Angular Location of Retinal Nerve Fiber Layer Defect: Association With Myopia and Open-Angle Glaucoma

Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability

PURPOSE

Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials.

DESIGN

Retrospective cohort and simulation study.

METHODS

We included 1 eye per patient with baseline reliable VFs, OCT, clinical measures (demographics, intraocular pressure, and visual acuity), and 5 subsequent reliable VFs to forecast VF variability using DLMs and perform sample size estimates. We estimated sample size for 3 groups of eyes: all eyes (AE), low variability eyes (LVE: the subset of AE with a standard deviation of mean deviation [MD] slope residuals in the bottom 25th percentile), and DLM-predicted low variability eyes (DLPE: the subset of AE predicted to be low variability by the DLM). Deep learning models using only baseline VF/OCT/clinical data as input (DLM1), or also using a second VF (DLM2) were constructed to predict low VF variability (DLPE1 and DLPE2, respectively). Data were split 60/10/30 into train/val/test. Clinical trial simulations were performed only on the test set. We estimated the sample size necessary to detect treatment effects of 20% to 50% in MD slope with 80% power. Power was defined as the percentage of simulated clinical trials where the MD slope was significantly worse from the control. Clinical trials were simulated with visits every 3 months with a total of 10 visits.

RESULTS

A total of 2817 eyes were included in the analysis. Deep learning models 1 and 2 achieved an area under the receiver operating characteristic curve of 0.73 (95% confidence interval [CI]: 0.68, 0.76) and 0.82 (95% CI: 0.78, 0.85) in forecasting low VF variability. When compared with including AE, using DLPE1 and DLPE2 reduced sample size to achieve 80% power by 30% and 38% for 30% treatment effect, and 31% and 38% for 50% treatment effect.

CONCLUSIONS

Deep learning models can forecast eyes with low VF variability using data from a single baseline clinical visit. This can reduce sample size requirements, and potentially reduce the burden of future glaucoma clinical trials.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Optic nerve head factors associated with initial central visual field defect in primary open-angle glaucoma

We investigated optic nerve head factors associated with initial parafoveal scotoma (IPFS) in primary open-angle glaucoma. Eighty (80) patients with an IPFS and 84 patients with an initial nasal step (INS) were compared. Central retinal vascular trunk (CRVT) deviation from the Bruch's membrane opening (BMO) center was measured as a surrogate of lamina cribrosa (LC)/BMO offset, and its obliqueness was defined as the absolute value of angular deviation from the fovea-BMO axis. Proximity of retinal nerve fiber layer defect (RNFLD) was defined as the angular deviation of the inner RNFLD margin from the fovea-BMO axis. Microvasculature dropout (MvD) was defined as a focal sectoral capillary dropout with no visible microvascular network identified in the choroidal layer. Factors associated with IPFS, as compared with INS, were assessed using logistic regression analyses and conditional inference tree analysis. The IPFS group had more oblique CRVT offset (P < 0.001), RNFLD closer to the fovea (P < 0.001), more MvD (P < 0.001), and more LC defects (P < 0.001) compared to the INS group. In logistic regression analyses, obliqueness of CRVT offset (P = 0.002), RNFLD proximity (P < 0.001), and MvD (P = 0.001) were significant factors influencing the presence of IPFS. Conditional inference tree analysis showed that RNFLD closer to the fovea (P < 0.001) in the upper level, more oblique CRVT offset (P = 0.013) and presence of MvD (P = 0.001) in the lower level were associated with the probability of having IPFS. IPFS was associated with closer RNFLD location to the fovea when assessed from the BMO. Oblique LC/BMO offset may not only mask RNFLD proximity to the fovea due to a deviated funduscopic disc appearance, but also potentiate IPFS via focal LC defect and MvD.

Optic neuropathy in high myopia: Glaucoma or high myopia or both?

