucoma. J Glaucoma. 2007;16: 543–548. pmid:17873716
      • 61. Gyatsho J, Kaushik S, Gupta A, Pandav SS, Ram J. Retinal nerve fiber layer thickness in normal, ocular hypertensive, and glaucomatous Indian eyes: an optical coherence tomography study. J Glaucoma. 2008;17: 122–127. pmid:18344758
      • 62. Hirasawa H, Mayama C, Tomidokoro A, Araie M, Iwase A, Sugiyama K, et al. Diagnostic performance and reproducibility of circumpapillary retinal nerve fiber layer thickness measurement in 10-degree sectors in early stage glaucoma. Jpn J Ophthalmol. 2015;59: 86–93. pmid:25523886
      • 63. Hirashima T, Hangai M, Nukada M, Nakano N, Morooka S, Akagi T, et al. Frequency-doubling technology and retinal measurements with spectral-domain optical coherence tomography in preperimetric glaucoma. Graefes Arch Clin Exp Ophthalmol. 2013;251: 129–137. pmid:22684903
      • 64. Hoesl LM, Tornow RP, Schrems WA, Horn FK, Mardin CY, Kruse FE, et al. Glaucoma Diagnostic Performance of GDxVCC and Spectralis OCT on Eyes With Atypical Retardation Pattern: J Glaucoma. 2013;22: 317–324. pmid:22027931
      • 65. Holló G, Naghizadeh F, Vargha P. Accuracy of Macular Ganglion-Cell Complex Thickness to Total Retina Thickness Ratio to Detect Glaucoma in White Europeans: J Glaucoma. 2014;23: e132–e137. pmid:24247997
      • 66. Hong S, Seong GJ, Kim SS, Kang SY, Kim CY. Comparison of Peripapillary Retinal Nerve Fiber Layer Thickness Measured by Spectral vs. Time Domain Optical Coherence Tomography. Curr Eye Res. 2011;36: 125–134. pmid:21158587
      • 67. Horn FK, Mardin CY, Bendschneider D, Jünemann AG, Adler W, Tornow RP. Frequency doubling technique perimetry and spectral domain optical coherence tomography in patients with early glaucoma. Eye. 2011;25: 17–29. pmid:21102494
      • 68. Huang J-Y, Pekmezci M, Mesiwala N, Kao A, Lin S. Diagnostic Power of Optic Disc Morphology, Peripapillary Retinal Nerve Fiber Layer Thickness, and Macular Inner Retinal Layer Thickness in Glaucoma Diagnosis With Fourier-domain Optical Coherence Tomography: J Glaucoma. 2011;20: 87–94. pmid:20577117
      • 69. Huang L, Fan N, Shen X, He J. Comparison of the diagnostic ability of retinal nerve fiber layer thickness measured using time domain and spectral domain optical coherence tomography in primary open angle glaucoma. Eye Sci. 2011;26: 132–137, 142. pmid:21913343
      • 70. Hung K-C, Wu P-C, Chang H-W, Lai I-C, Tsai J-C, Lin P-W, et al. Macular parameters of Stratus optical coherence tomography for assessing glaucoma in high myopia: Macular parameters for glaucoma in high myopia. Clin Exp Optom. 2015;98: 39–44. pmid:25349103
      • 71. Hung K-C, Wu P-C, Poon Y-C, Chang H-W, Lai C, Tsai J-C, et al. Macular diagnostic ability in OCT for assessing glaucoma in high myopia. Optom Vis Sci. 2016;93: 126–135. pmid:26704143
      • 72. Hwang YH, Ahn SI, Ko SJ. Diagnostic ability of macular ganglion cell asymmetry for glaucoma: Ganglion cell asymmetry in glaucoma. Clin Experiment Ophthalmol. 2015;43: 720–726. pmid:25939316
      • 73. Hwang YH, Kim YY. Glaucoma Diagnostic Ability of Quadrant and Clock-Hour Neuroretinal Rim Assessment Using Cirrus HD Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2012;53: 2226. pmid:22410556
      • 74. Jeoung JW, Choi YJ, Park KH, Kim DM. Macular Ganglion Cell Imaging Study: Glaucoma Diagnostic Accuracy of Spectral-Domain Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2013;54: 4422. pmid:23722389
      • 75. Jeoung JW, Kim T-W, Weinreb RN, Kim SH, Park KH, Kim DM. Diagnostic Ability of Spectral-domain Versus Time-domain Optical Coherence Tomography in Preperimetric Glaucoma: J Glaucoma. 2014;23: 299–306. pmid:23377582
      • 76. Jeoung JW, Park KH. Comparison of Cirrus OCT and Stratus OCT on the ability to detect localized retinal nerve fiber layer defects in preperimetric glaucoma. Invest Ophthalmol Vis Sci. 2010;51: 938–945. pmid:19797208
