Comparison of Ganglion Cell Layer and Ganglion Cell/Inner Plexiform Layer Measures for Detection of Early Glaucoma

Advancing Optical Coherence Tomography Diagnostic Capabilities: Machine Learning Approaches to Detect Autoimmune Inflammatory Diseases

BACKGROUND

In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs).

METHODS

Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance.

RESULTS

The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53.

CONCLUSIONS

ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

Racial-ethnic disparities in concurrent rates of peripapillary & macular OCT parameters among a large glaucomatous clinical population

OBJECTIVES

To compare rates of change in peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGCIPL) parameters among different race-ethnicities from a large electronic health record database of subjects with or suspected of glaucoma.

METHODS

In this retrospective cohort study, rates of change were obtained using joint longitudinal linear mixed models for eyes with ≥3 visits and ≥1 year of follow-up, adjusting for age, sex, intraocular pressure, central corneal thickness, and baseline pRNFL and mGCIPL thickness. Best linear unbiased predictor estimates of various parameters were stratified by baseline glaucoma severity and analysed by racial-ethnic group.

RESULTS

A total of 21,472 spectral domain optical coherence tomography (OCT) pRNFL scans and 14,431 mGCIPL scans from 2002 eyes were evaluated. A total of 200 (15.6%) and 601 (46.8%) subjects identified as non-Hispanic Black (NHB) and Hispanic, respectively. NHB eyes exhibited faster rates of change in pRNFL among glaucoma suspect (global pRNFL -0.57 ± 0.55 µm/year vs. -0.37 ± 0.62 µm/year among Hispanics, p < 0.001), mild glaucoma (superior pRNFL quadrant -1.20 ± 1.06 µm/year vs. -0.75 ± 1.51 µm/year among non-Hispanic Whites (NHW), p = 0.043), and moderate glaucoma eyes (superior pRNFL quadrant -1.31 ± 1.49 µm/year vs. -0.52 ± 1.26 µm/year among Hispanics, p = 0.003). NHB eyes exhibited faster rates of mGCIPL loss corresponding to pRNFL rates. Global pRNFL and mGCIPL rates were strongly correlated (R2 = 0.70).

CONCLUSIONS

Adjusted rates of pRNFL and mGCIPL loss significantly differed between racial-ethnic groups when stratified by glaucoma severity, with faster rates among NHB patients. These differences highlight key racial-ethnic disparities in adjusted rates of glaucoma OCT parameters.

Diagnostic Performance for Detection of Glaucomatous Structural Damage Using Pixelwise Analysis of Retinal Thickness Measurements

Purpose

To compare the diagnostic accuracy of thickness measurements of individual and combined macular retinal layers to discriminate 188 glaucomatous and 148 glaucoma suspect eyes from 362 healthy control (HC) eyes on a pixel-by-pixel basis.

Methods

For this retrospective study, we manually corrected the segmentations of posterior pole optical coherence tomography (OCT) scans to determine the thickness of the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), the ganglion cell complex (GCC), and the total neural retina (TR). For each eye, the total number of pixels with thickness values less than the fifth percentile of the HC distribution was used to create a receiver operating characteristic (ROC) curve for each layer and for layer combinations.

Results

Using total abnormal pixel count criteria to discriminate glaucoma from HC eyes, the individual layers with the highest area under the ROC curve (AUC) were the NFL and GCL; IPL performance was significantly lower (P < 0.05). GCC had a significant higher AUC (94.3%) than individual the AUC of the NFL (92.3%) (P = 0.0231) but not higher than AUC of the GCL (93.4%) (P = 0.3487). The highest AUC (95.4%) and sensitivity (85.1%) at 95% specificity was found for the Boolean combination of NFL or GCL. The highest AUC is not significantly higher (P = 0.0882) than the AUC of the GCC but the highest sensitivity is significantly higher than the sensitivity of the GCC. This pattern was similar for discriminating between suspect and HC eyes (P = 0.0356).

Conclusions

Using pixel-based methods, the diagnostic accuracy of NFL and GCL exceeded that of IPL and TR. GCC had equivalent performance as NFL and GCL. The specific spatial locations within the posterior pole that exhibit best performance vary depending on which layer is being assessed. Recognizing this dependency highlights the importance of considering multiple layers independently, as they offer complementary information for effective and comprehensive diagnosis.

