Although glaucoma is classified as an optic nerve disease, pathologically it is characterized by the death of retinal ganglion cells (RGCs) and their axons. Traditionally, loss of RGCs has been judged based on damage to the optic nerve head (ONH) and visual field (VF) deficits. Because these assessments do not exactly reflect the extent to which the RGC population is affected, it became evident that any method enabling measurement of RGCs would advance both early detection and longitudinal monitoring of glaucoma. Advances in ocular imaging techniques, particularly the advent of optical coherence tomography (OCT), have enabled assessment of RGC axons by measuring the thickness of the peripapillary retinal nerve fiber layer (RNFL). In addition to peripapillary RNFL, OCT devices can measure the thickness of the whole retina, particularly in the macular area. Decreases in macular thickness that are believed to be due to loss of RGCs and that correlate with both RNFL thickness and VF defects have been reported in glaucomatous eyes.1–8 However, measuring the whole macular thickness as a surrogate for glaucoma has the disadvantage of taking into account cell layers (i.e., outer retinal layers) that are not affected in glaucoma, thus obscuring the actual contribution of the RGC layer to the process.

Since its introduction, OCT has undergone several improvements, the latest of which is spectral domain (SD) technology. More recent advances in segmentation algorithms by several research groups have made possible the visualization and measurement of individual retinal layers with OCT in the macular region.6,9–18 Anatomically, the human retina contains more than 1 million RGCs, with substantial interindividual variability; approximately 50% of the cells are concentrated within 4.5 mm of the fovea.19 Although there is no variability in the RGC population in this small parafoveal area, there is, in contrast, considerable variability in the entire retina, resulting in variability in the number of axons in the peripapillary region.19

Glaucoma preferentially affects the ganglion cell complex (GCC), which is the sum of the three innermost layers: the RNFL, which is composed of axons; the ganglion cell layer (GCL), which is composed of cell bodies; and the inner plexiform layer (IPL), which contains the RGC dendrites. A few recent studies20–22 have shown that measurement of macular GCC thickness has the same glaucoma diagnostic performance as RNFL thickness. However, it is possible that including the RNFL in the measurement may affect the diagnostic ability of GCC thickness. An underlying assumption here is that the ganglion cell structure differs less in a normal population than do other diagnostically important structures such as the RNFL and the ONH. As such, the potential sensitivity of a measurement that is compared with that in a normal population could be higher because the difference would more likely be the result of pathologic change than of normal variation. This is a working hypothesis because until the advent of SD-OCT, we have been unable to study this in a reliable way. Furthermore, it is also hypothesized that the normal ganglion cell structure has symmetries that could be exploited in attempting to detect loss; an example would be a temporal defect in the anatomy corresponding to a nasal step in the VF. Including the RNFL in the measurement would make exploiting such symmetries in the analyses impossible.

The purpose of this study was to demonstrate the capability of the ganglion cell analysis (GCA) developed for Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) to automatically segment macular layers, to measure the thickness of the complex ganglion cell–inner plexiform layer, referred to in this study as GCIPL, and to assess its intervisit reproducibility.

/pdf/macular-ganglion-cell-inner-plexiform-layer-automated-1hzk9dtpq4.pdf

Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma.

PURPOSE: To assess the relationship between visual function and macular ganglion cell complex (GCC) thickness measured by Fourier-domain optical coherence tomography (OCT) and to evaluate the diagnostic value of GCC thickness for detecting early, moderate, and severe glaucoma. METHODS: Participants underwent reliable standard automated perimetry testing and OCT imaging with optic nerve head (ONH) mode and GCC mode within a single day. The relationship between structure and function was evaluated by comparing GCC thickness with mean deviation (MD) and visual field index (VFI), by regression analysis. The results were compared with those obtained for retinal nerve fiber layer (RNFL) thickness. The area under the receiver operating characteristic curve (AUC) was used to determine the relationship between disease severity and glaucomatous changes in RNFL and GCC parameters. RESULTS: One hundred three normal control subjects and 138 patients with glaucoma were included in the present study. Compared with linear models, second-order polynomial models better described the relationships between GCC thickness and MD (P<0.001), and between GCC thickness and VFI (P<0.001). A GCC pattern parameter, global loss volume (GLV), had the highest AUC for detecting early glaucoma. The AUC of mean GCC thickness for early glaucoma was higher than that of mean RNFL; however, the difference was not significant (P=0.330). CONCLUSIONS: A curvilinear function best described the relationship between VF sensitivity and GCC thickness. Macular GCC thickness and RNFL thickness showed similar diagnostic performance for detecting early, moderate, and severe glaucoma.

Structure-function relationship and diagnostic value of macular ganglion cell complex measurement using Fourier-domain OCT in glaucoma

The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF) We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF) We also developed synthesized features from original features We then selected the best features proper for classification (diagnosis) through feature evaluation We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset To develop the glaucoma prediction model, we considered four machine learning algorithms: C50, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset Finally, we got the best learning model that produces the highest validation accuracy We analyzed quality of the models using several measures The random forest model shows best performance and C50, SVM, and KNN models show similar accuracy In the random forest model, the classification accuracy is 098, sensitivity is 0983, specificity is 0975, and AUC is 0979 The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes It will be used for predicting glaucoma against unknown examination records Clinicians may reference the prediction results and be able to make better decisions We may combine multiple learning models to increase prediction accuracy The C50 model includes decision rules for prediction It can be used to explain the reasons for specific predictions

/pdf/development-of-machine-learning-models-for-diagnosis-of-4ixt8zkftw.pdf

Development of machine learning models for diagnosis of glaucoma.