      • 77. Jung H-H, Sung M-S, Heo H, Park S-W. Macular inner plexiform and retinal nerve fiber layer thickness in glaucoma. Optom Vis Sci. 2014;91: 1320–1327. pmid:25237762
      • 78. Kanamori A, Naka M, Akashi A, Fujihara M, Yamada Y, Nakamura M. Cluster Analyses of Grid-Pattern Display in Macular Parameters Using Optical Coherence Tomography for Glaucoma DiagnosisOptical Coherence Tomography in Glaucoma Diagnosis. Invest Ophthalmol Vis Sci. 2013;54: 6401–6408. pmid:23989192
      • 79. Kang SY, Sung KR, Na JH, Choi EH, Cho JW, Cheon MH, et al. Comparison Between Deviation Map Algorithm and Peripapillary Retinal Nerve Fiber Layer Measurements Using Cirrus HD-OCT in the Detection of Localized Glaucomatous Visual Field Defects: J Glaucoma. 2012;21: 372–378. pmid:21430549
      • 80. Kaushik S, Singh Pandav S, Ichhpujani P, Gupta A, Gupta P. Retinal nerve fiber layer measurement and diagnostic capability of spectral-domain versus time-domain optical coherence tomography. Eur J Ophthalmol. 2011;21: 566–572. pmid:21279977
      • 81. Kiddee W, Tantisarasart T, Wangsupadilok B. Performance of optical coherence tomography for distinguishing between normal eyes, glaucoma suspect and glaucomatous eyes. J Med Assoc Thai. 2013;96: 689–95. pmid:23951826
      • 82. Kim HJ, Lee S-Y, Park KH, Kim DM, Jeoung JW. Glaucoma Diagnostic Ability of Layer-by-Layer Segmented Ganglion Cell Complex by Spectral-Domain Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2016;57: 4799. pmid:27654408
      • 83. Kim KE, Ahn SJ, Kim DM. Comparison of two different spectral domain optical coherence tomography devices in the detection of localized retinal nerve fiber layer defects. Jpn J Ophthalmol. 2013;57: 347–358. pmid:23539100
      • 84. Kim KE, Kim SH, Jeoung JW, Park KH, Kim TW, Kim DM. Comparison of ability of time-domain and spectral-domain optical coherence tomography to detect diffuse retinal nerve fiber layer atrophy. Jpn J Ophthalmol. 2013;57: 529–539. pmid:24000036
      • 85. Kim NR, Hong S, Kim JH, Rho SS, Seong GJ, Kim CY. Comparison of Macular Ganglion Cell Complex Thickness by Fourier-Domain OCT in Normal Tension Glaucoma and Primary Open-Angle Glaucoma: J Glaucoma. 2013;22: 133–139. pmid:21701394
      • 86. Kim NR, Kim JH, Kim CY, Jun I, Je Seong G, Lee ES. Comparison of the Optic Nerve Imaging by Time-Domain Optical Coherence Tomography and Fourier-Domain Optical Coherence Tomography in Distinguishing Normal Eyes From Those With Glaucoma: J Glaucoma. 2013;22: 36–43. pmid:21623218
      • 87. Kim NR, Lee ES, Seong GJ, Choi EH, Hong S, Kim CY. Spectral-domain optical coherence tomography for detection of localized retinal nerve fiber layer defects in patients with open-angle glaucoma. Arch Ophthalmol. 2010;128: 1121–1128. pmid:20837794
      • 88. Kim NR, Lee ES, Seong GJ, Kim JH, An HG, Kim CY. Structure–function relationship and diagnostic value of macular ganglion cell complex measurement using Fourier-domain OCT in glaucoma. Invest Ophthalmol Vis Sci. 2010;51: 4646–4651. pmid:20435603
      • 89. Kim NR, Lee ES, Seong GJ, Kang SY, Kim JH, Hong S, et al. Comparing the ganglion cell complex and retinal nerve fibre layer measurements by Fourier domain OCT to detect glaucoma in high myopia. Br J Ophthalmol. 2011;95: 1115–1121. pmid:20805125
      • 90. Kim YK, Yoo BW, Jeoung JW, Kim HC, Kim HJ, Park KH. Glaucoma-Diagnostic Ability of Ganglion Cell-Inner Plexiform Layer Thickness Difference Across Temporal Raphe in Highly Myopic Eyes. Investig Opthalmology Vis Sci. 2016;57: 5856. pmid:27802515
      • 91. Kim YJ, Kang MH, Cho HY, Lim HW, Seong M. Comparative study of macular ganglion cell complex thickness measured by spectral-domain optical coherence tomography in healthy eyes, eyes with preperimetric glaucoma, and eyes with early glaucoma. Jpn J Ophthalmol. 2014;58: 244–251. pmid:24610541
      • 92. Kita Y, Kita R, Takeyama A, Anraku A, Tomita G, Goldberg I. Relationship between macular ganglion cell complex thickness and macular outer retinal thickness: a spectral-domain optical coherence tomography study: Outer retinal thickness in glaucoma. Clin Experiment Ophthalmol. 2013; n/a-n/a. pmid:23433351