Correlation of Macular Ganglion Cell Layer + Inner Plexiform Layer (GCL + IPL) and Circumpapillary Retinal Nerve Fiber Layer (cRNFL) Thickness in Glaucoma Suspects and Glaucomatous Eyes

Purpose

The study aimed to correlate macular ganglion cell layer + inner plexiform layer (GCL + IPL) thickness and circumpapillary retinal nerve fiber layer (cRNFL) thickness and to determine the validity of GCL + IPL in the evaluation of glaucoma across different stages using the area under the curve (AUC) analysis in comparison to cRNFL.

Patients and Methods

The charts of 260 adult glaucoma suspect and glaucoma patients having macular ganglion cell analysis, optical coherence tomography (OCT) of the cRNFL and automated visual field (AVF) were reviewed. GCL + IPL thickness (average, minimum and sectoral) and sectoral cRNFL thickness were obtained. Glaucomatous eyes were further classified into stages based on the Hodapp-Anderson-Parrish Visual Field Criteria of Glaucoma Severity. AUC analysis was used to compare GCL + IPL parameters with cRNFL in glaucoma suspects and glaucoma patients.

Results

A total of 122 eyes were included in the study and were grouped into glaucoma suspects (n = 43), early or mild glaucoma (n = 40), and moderate-to-severe glaucoma (n = 39). Both GCL + IPL and cRNFL thickness parameters showed a significant decline with greater glaucoma severity. In the determination of visual field defects across all glaucoma stages, the highest AUC was obtained by minimum GCL + IPL (AUC = 0.859) with cut-off value at ≤70 µm. Average GCL + IPL had the highest AUC (0.835) in detecting progression from glaucoma suspect to mild glaucoma, while the inferior sector of the cRNFL had the highest AUC (0.937) in discerning mild from moderate-to-severe glaucoma.

Conclusion

The results of this study highlight the significance of macular ganglion cell analysis in the screening, detection and staging of glaucoma. Compared to cRNFL, macular ganglion analysis may be more beneficial in glaucoma screening and detecting progression from glaucoma suspect to mild glaucoma.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma

Glaucoma, a leading cause of irreversible blindness, poses a significant global health burden. Early detection is crucial for effective management and prevention of vision loss. This study presents a collection of novel structural biomarkers in glaucoma diagnosis. By employing advanced imaging techniques and data analysis algorithms, we now can recognize indicators of glaucomatous progression. Many research studies have revealed a correlation between the structural changes in the eye or brain, particularly in the optic nerve head and retinal nerve fiber layer, and the progression of glaucoma. These biomarkers demonstrate value in distinguishing glaucomatous eyes from healthy ones, even in the early stages of the disease. By facilitating timely detection and monitoring, they hold the potential to mitigate vision impairment and improve patient outcomes. This study marks an advancement in the field of glaucoma, offering a promising avenue for enhancing the diagnosis and possible management.

A novel HDAC8 inhibitor H7E exerts retinoprotective effects against glaucomatous injury via ameliorating aberrant Müller glia activation and oxidative stress

Glaucoma is considered a neurodegenerative disease characterized by progressive visual field defects that may lead to blindness. Although controlling intraocular pressure (IOP) is the mainstay of glaucoma treatment, some glaucoma patients have unmet needs due to unclear pathogenic mechanisms. Recently, there has been growing evidence that neuroinflammation is a potential target for the development of novel antiglaucoma agents. In this study, we investigated the protective effects and cellular mechanisms of H7E, a novel small molecule inhibits HDAC8, using in vitro and in vivo glaucoma-like models. Importantly, H7E mitigated extracellular MMP-9 activity and MCP-1 levels in glutamate- or S100B-stimulated reactive Müller glia. In addition, H7E inhibited the upregulation of inflammation- and proliferation-related signaling pathways, particularly the ERK and JNK MAPK pathways. Under conditions of oxidative damage, H7E prevents retinal cell death and reduces extracellular glutamate released from stressed Müller glia. In a mouse model of NMDA-induced retinal degeneration, H7E alleviated functional and structural defects within the inner retina as assessed by electroretinography and optical coherence tomography. Our results demonstrated that the newly identified compound H7E protects against glaucoma damage by specifically targeting HDAC8 activity in the retina. This protective effect is attributed to the inhibition of Müller glial activation and the prevention of retinal cell death caused by oxidative stress.