Due to the increasing prevalence of high myopia around the world, structural and functional damages to the optic nerve in high myopia has recently attracted much attention. Evidence has shown that high myopia is related to the development of glaucomatous or glaucoma-like optic neuropathy, and that both have many common features. These similarities often pose a diagnostic challenge that will affect the future management of glaucoma suspects in high myopia. In this review, we summarize similarities and differences in optic neuropathy arising from non-pathologic high myopia and glaucoma by considering their respective structural and functional characteristics on fundus photography, optical coherence tomography scanning, and visual field tests. These features may also help to distinguish the underlying mechanisms of the optic neuropathies and to determine management strategies for patients with high myopia and glaucoma.

Clinical usefulness of layer-by-layer deviation maps of Spectralis OCT: comparison with Cirrus OCT

BACKGROUND/AIMS

To compare the diagnostic abilities of Spectralis (Heidelberg Engineering, Heidelberg, Germany) and Cirrus (Carl Zeiss Meditec, Dublin, California, USA) spectral domain-optical coherence tomography (OCT) for retinal nerve fibre layer (RNFL) defect detection among patients with preperimetric glaucoma (PPG) and early glaucoma (EG).

METHODS

In this cross-sectional study, a total of 144 eyes (47 healthy, 43 PPG, 54 EG; MD≥-6 dB) of 144 participants underwent Spectralis and Cirrus OCT on the same day. The presence of RNFL defect on red-free RNFL photography and the respective deviation maps of Spectralis and Cirrus OCT was rated. Areas under the receiver operating characteristic curves (AUCs), sensitivities and specificities were analysed for each deviation layer to discriminate healthy eyes from PPG and EG eyes.

RESULTS

The RNFL, ganglion cell layer (GCL) and retinal layers of Spectralis OCT and the RNFL and macular ganglion cell-inner plexiform layer of Cirrus OCT showed high diagnostic performance (all AUCs >0.8) in discriminating PPG and EG eyes from healthy eyes. Among them, RNFL layer of Cirrus OCT had the largest AUC (0.840 for PPG, 0.959 for EG) but showed no statistical differences from RNFL and retinal layers of Spectralis OCT. The inner plexiform layer (IPL) of Spectralis OCT had the smallest AUC (0.563 for PPG, 0.799 for EG).

CONCLUSIONS

The Spectralis and Cirrus OCT deviation maps showed good diagnostic abilities except for the IPL layer of Spectralis. In the clinical setting, both Spectralis and Cirrus OCT can be useful for detection of RNFL defects in PPG and EG eyes.

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data

PURPOSE

To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data.

DESIGN

A retrospective cohort study.

SUBJECTS

In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs).

METHODS

We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1 + VF2; (6) V2OC: VF1 + VF2 + Clinical1 + OCT1; (7) V3: VF1 + VF2 + VF3; and (8) V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1.

MAIN OUTCOME MEASURES

The AUC of DLMs when forecasting rapidly worsening eyes.

RESULTS

Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]).

CONCLUSIONS

Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Progress in clinical characteristics of high myopia with primary open-angle glaucoma

High myopia (HM) is a significant risk factor for the occurrence and progression of primary open-angle glaucoma (POAG). Identification with POAG in the HM population is an emergent challenge. Patients with HM have a significantly higher probability of complicating POAG than those without HM. When HM is associated with POAG, the changes to the fundus caused by both of them are confused with each other, making the diagnosis of early glaucoma difficult. This article reviews available researches on HM with POAG, summarizing the characteristics of the fundus structure such as epidemiology, intraocular pressure, optic disc, ganglion cell layer, retinal nerve fiber layer, vascular density, and visual field.