      • 93. Kita Y, Kita R, Takeyama A, Takagi S, Nishimura C, Tomita G. Ability of Optical Coherence Tomography–determined Ganglion Cell Complex Thickness to Total Retinal Thickness Ratio to Diagnose Glaucoma: J Glaucoma. 2013;22: 757–762. pmid:22668980
      • 94. Koh KM, Jin S, Hwang YH. Cirrus High-definition Optical Coherence Tomography Versus Spectral Optical Coherence Tomography/Scanning Laser Ophthalmoscopy in the Diagnosis of Glaucoma. Curr Eye Res. 2014;39: 62–68. pmid:24074220
      • 95. Kotera Y, Hangai M, Hirose F, Mori S, Yoshimura N. Three-Dimensional Imaging of Macular Inner Structures in Glaucoma by Using Spectral-Domain Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2011;52: 1412. pmid:21087959
      • 96. Kotowski J, Folio LS, Wollstein G, Ishikawa H, Ling Y, Bilonick RA, et al. Glaucoma discrimination of segmented cirrus spectral domain optical coherence tomography (SD-OCT) macular scans. Br J Ophthalmol. 2012;96: 1420–1425. pmid:22914498
      • 97. Larrosa JM, Polo V, Ferreras A, García-Martín E, Calvo P, Pablo LE. Neural Network Analysis of Different Segmentation Strategies of Nerve Fiber Layer Assessment for Glaucoma Diagnosis: J Glaucoma. 2015;24: 672–678. pmid:25055209
      • 98. Lee EJ, Lee KM, Kim H, Kim T-W. Glaucoma Diagnostic Ability of the New Circumpapillary Retinal Nerve Fiber Layer Thickness Analysis Based on Bruch’s Membrane Opening. Investig Opthalmology Vis Sci. 2016;57: 4194. pmid:27548890
      • 99. Lee KM, Lee EJ, Kim T-W, Kim H. Comparison of the Abilities of SD-OCT and SS-OCT in Evaluating the Thickness of the Macular Inner Retinal Layer for Glaucoma Diagnosis. Gonzalez P, editor. PLOS ONE. 2016;11: e0147964. pmid:26812064
      • 100. Lee S, Sung KR, Cho JW, Cheon MH, Kang SY, Kook MS. Spectral-domain optical coherence tomography and scanning laser polarimetry in glaucoma diagnosis. Jpn J Ophthalmol. 2010;54: 544–549. pmid:21191714
      • 101. Leite MT, Rao HL, Zangwill LM, Weinreb RN, Medeiros FA. Comparison of the Diagnostic Accuracies of the Spectralis, Cirrus, and RTVue Optical Coherence Tomography Devices in Glaucoma. Ophthalmology. 2011; pmid:21377735
      • 102. Leite MT, Zangwill LM, Weinreb RN, Rao HL, Alencar LM, Sample PA, et al. Effect of disease severity on the performance of Cirrus spectral-domain OCT for glaucoma diagnosis. Invest Ophthalmol Vis Sci. 2010;51: 4104–4109. pmid:20335619
      • 103. Leung CK, Chan W, Chong KK-L, Yung W, Tang K, Woo J, et al. Comparative Study of Retinal Nerve Fiber Layer Measurement by StratusOCT and GDx VCC, I: Correlation Analysis in Glaucoma. Investig Opthalmology Vis Sci. 2005;46: 3214. pmid:16123421
      • 104. Leung CK, Chan W, Hui Y, Yung W, Woo J, Tsang M, et al. Analysis of Retinal Nerve Fiber Layer and Optic Nerve Head in Glaucoma with Different Reference Plane Offsets, Using Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2005;46: 891. pmid:15728545
      • 105. Leung CK, Cheung CY, Weinreb RN, Qiu Q, Liu S, Li H, et al. Retinal Nerve Fiber Layer Imaging with Spectral-Domain Optical Coherence Tomography. Ophthalmology. 2009;116: 1257–1263.e2. pmid:19464061
      • 106. Leung CK, Ye C, Weinreb RN, Cheung CYL, Qiu Q, Liu S, et al. Retinal Nerve Fiber Layer Imaging with Spectral-Domain Optical Coherence Tomography. Ophthalmology. 2010;117: 267–274. pmid:19969364
      • 107. Leung CKS, Lam S, Weinreb RN, Liu S, Ye C, Liu L, et al. Retinal Nerve Fiber Layer Imaging with Spectral-Domain Optical Coherence Tomography. Ophthalmology. 2010;117: 1684–1691. pmid:20663563
      • 108. Lisboa R, Leite MT, Zangwill LM, Tafreshi A, Weinreb RN, Medeiros FA. Diagnosing Preperimetric Glaucoma with Spectral Domain Optical Coherence Tomography. Ophthalmology. 2012;119: 2261–2269. pmid:22883689