Comparative In Vivo Imaging of Retinal Structures in Tree Shrews, Humans, and Mice

Rodent models, such as mice and rats, are commonly used to examine retinal ganglion cell damage in eye diseases. However, as nocturnal animals, rodent retinal structures differ from primates, imposing significant limitations in studying retinal pathology. Tree shrews (Tupaia belangeri) are small, diurnal paraprimates that exhibit superior visual acuity and color vision compared with mice. Like humans, tree shrews have a dense retinal nerve fiber layer (RNFL) and a thick ganglion cell layer (GCL), making them a valuable model for investigating optic neuropathies. In this study, we applied high-resolution visible-light optical coherence tomography to characterize the tree shrew retinal structure in vivo and compare it with that of humans and mice. We quantitatively characterize the tree shrew's retinal layer structure in vivo, specifically examining the sublayer structures within the inner plexiform layer (IPL) for the first time. Next, we conducted a comparative analysis of retinal layer structures among tree shrews, mice, and humans. We then validated our in vivo findings in the tree shrew inner retina using ex vivo confocal microscopy. The in vivo and ex vivo analyses of the shrew retina build the foundation for future work to accurately track and quantify the retinal structural changes in the IPL, GCL, and RNFL during the development and progression of human optic diseases.

Relationships of Macular Functional Impairment With Structural and Vascular Changes According to Glaucoma Severity

Purpose

To determine the pointwise relationships of central visual field (VF) defects with macular ganglion cell loss and macular vessel density (VD) loss during various stages of glaucoma.

Methods

Eyes with primary open-angle glaucoma (POAG) were subjected to optical coherence tomography (OCT) and OCT angiography (OCTA) to evaluate macular ganglion cell layer (GCL) thickness and macular VD in the superficial and deep vascular complexes (SVC and DVC). OCT, OCTA, and VF locations were matched after correcting for retinal ganglion cell (RGC) displacement. Pointwise correlations of GCL thickness and VDs of the SVC and DVC with central VF sensitivity (VFS) were evaluated by Pearson's correlation analysis and compared in eyes with early and advanced POAG by Meng's test.

Results

Of the 100 eyes, 52 and 48 were classified as early and advanced POAG. Macular VD showed overall better correlation with central VFS than GCL thickness in both the early and advanced groups. SVC density showed the strongest correlation with central VFS in all groups (R = 0.327 in early group, R = 0.325 in advanced group, all P < 0.001). Although DVC density showed better correlation with VFS (R = 0.311) than GCL thickness (R = 0.212) in the early group (P < 0.001), the correlation was comparable in the advanced group (R = 0.199 and 0.176, respectively, P = 0.254).

Conclusions

After adjustment for RGC displacement, macular SVC density was better correlated with central VFS than macular GCL thickness in both early and advanced POAG. Macular DVC density showed better correlation with VFS than GCL thickness in early but not in advanced POAG, indicating that DVC loss may be involved in early central VF loss.

Evaluation of Retinal Nerve Fiber Layer and Macular Ganglion Cell Layer Thickness in Relation to Optic Disc Size

To investigate whether optic nerve ganglion cell amount is dependent on optic disc size, this trial analyzes the correlation between Bruch's membrane opening area (BMOA) and retinal nerve fiber layer (RNFL) thickness as well as macular ganglion cell layer thickness (mGCLT). Additionally, differences in RNFL and mGCLT regarding various optic disc cohorts are evaluated. This retrospective, monocentric study included 501 healthy eyes of 287 patients from the University Hospital Münster, Germany, who received macular and optic disc optical coherence tomography (OCT) scans. Rank correlation coefficients for clustered data were calculated to investigate the relationship between BMOA and thickness values of respective retinal layers. Furthermore, these values were compared between different optic disc groups based on BMOA. Statistical analysis did not reveal a significant correlation between BMOA and RNFL thickness, nor between BMOA and mGCLT. However, groupwise analysis showed global RNFL to be significantly decreased in small and large discs in comparison to medium discs. This was not observed for global mGCLT. This study extends existing normative data for mGCLT taking optic disc size into account. While the ganglion cell amount represented by the RNFL and mGCLT seemed independent of BMOA, mGCLT was superior to global RNFL in displaying optic nerve integrity in very small and very large optic discs.