Segregation of neuronal-vascular components in a retinal nerve fiber layer for thickness measurement using OCT and OCT angiography

Segregation of neuronal and vascular retinal damage in patients with hypertension and diabetes

This study aimed to examine the impact of diabetes and hypertension on retinal nerve fiber layer (RNFL) thickness components. Optical coherence tomography (OCT) measurements do not consider blood vessel contribution, which this study addressed. We hypothesized that diabetes and/or hypertension would lead to thinner RNFL versus controls due to the vascular component. OCT angiography was used to measure the RNFL in 121 controls, 50 diabetes patients, 371 hypertension patients, and 177 diabetes patients with hypertension. A novel technique separated the RNFL thickness into original (vascular component) and corrected (no vascular component) measurements. Diabetes-only (98 ± 1.7 µm; p = 0.002) and diabetes with hypertension (99 ± 0.8 µm; p = 0.001) patients had thinner original RNFL versus controls (102 ± 0.8 µm). No difference was seen between hypertension-only patients (101 ± 0.5 µm; p = 0.083) and controls. After removing the blood vessel component, diabetes/hypertension groups had thinner corrected RNFL versus controls (p = 0.024). Discrepancies in diabetes/hypertension patients were due to thicker retinal blood vessels within the RNFL thickness (p = 0.002). Our findings suggest that diabetes and/or hypertension independently contribute to neurodegenerative thinning of the RNFL, even in the absence of retinopathy. The differentiation of neuronal and vascular components in RNFL thickness measurements provided by the novel technique highlights the importance of considering vascular changes in individuals with these conditions.

Association of macular vessel density and ganglion cell complex thickness with central visual field progression in glaucoma

BACKGROUND/AIMS

To evaluate the association of macular vessel density (VD) and ganglion cell complex (GCC) thickness with 10-2 central visual field (CVF) progression in glaucoma.

METHODS

In this retrospective cohort study, patients with glaucoma from Diagnostic Innovation in Glaucoma Study with≥five 10-2 visual field (VF) tests and 3-year follow-up before optical coherence tomography (OCT) and OCT angiography (OCTA) imaging were included. Whole-image GCC thickness (wiGCC) and superficial VD (wiVD) were obtained from 6*6 macula scans. The association of wiVD and wiGCC with past rate of 10-2 VF mean deviation worsening, and with past CVF progression (defined using clustered linear regression criteria) was evaluated using linear mixed models after adjusting for confounders.

RESULTS

From 238 eyes (141 patients), 25 eyes (11%) of 16 patients were CVF progressors. In the multivariable analysis of the association between OCT/OCTA parameters and past rate of 10-2 CVF worsening, lower wiVD (β=-0.04 (-0.05, -0.02); p<0.001; R2=0.32) and wiGCC (β=-0.01 (-0.01, 0.00); p=0.004; R2=0.21) were significantly associated with faster CVF worsening. For the association between OCT/OCTA parameters and past CVF progression, the multivariable analysis showed that a lower wiVD was significantly associated with increased odds of past CVF progression (OR=1.23 (1.06, 1.44) per 1% lower; p=0.008), while wiGCC did not show correlation.

CONCLUSIONS

Lower macular VD and GCC were associated with faster worsening of CVF, and lower macular VD was associated with increased odds of CVF progression. Assessment of macular OCT and OCTA may help detect glaucoma eyes with CVF progression.

Foveal avascular zone vessel density is associated with visual field progression in early-stage glaucoma eyes with central visual field damage

We investigated the relationship between foveal avascular zone (FAZ)-related parameters, assessed by optical coherence tomography angiography (OCT-A), and visual field (VF) progression in early-stage open-angle glaucoma (OAG) eyes with central visual field (CVF) defects. Early-stage glaucoma eyes [VF mean deviation (MD) ≥ - 6 dB] with CVF defects were included. The rates of longitudinal change in FAZ-related parameters and structural parameters were evaluated and compared between VF progressors and non-progressors, using linear mixed effects models. Cox proportional hazards model and linear regression models were used to identify factors associated with VF progression, the VF MD reduction rate and the change rate of mean total deviation in central 12 VF points (MTD10). A total of 131 eyes were included and VF progression was detected in 32 eyes (24.4%) during 3.45 years of follow-up. The rates of reduction in vessel density in the 300 µm width annular region surrounding the FAZ (FD300) and macular ganglion cell-inner plexiform layer thickness (mGCIPLT) were significantly faster in progressors than in non-progressors. The faster VF MD or MTD10 reduction rates were associated with faster rates of FD300 loss and mGCIPLT reduction. The FD300 reduction rate is significantly associated with VF progression in early-stage OAG eyes with CVF defects. FD300 may be an adjunctive biomarker of VF progression in glaucomatous eyes with CVF defects.