      • 109. Lisboa R, Leite MT, Zangwill LM, Tafreshi A, Weinreb RN, Medeiros FA. Diagnosing Preperimetric Glaucoma with Spectral Domain Optical Coherence Tomography. Ophthalmology. 2012;119: 2261–2269. pmid:22883689
      • 110. Liu S, Wang B, Yin B, Milner TE, Markey MK, McKinnon SJ, et al. Retinal Nerve Fiber Layer Reflectance for Early Glaucoma Diagnosis: J Glaucoma. 2014;23: e45–e52. pmid:23835671
      • 111. Lu AT-H, Wang M, Varma R, Schuman JS, Greenfield DS, Smith SD, et al. Combining Nerve Fiber Layer Parameters to Optimize Glaucoma Diagnosis with Optical Coherence Tomography. Ophthalmology. 2008;115: 1352–1357.e2. pmid:18514318
      • 112. Medeiros FA, Lisboa R, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM. A Combined Index of Structure and Function for Staging Glaucomatous Damage. Arch Ophthalmol. 2012;130. pmid:23130365
      • 113. Medeiros FA, Zangwill LM, Bowd C, Weinreb RN. Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT optical coherence tomograph for the detection of glaucoma. Arch Ophthalmol Chic Ill 1960. 2004;122: 827–837. pmid:15197057
      • 114. Mendez-Hernandez C, Rodriguez-Uña I, Gonzalez-de-la Rosa M, Arribas-Pardo P, Garcia-Feijoo J. Glaucoma diagnostic capacity of optic nerve head haemoglobin measures compared with spectral domain OCT and HRT III confocal tomography. Acta Ophthalmol (Copenh). 2016;94: 697–704. pmid:27130650
      • 115. Monsalve B, Ferreras A, Calvo P, Urcola JA, Figus M, Monsalve J, et al. Diagnostic ability of Humphrey perimetry, Octopus perimetry, and optical coherence tomography for glaucomatous optic neuropathy. Eye. 2016; pmid:27834960
      • 116. Moreno-Montañés J, Antón A, García N, Olmo N, Morilla A, Fallon M. Comparison of Retinal Nerve Fiber Layer Thickness Values Using Stratus Optical Coherence Tomography and Heidelberg Retina Tomograph-III: J Glaucoma. 2009;18: 528–534. pmid:19745667
      • 117. Moreno-Montañés J, Olmo N, Alvarez A, García N, Zarranz-Ventura J. Cirrus High-Definition Optical Coherence Tomography Compared with Stratus Optical Coherence Tomography in Glaucoma Diagnosis. Investig Opthalmology Vis Sci. 2010;51: 335. pmid:19737881
      • 118. Na JH, Lee K, Lee JR, Baek S, Yoo SJ, Kook MS. Detection of macular ganglion cell loss in preperimetric glaucoma patients with localized retinal nerve fibre defects by spectral-domain optical coherence tomography: Detection of ganglion cell complex loss in preperimetric glaucoma. Clin Experiment Ophthalmol. 2013;41: 870–880. pmid:23777476
      • 119. Na JH, Lee KS, Lee JR, Lee Y, Kook MS. The glaucoma detection capability of spectral-domain OCT and GDx-VCC deviation maps in early glaucoma patients with localized visual field defects. Graefes Arch Clin Exp Ophthalmol. 2013;251: 2371–2382. pmid:23818227
      • 120. Na JH, Sung KR, Baek S, Kim YJ, Durbin MK, Lee HJ, et al. Detection of Glaucoma Progression by Assessment of Segmented Macular Thickness Data Obtained Using Spectral Domain Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2012;53: 3817. pmid:22562510
      • 121. Na JH, Sung KR, Baek S, Sun JH, Lee Y. Macular and Retinal Nerve Fiber Layer Thickness: Which Is More Helpful in the Diagnosis of Glaucoma? Investig Opthalmology Vis Sci. 2011;52: 8094. pmid:21911590
      • 122. Nakatani Y, Higashide T, Ohkubo S, Sugiyama K. Influences of the Inner Retinal Sublayers and Analytical Areas in Macular Scans by Spectral-Domain OCT on the Diagnostic Ability of Early Glaucoma. Investig Opthalmology Vis Sci. 2014;55: 7479. pmid:25342613
      • 123. Nomoto H, Matsumoto C, Takada S, Hashimoto S, Arimura E, Okuyama S, et al. Detectability of glaucomatous changes using SAP, FDT, flicker perimetry, and OCT. J Glaucoma. 2009;18: 165–171. pmid:19225357
      • 124. Nouri-Mahdavi K, Nowroozizadeh S, Nassiri N, Cirineo N, Knipping S, Giaconi J, et al. Macular Ganglion Cell/Inner Plexiform Layer Measurements by Spectral Domain Optical Coherence Tomography for Detection of Early Glaucoma and Comparison to Retinal Nerve Fiber Layer Measurements. Am J Ophthalmol. 2013;156: 1297–1307.e2. pmid:24075422