Update on the Utility of Optical Coherence Tomography in the Analysis of the Optic Nerve Head in Highly Myopic Eyes with and without Glaucoma

Glaucoma diagnosis in highly myopic subjects by optic nerve head (ONH) imaging is challenging as it is difficult to distinguish structural defects related to glaucoma from myopia-related defects in these subjects. Optical coherence tomography (OCT) has evolved to become a routine examination at present, providing key information in the assessment of glaucoma based on the study of the ONH. However, the correct segmentation and interpretation of the ONH data employing OCT is still a challenge in highly myopic patients. High-resolution OCT images can help qualitatively and quantitatively describe the structural characteristics and anatomical changes in highly myopic subjects with and without glaucoma. The ONH and peripapillary area can be analyzed to measure the myopic atrophic-related zone, the existence of intrachoroidal cavitation, staphyloma, and ONH pits by OCT. Similarly, the lamina cribosa observed in the OCT images may reveal anatomical changes that justify visual defects. Several quantitative parameters of the ONH obtained from OCT images were proposed to predict the progression of visual defects in glaucoma subjects. Additionally, OCT images help identify factors that may negatively influence the measurement of the retinal nerve fiber layer (RNFL) and provide better analysis using new parameters, such as Bruch’s Membrane Opening-Minimum Rim Width, which serves as an alternative to RNFL measurements in highly myopic subjects due to its superior diagnostic ability.

Hemisphere opposite to central retinal vascular trunk deviation is earlier affected by glaucomatous damage in primary angle-closure glaucoma

PURPOSE

The purpose of the study was to investigate whether the position of the central retinal vascular trunk (CRVT), as a surrogate for lamina cribrosa (LC) offset, is associated with the dominant hemisphere of visual field defect in primary angle-closure glaucoma (PACG) eyes.

METHODS

Central retinal vascular trunk deviation was measured from Bruch's membrane opening (BMO) centre, which was delineated by OCT imaging, using the horizontal midline as a reference. The dominant hemisphere developing visual field defect was defined as three connected abnormal points (having a p-value < 5% probability of being normal) appearing in only one hemisphere or each point of the hemisphere having a statistically worse value compared with its mirrored point in the opposite hemisphere on pattern deviation plots.

RESULTS

One hundred five (80%) of 132 eyes with PACG had dominant hemisphere of visual field defect initially: 70 eyes (67%) in the superior and 35 eyes (33%) in the inferior hemisphere. The CRVT was located superiorly in the dominant superior visual field defect group (p < 0.001). A logistic regression analysis revealed that superior deviation of the CRVT was the only factor associated with dominant superior visual field defect (p < 0.001). Externally oblique border (EOB) presence was associated with larger BMO (p = 0.005) and angular deviation of CRVT (p = 0.002).

CONCLUSIONS

Central retinal vascular trunk deviation was associated with the dominant hemisphere of visual field defect in PACG eyes. This finding implies that the LC position relative to the BMO centre (LC/BMO offset) may incur structural vulnerability in the optic nerve head of PACG eyes.

Offset of openings in optic nerve head canal at level of Bruch's membrane, anterior sclera, and lamina cribrosa

We compared the central retinal vascular trunk (CRVT) position, as a surrogate of lamina cribrosa (LC) offset, with the anterior scleral opening (ASCO) offset from the Bruch’s membrane opening (BMO). Based on the BMO-centered radial scans, the BMO and ASCO margins were demarcated, and each center was determined as the center of the best-fitted ellipse for each margin. The ASCO/BMO offset was defined as the offset between each center. Angular deviations and the extent of ASCO and CRVT offsets from the BMO center were compared directly. Incomplete demarcation of ASCO was found in 20%, which was associated with a larger BMO area and a larger ASCO offset from the BMO. The angular deviation of ASCO offset was associated with that of CRVT offset and that of the longest externally oblique border. The ASCO offset was smaller than the CRVT offset, and, unlike the CRVT offset, it was rarely deviated to the inferior side. The complete ASCO margin might not be demarcatable when determined on BMO-centered radial scans in the presence of an offset. Also, the ASCO, which reflects only the superficial scleral layer, might not reflect the LC position, because the LC might be shifted further from the ASCO.