Neurovascular segregation of the retinal nerve fiber layer in glaucoma

The imaging data of one eye from 154 healthy and 143 glaucoma participants were acquired to evaluate the contributions of the neuronal and vascular components within the retinal nerve fiber layer (RNFL) for detecting glaucoma and modeling visual field loss through the use of optical coherence tomography (OCT) and OCT angiography. The neuronal and vascular components within the circumpapillary RNFL were independently evaluated. In healthy eyes, the neuronal component showed a stronger association with age (r = -0.52, p < 0.001) compared to measured RNFL thickness (r = -0.46, p < 0.001). Using the neuronal component alone improved detection of glaucoma (AUC: 0.890 ± 0.020) compared to measured RNFL thickness (AUC: 0.877 ± 0.021; χ2 = 5.54, p = 0.019). Inclusion of the capillary components with the sectoral neuronal component resulted in a significant improvement in glaucoma detection (AUC: 0.927 ± 0.015; χ2 = 15.34, p < 0.001). After adjusting for potential confounders, AUC increased to 0.952 ± 0.011. Results from modeling visual field loss in glaucoma eyes suggest that visual field losses associated with neuronal thinning were moderated in eyes with a larger capillary component. These findings suggest that segregation of the neurovascular components could help improve understanding of disease pathophysiology and affect disease management in glaucoma.

Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis

This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neuritis (ON). The training/validation dataset included 750 RNFL OCT B-scans. Performance of our algorithm was evaluated on 194 OCT B-scans from 70 healthy eyes, 82 scans from 28 NAION eyes, and 84 scans of 29 ON eyes. Results were compared to manual segmentation as a ground-truth and to RNFL calculations from the built-in instrument software. The Dice coefficient for the test images was 0.87. The mean average RNFL thickness using our U-Net was not different from the manually segmented best estimate and OCT machine data in control and ON eyes. In NAION eyes, while the mean average RNFL thickness using our U-Net algorithm was not different from the manual segmented value, the OCT machine data were different from the manual segmented values. In NAION eyes, the MAE of the average RNFL thickness was 1.18 ± 0.69 μm and 6.65 ± 5.37 μm in the U-Net algorithm segmentation and the conventional OCT machine data, respectively (P = 0.0001).

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.

Comparison of machine learning approaches for structure-function modeling in glaucoma

To evaluate machine learning (ML) approaches for structure-function modeling to estimate visual field (VF) loss in glaucoma, models from different ML approaches were trained on optical coherence tomography thickness measurements to estimate global VF mean deviation (VF MD) and focal VF loss from 24-2 standard automated perimetry. The models were compared using mean absolute errors (MAEs). Baseline MAEs were obtained from the VF values and their means. Data of 832 eyes from 569 participants were included, with 537 Asian eyes for training, and 148 Asian and 111 Caucasian eyes set aside as the respective test sets. All ML models performed significantly better than baseline. Gradient-boosted trees (XGB) achieved the lowest MAE of 3.01 (95% CI: 2.57, 3.48) dB and 3.04 (95% CI: 2.59, 3.99) dB for VF MD estimation in the Asian and Caucasian test sets, although difference between models was not significant. In focal VF estimation, XGB achieved median MAEs of 4.44 [IQR 3.45-5.17] dB and 3.87 [IQR 3.64-4.22] dB across the 24-2 VF for the Asian and Caucasian test sets and was comparable to VF estimates from support vector regression (SVR) models. VF estimates from both XGB and SVR were significantly better than the other models. These results show that XGB and SVR could potentially be used for both global and focal structure-function modeling in glaucoma.

Combining OCT and OCTA for Focal Structure-Function Modeling in Early Primary Open-Angle Glaucoma

Purpose

To investigate modeling of the focal visual field (VF) loss by combining structural measurements and vascular measurements in eyes with early primary open-angle glaucoma (POAG).

Methods

In this cross-sectional study, subjects with early glaucoma (VF mean deviation, ≥−6 dB) underwent optical coherence tomography (OCT) imaging, optical coherence tomography angiography (OCTA) imaging, and Humphrey 24-2 VF tests. Capillary perfusion densities (CPDs) were calculated after the removal of large vessels in the OCTA images. Focal associations between VF losses at the individual VF test locations, circumpapillary retinal nerve fiber layer (RNFL) thickness measurements from OCT, and CPDs were determined using nerve fiber trajectory tracings. Linear mixed models were used to model focal VF losses at each VF test location.

Results

Ninety-seven eyes with early POAG (VF mean deviation, −2.47 ± 1.64 dB) of 71 subjects were included. Focal VF modeling using a combined RNFL–CPD approach resulted in a median adjusted R² value of 0.30 (interquartile range [IQR], 0.13–0.55), whereas the RNFL-only and CPD-only approaches resulted in median values of 0.22 (IQR, 0.10–0.51) and 0.26 (IQR, 0.10–0.52), respectively. Seventeen VF locations with the combined approach had an adjusted R² value greater than 0.50. Likelihood testing at each VF test location showed that the combined approach performed significantly better at the superior nasal VF regions of the eyes compared with the univariate approaches.

Conclusions

Modeling of focal VF losses showed improvements when structural thickness and vascular parameters were included in tandem. Evaluation of VF defects in early glaucoma may benefit from considering both RNFL and OCTA characteristics.