      • 125. Oli A, Joshi D. Can ganglion cell complex assessment on cirrus HD OCT aid in detection of early glaucoma? Saudi J Ophthalmol. 2015;29: 201–204. pmid:26155079
      • 126. Pablo LE, Ferreras A, Schlottmann PG. Retinal nerve fibre layer evaluation in ocular hypertensive eyes using optical coherence tomography and scanning laser polarimetry in the diagnosis of early glaucomatous defects. Br J Ophthalmol. 2011;95: 51–55. pmid:20576777
      • 127. Parikh RS, Parikh S, Sekhar GC, Kumar RS, Prabakaran S, Ganesh Babu J, et al. Diagnostic Capability of Optical Coherence Tomography (Stratus OCT 3) in Early Glaucoma. Ophthalmology. 2007;114: 2238–2243. pmid:17561260
      • 128. Parikh RS, Parikh SR, Thomas R. Diagnostic capability of macular parameters of Stratus OCT 3 in detection of early glaucoma. Br J Ophthalmol. 2010;94: 197–201. pmid:19493860
      • 129. Park HYL, Park CK. Structure-Function Relationship and Diagnostic Value of RNFL Area Index Compared With Circumpapillary RNFL Thickness by Spectral-Domain OCT: J Glaucoma. 2013;22: 88–97. pmid:23232911
      • 130. Park H-YL, Park CK. Diagnostic Capability of Lamina Cribrosa Thickness by Enhanced Depth Imaging and Factors Affecting Thickness in Patients with Glaucoma. Ophthalmology. 2013;120: 745–752. pmid:23260259
      • 131. Park J-W, Jung H-H, Heo H, Park S-W. Validity of the temporal-to-nasal macular ganglion cell-inner plexiform layer thickness ratio as a diagnostic parameter in early glaucoma. Acta Ophthalmol (Copenh). 2015;93: e356–e365. pmid:25619801
      • 132. Park SB, Sung KR, Kang SY, Kim KR, Kook MS. Comparison of glaucoma diagnostic capabilities of Cirrus HD and Stratus optical coherence tomography. Arch Ophthalmol. 2009;127: 1603–1609. pmid:20008715
      • 133. Pueyo V, Polo V, Larrosa JM, Ferreras A, Pablo LE, Honrubia FM. Diagnostic ability of the Heidelberg retina tomograph, optical coherence tomograph, and scanning laser polarimeter in open-angle glaucoma. J Glaucoma. 2007;16: 173–177. pmid:17473725
      • 134. Rao HL, Addepalli UK, Chaudhary S, Kumbar T, Senthil S, Choudhari NS, et al. Ability of Different Scanning Protocols of Spectral Domain Optical Coherence Tomography to Diagnose Preperimetric GlaucomaDiagnostic Ability of SDOCT in Preperimetric Glaucoma. Invest Ophthalmol Vis Sci. 2013;54: 7252–7257. pmid:24114539
      • 135. Rao HL, Kumbar T, Addepalli UK, Bharti N, Senthil S, Choudhari NS, et al. Effect of Spectrum Bias on the Diagnostic Accuracy of Spectral-Domain Optical Coherence Tomography in Glaucoma. Investig Opthalmology Vis Sci. 2012;53: 1058. pmid:22266520
      • 136. Rao HL, Yadav RK, Addepalli UK, Begum VU, Senthil S, Choudhari NS, et al. Reference Standard Test and the Diagnostic Ability of Spectral Domain Optical Coherence Tomography in Glaucoma: J Glaucoma. 2015;24: e151–e156. pmid:25014362
      • 137. Rao HL, Yadav RK, Addepalli UK, Chaudhary S, Senthil S, Choudhari NS, et al. Peripapillary Retinal Nerve Fiber Layer Assessment of Spectral Domain Optical Coherence Tomography and Scanning Laser Polarimetry to Diagnose Preperimetric Glaucoma. Bhattacharya S, editor. PLoS ONE. 2014;9: e108992. pmid:25279801
      • 138. Rao HL, Zangwill LM, Weinreb RN, Sample PA, Alencar LM, Medeiros FA. Comparison of Different Spectral Domain Optical Coherence Tomography Scanning Areas for Glaucoma Diagnosis. Ophthalmology. 2010;117: 1692–1699.e1. pmid:20493529
      • 139. Rao HL, Babu JG, Addepalli UK, Senthil S, Garudadri CS. Retinal nerve fiber layer and macular inner retina measurements by spectral domain optical coherence tomograph in Indian eyes with early glaucoma. Eye. 2012;26: 133–139. pmid:22079964
      • 140. Rao HL, Yadav RK, Addepalli UK, Chaudhary S, Senthil S, Choudhari NS, et al. Retinal nerve fiber layer evaluation of spectral domain optical coherence tomograph and scanning laser polarimeter to diagnose glaucoma. Eye. 2014;28: 654–661. pmid:24603422
