Baseline lower CH measurements were significantly associated with increased risk of developing glaucomatous visual field defects over time. The prospective longitudinal design of this study supports a role of CH as a risk factor for developing glaucoma.

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PurposeCrowding refers to the phenomenon in which objects that can be recognized when viewed in isolation are unrecognizable in clutter. Crowding sets a fundamental limit to the capabilities of the peripheral vision and is essential in explaining performance in a broad array of daily tasks. Due to the effects of glaucoma on peripheral vision, we hypothesized that neural loss in the disease would lead to stronger effects of visual crowding.MethodsSubjects were asked to discriminate the orientation of a target letter when presented with surrounding flankers. The critical spacing value (scritical), which was required for correct discrimination of letter orientation, was obtained for each quadrant of the visual field. scritical values were correlated with standard automated perimetry (SAP) mean sensitivity (MS) and optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness measurements.ResultsThe study involved 13 subjects with mild glaucomatous visual field loss and 13 healthy controls. Glaucomatous eyes had significantly greater (worse) scritical than controls (170.4 ± 27.1 vs. 145.8 ± 28.0 minimum of visual angle, respectively; P = 0.007). scritical measurements were significantly associated with RNFL thickness measurements (R2 = 26%; P < 0.001) but not with SAP MS (P = 0.947).ConclusionsIn glaucoma patients, a pronounced visual crowding effect is observed, even in the presence of mild visual field loss on standard perimetry. scritical was associated with the amount of neural loss quantified by OCT. These results may have implications for understanding how glaucoma patients are affected in daily tasks where crowding effects may be significant.

PurposeAlthough recent studies have shown that macular pigment (MP) is significantly lower in glaucoma patients, this relationship merits further investigation.MethodsThis cross-sectional study included 85 glaucoma patients and 22 controls. All subjects had standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) thickness measurements. Intake of lutein (L) and zeaxanthin (Z) was estimated using a novel dietary screener. The Heidelberg Spectralis dual-wavelength autofluorescence (AF) technology was employed to study the relationship between MP and glaucoma. The association between MP volume and glaucoma was investigated using linear regression models accounting for potential confounding factors.ResultsGlaucoma patients had significantly worse SAP mean deviation (MD) and lower RNFL thickness in the study eye compared to control subjects (P < 0.001 for both). MP (volume) was comparable between groups (P = 0.436). In the univariable model, diagnosis of glaucoma was not associated with MP volume (R2 = 1.22%; P = 0.257). Dietary intake of L and Z was positively and significantly related to MP in the univariable (P = 0.022) and multivariable (P = 0.020) models.ConclusionsThese results challenge previous studies that reported that glaucoma is associated with low MP. Dietary habits were found to be the main predictor of MP in this sample. Further research is merited to better understand the relationship between glaucoma, MP, and visual performance in these patients.

In this study we developed a deep learning (DL) algorithm that detects errors in retinal never fibre layer (RNFL) segmentation on spectral-domain optical coherence tomography (SDOCT) B-scans using human grades as the reference standard. A dataset of 25,250 SDOCT B-scans reviewed for segmentation errors by human graders was randomly divided into validation plus training (50%) and test (50%) sets. The performance of the DL algorithm was evaluated in the test sample by outputting a probability of having a segmentation error for each B-scan. The ability of the algorithm to detect segmentation errors was evaluated with the area under the receiver operating characteristic (ROC) curve. Mean DL probabilities of segmentation error in the test sample were 0.90 ± 0.17 vs. 0.12 ± 0.22 (P < 0.001) for scans with and without segmentation errors, respectively. The DL algorithm had an area under the ROC curve of 0.979 (95% CI: 0.974 to 0.984) and an overall accuracy of 92.4%. For the B-scans with severe segmentation errors in the test sample, the DL algorithm was 98.9% sensitive. This algorithm can help clinicians and researchers review images for artifacts in SDOCT tests in a timely manner and avoid inaccurate diagnostic interpretations.