To evaluate the diagnostic ability of Fourier-domain optical coherence tomography (FD-OCT) measurements in glaucoma patients, patients with ocular hypertension, and normal subjects. Ninety-three participants with open-angle glaucoma (OAG), 58 patients with ocular hypertension (OHT), and 60 healthy control subjects were included in the study. All study participants underwent FD-OCT imaging. Retinal ganglion cell complex (GCC), macular thickness, peripapillary retinal nerve fiber layer thickness (RFNL), and optic nerve head parameters (ONH) were measured in each participant. The diagnostic ability was evaluated using area under the receiver operating characteristics curves (AUROC). Glaucoma patients showed a significant reduction in GCC and macular retinal thickness compared to patients with OHT and normal subjects. No differences in GCC were found between the patients with OHT and normal subjects. The best diagnostic ability in the comparison between glaucoma and normal subjects after adjusting for age was found for cup-to-disc ratio (AUROC = 0.848), RNFL average thickness (AUROC = 0.828), and GCC global loss volume (AUROC = 0.805). The diagnostic power of the best GCC, RNFL, and ONH parameter did not show differences beyond random variation (p > 0.05). Imaging of the GCC using FD-OCT (RTVue-100) has a comparable diagnostic ability to RNFL and ONH measurements in distinguishing between glaucoma patients and healthy subjects. No differences were found between patients with OHT and normal subjects with regard to ONH, RNFL, and GCC parameters.

Diagnostic ability of retinal ganglion cell complex, retinal nerve fiber layer, and optic nerve head measurements by Fourier-domain optical coherence tomography

Aim To compare the diagnostic ability to detect glaucomatous changes between peripapillary retinal nerve fibre layer (RNFL) thickness and the macular ganglion cell complex (GCC) in highly myopic patients using Fourier domain optical coherence tomography. Methods Participants, consecutively enrolled from January 2009 to June 2009, were imaged with RTVue-100 (NHM4 and MM7 scan). The sensitivity and specificity of a colour code less than 5% (red or yellow) for glaucoma diagnosis were calculated. Area under the receiver operator characteristic (AUROC) curves were generated to assess the ability of each parameter to detect glaucomatous changes. Results 73 normal controls and 77 glaucoma patients were included. Participants were categorised as 105 non-high myopes (spherical equivalent >−6.0 dioptres) and 45 high myopes (Spherical equivalent ≤−6.0 dioptres). The GCC thickness showed a strong correlation with RNFL thickness (correlation coefficient=0.763, p Conclusions The ability to diagnose glaucoma with macular GCC thickness was comparable with that with peripapillary RNFL thickness in high-myopia patients. Macular GCC thickness measurements may be a good alternative or a complementary measurement to RNFL thickness assessment in the clinical evaluation of glaucoma in patients with high myopia.

https://bjo.bmj.com/content/bjophthalmol/95/8/1115.full.pdf

Comparing the ganglion cell complex and retinal nerve fibre layer measurements by Fourier domain OCT to detect glaucoma in high myopia

PURPOSE. To evaluate and compare the glaucoma discrimination ability of macular inner retinal layer (MIRL) thickness with that of peripapillary retinal nerve fiber layer (pRNFL) thickness measured by spectral-domain optical coherence tomography (RTVue-100; Optovue Inc, Fremont, CA) in patients with normal-tension glaucoma (NTG). METHODS. Sixty-five healthy subjects and 102 with NTG were enrolled. MIRL thickness provided by a ganglion cell complex (GCC) scan and two RNFL thicknesses measured by the NHM4 (RNFL1) and RNFL 3.45 (RNFL2) modes of the RTVue-100 system were analyzed. The areas under the receiver operating characteristic curves (AUCs) of MIRL and pRNFL thicknesses for discriminating patients with NTG from control subjects were determined. The AUCs were compared between patients with central visual field (VF) defects (VF; 10° of fixation) and peripheral VF defects (10° from fixation). RESULTS. The average MIRL thickness showed a strong correlation with both RNFL1 and -2 thicknesses (R 2 0.773, 0.774, both P 0.0001). The AUCs for average MIRL, RNFL1, and RNFL2 thicknesses were not significantly different at 0.945, 0.973, and 0.976, respectively. However, the AUCs of the average and superior MIRL thicknesses were significantly less than that of the pRNFL thickness in eyes with moderate-toadvanced glaucoma and eyes with peripheral VF defects. CONCLUSIONS. The average MIRL thickness showed a strong correlation with pRNFL thickness, because patients with NTG at an early stage showed paracentral VF defects near the fixation point. MIRL thickness showed glaucoma discrimination ability comparable to that of pRNFL thickness in patients with NTG with early VF defects. In eyes with advanced or peripheral VF defect, pRNFL measurement showed a better glaucoma diagnostic ability than did MIRL measurement. (Invest Ophthalmol

Macular and peripapillary retinal nerve fiber layer measurements by spectral domain optical coherence tomography in normal-tension glaucoma.

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Macular and peripapillary retinal nerve fiber layer measurements by spectral domain optical coherence tomography in normal-tension glaucoma.