      • 141. Raza AS, Zhang X, De Moraes CGV, Reisman CA, Liebmann JM, Ritch R, et al. Improving Glaucoma Detection Using Spatially Correspondent Clusters of Damage and by Combining Standard Automated Perimetry and Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2014;55: 612. pmid:24408977
      • 142. Rimayanti U, Latief MA, Arintawati P, Akita T, Tanaka J, Kiuchi Y. Width of abnormal ganglion cell complex area determined using optical coherence tomography to predict glaucoma. Jpn J Ophthalmol. 2014;58: 47–55. pmid:24150101
      • 143. Rolle T, Manerba L, Lanzafame P, Grignolo FM. Diagnostic Power of Macular Retinal Thickness Analysis and Structure-Function Relationship in Glaucoma Diagnosis Using SPECTRALIS OCT. Curr Eye Res. 2015; 1–9. pmid:26200743
      • 144. Rolle T. Ganglion cell complex and retinal nerve fiber layer measured by fourier-domain optical coherence tomography for early detection of structural damage in patients with preperimetric glaucoma. Clin Ophthalmol. 2011; 961. pmid:21792286
      • 145. Schrems WA, Mardin CY, Horn FK, Juenemann AGM, Laemmer R. Comparison of Scanning Laser Polarimetry and Optical Coherence Tomography in Quantitative Retinal Nerve Fiber Assessment: J Glaucoma. 2010;19: 83–94. pmid:19373100
      • 146. Schulze A, Lamparter J, Pfeiffer N, Berisha F, Schmidtmann I, Hoffmann EM. Diagnostic ability of retinal ganglion cell complex, retinal nerve fiber layer, and optic nerve head measurements by Fourier-domain optical coherence tomography. Graefes Arch Clin Exp Ophthalmol. 2011;249: 1039–1045. pmid:21240522
      • 147. Schulze A, Lamparter J, Pfeiffer N, Berisha F, Schmidtmann I, Hoffmann EM. Comparison of Laser Scanning Diagnostic Devices for Early Glaucoma Detection: J Glaucoma. 2015;24: 442–447. pmid:24844535
      • 148. Sehi M, Grewal DS, Sheets CW, Greenfield DS. Diagnostic Ability of Fourier-Domain vs Time-Domain Optical Coherence Tomography for Glaucoma Detection. Am J Ophthalmol. 2009;148: 597–605. pmid:19589493
      • 149. Sevim MS, Buttanri B, Acar BT, Kahya A, Vural ET, Acar S. Ability of Fourier-domain Optical Coherence Tomography to Detect Retinal Ganglion Cell Complex Atrophy in Glaucoma Patients: J Glaucoma. 2013;22: 542–549. pmid:22407395
      • 150. Shah SB, Garcia AG, Leiby BE, Cox LA, Katz LJ, Myers JS. Color Reflectivity Discretization Analysis of OCT Images in the Detection of Glaucomatous Nerve Fiber Layer Defects: J Glaucoma. 2016;25: e346–e354. pmid:26766397
      • 151. Shieh E, Lee R, Que C, Srinivasan V, Guo R, DeLuna R, et al. Diagnostic Performance of a Novel Three-Dimensional Neuroretinal Rim Parameter for Glaucoma Using High-Density Volume Scans. Am J Ophthalmol. 2016;169: 168–178. pmid:27349414
      • 152. Shin CJ, Sung KR, Um TW, Kim YJ, Kang SY, Cho JW, et al. Comparison of retinal nerve fibre layer thickness measurements calculated by the optic nerve head map (NHM4) and RNFL3.45 modes of spectral-domain optical coherence tomography (RTVue-100). Br J Ophthalmol. 2010;94: 763–767. pmid:20508052
      • 153. Shin H-Y, Park H-YL, Jung K-I, Choi J-A, Park CK. Glaucoma Diagnostic Ability of Ganglion Cell–Inner Plexiform Layer Thickness Differs According to the Location of Visual Field Loss. Ophthalmology. 2014;121: 93–99. pmid:23962652
      • 154. Shin HY, Park H-YL, Jung KI, Park CK. Glaucoma diagnosis optic disc analysis comparing Cirrus spectral domain optical coherence tomography and Heidelberg retina tomograph II. Jpn J Ophthalmol. 2013;57: 41–46. pmid:23104685
      • 155. Shin HJ, Cho BJ. Comparison of Retinal Nerve Fiber Layer Thickness between Stratus and Spectralis OCT. Korean J Ophthalmol. 2011;25: 166. pmid:21655041
      • 156. Shin JW, Uhm KB, Seong M. Retinal Nerve Fiber Layer Defect Volume Deviation Analysis Using Spectral-Domain Optical Coherence TomographyRNFL Defect Volume Deviation Analysis. Invest Ophthalmol Vis Sci. 2015;56: 21–28.
      • 157. Shoji T, Nagaoka Y, Sato H, Chihara E. Impact of high myopia on the performance of SD-OCT parameters to detect glaucoma. Graefes Arch Clin Exp Ophthalmol. 2012;250: 1843–1849. pmid:22555896
      • 158. Silva FR, Vidotti VG, Cremasco F, Dias M, Gomi ES, Costa VP. Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry. Arq Bras Oftalmol. 2013;76: 170–174. pmid:23929078
      • 159. Simavli H, Que CJ, Akduman M, Rizzo JL, Tsikata E, de Boer JF, et al. Diagnostic Capability of Peripapillary Retinal Thickness in Glaucoma Using 3D Volume Scans. Am J Ophthalmol. 2015;159: 545–556.e2. pmid:25498354
      • 160. Suh MH, Kim SK, Park KH, Kim DM, Kim SH, Kim HC. Combination of optic disc rim area and retinal nerve fiber layer thickness for early glaucoma detection by using spectral domain OCT. Graefes Arch Clin Exp Ophthalmol. 2013;251: 2617–2625. pmid:24065214
      • 161. Sullivan-Mee M, Ruegg CC, Pensyl D, Halverson K, Qualls C. Diagnostic Precision of Retinal Nerve Fiber Layer and Macular Thickness Asymmetry Parameters for Identifying Early Primary Open-Angle Glaucoma. Am J Ophthalmol. 2013;156: 567–577.e1. pmid:23810475
      • 162. Sung KR, Na JH, Lee Y. Glaucoma Diagnostic Capabilities of Optic Nerve Head Parameters as Determined by Cirrus HD Optical Coherence Tomography: J Glaucoma. 2012;21: 498–504. pmid:21637115
      • 163. Sung M-S, Kang B-W, Kim H-G, Heo H, Park S-W. Clinical Validity of Macular Ganglion Cell Complex by Spectral Domain-Optical Coherence Tomography in Advanced Glaucoma: J Glaucoma. 2014;23: 341–346. pmid:23221905
      • 164. Sung MS, Yoon JH, Heo H, Park SW. Effect of myopic refractive error on the glaucoma diagnostic ability of cirrus high-definition optical coherence tomography. Acta Ophthalmol (Copenh). 2015;93: e236–e237. pmid:25175669
      • 165. Sung M-S, Yoon J-H, Park S-W. Diagnostic Validity of Macular Ganglion Cell-Inner Plexiform Layer Thickness Deviation Map Algorithm Using Cirrus HD-OCT in Preperimetric and Early Glaucoma: J Glaucoma. 2014;23: e144–e151. pmid:24240879
      • 166. Takayama K, Hangai M, Durbin M, Nakano N, Morooka S, Akagi T, et al. A Novel Method to Detect Local Ganglion Cell Loss in Early Glaucoma Using Spectral-Domain Optical Coherence Tomography. Investig Opthalmology Vis Sci. 2012;53: 6904. pmid:22977136
      • 167. Toshev AP, Lamparter J, Pfeiffer N, Hoffmann EM. Bruch’s Membrane Opening-Minimum Rim Width Assessment With Spectral-Domain Optical Coherence Tomography Performs Better Than Confocal Scanning Laser Ophthalmoscopy in Discriminating Early Glaucoma Patients From Control Subjects: J Glaucoma. 2017;26: 27–33. pmid:27636592
      • 168. Ulas F, Dogan Ü, Kaymaz A, Çelik F, Çelebi S. Evaluation of subjects with a moderate cup to disc ratio using optical coherence tomography and Heidelberg retina tomograph 3: Impact of the disc area. Indian J Ophthalmol. 2015;63: 3. pmid:25686054
      • 169. Vessani RM, Moritz R, Batis L, Zagui RB, Bernardoni S, Susanna R. Comparison of quantitative imaging devices and subjective optic nerve head assessment by general ophthalmologists to differentiate normal from glaucomatous eyes. J Glaucoma. 2009;18: 253–261. pmid:19295383
      • 170. Wang M, Lu AT-H, Varma R, Schuman JS, Greenfield DS, Huang D. Combining Information From 3 Anatomic Regions in the Diagnosis of Glaucoma With Time-Domain Optical Coherence Tomography: J Glaucoma. 2014;23: 129–135. pmid:22828002
      • 171. Wang X, Li S, Fu J, Wu G, Mu D, Li S, et al. Comparative study of retinal nerve fibre layer measurement by RTVue OCT and GDx VCC. Br J Ophthalmol. 2011;95: 509–513. pmid:20657017
      • 172. Wollstein G, Ishikawa H, Wang J, Beaton SA, Schuman JS. Comparison of three optical coherence tomography scanning areas for detection of glaucomatous damage. Am J Ophthalmol. 2005;139: 39–43. pmid:15652826
      • 173. Wu H, de Boer JF, Chen TC. Diagnostic Capability of Spectral-Domain Optical Coherence Tomography for Glaucoma. Am J Ophthalmol. 2012;153: 815–826.e2. pmid:22265147
      • 174. Yang Z, Tatham AJ, Weinreb RN, Medeiros FA, Liu T, Zangwill LM. Diagnostic Ability of Macular Ganglion Cell Inner Plexiform Layer Measurements in Glaucoma Using Swept Source and Spectral Domain Optical Coherence Tomography. Linden R, editor. PLOS ONE. 2015;10: e0125957. pmid:25978420
      • 175. Yoon MH, Park SJ, Kim CY, Chin HS, Kim NR. Glaucoma diagnostic value of the total macular thickness and ganglion cell-inner plexiform layer thickness according to optic disc area. Br J Ophthalmol. 2014;98: 315–321. pmid:24385290
      • 176. Zhang Y, Wen W, Sun X. Comparison of Several Parameters in Two Optical Coherence Tomography Systems for Detecting Glaucomatous Defects in High Myopia. Investig Opthalmology Vis Sci. 2016;57: 4910. pmid:27654417
      • 177. Zhong Y, Shen X, Zhou X, Cheng Y, Min Y. Blue-on-yellow perimetry and optical coherence tomography in patients with preperimetric glaucoma. Clin Experiment Ophthalmol. 2009;37: 262–269. pmid:19472535
      • 178. Zhong Y, Zhou X, Cheng Y, Xie L. Relation between blue-on-yellow perimetry and optical coherence tomography in normal tension glaucoma. Can J Ophthalmol Can Ophtalmol. 2010;45: 494–500.
      • 179. Traynis I, De Moraes CG, Raza AS, Liebmann JM, Ritch R, Hood DC. Prevalence and Nature of Early Glaucomatous Defects in the Central 10° of the Visual Field. JAMA Ophthalmol. 2014;132: 291. pmid:24407153
      • 180. Mazzarella J, Cole J. The anatomy of an OCT scan. Rev Optom. 2015;152.9: 58.
      • 181. Michelessi M, Lucenteforte E, Oddone F, Brazzelli M, Parravano M, Franchi S, et al. Optic nerve head and fibre layer imaging for diagnosing glaucoma. In: The Cochrane Collaboration, editor. Cochrane Database of Systematic Reviews. Chichester, UK: John Wiley & Sons, Ltd; 2015. https://doi.org/10.1002/14651858.CD008803.pub2 pmid:26618332
        • 182. Ahmed S, Khan Z, Si F, Mao A, Pan I, Yazdi F, et al. Summary of Glaucoma Diagnostic Testing Accuracy: An Evidence-Based Meta-Analysis. J Clin Med Res. 2016;8: 641–649. pmid:27540437
        • 183. Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, et al. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma. Ophthalmology. 2016;123: 939–949. pmid:26891880
        • 184. Chen H-Y, Chang Y-C. Meta-analysis of stratus OCT glaucoma diagnostic accuracy. Optom Vis Sci. 2014;91: 1129–1139. pmid:25036543
        • 185. Fallon M, Valero O, Pazos M, Antón A. Diagnostic Accuracy of Imaging Devices in Glaucoma: A Meta-Analysis. Surv Ophthalmol. 2017; pmid:28093287
        • 186. Whiting P, Rutjes AWS, Dinnes J, Reitsma J, Bossuyt PMM, Kleijnen J. Development and validation of methods for assessing the quality of diagnostic accuracy studies. Health Technol Assess Winch Engl. 2004;8: iii, 1–234.