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The purpose of this study was to assess whether growth and remodeling (G&R) theory could explain staphyloma formation from a local scleral weakening-as could occur from age-related elastin degradation, myopia progression, or other factors. A finite element model of a healthy eye was reconstructed, including the lamina cribrosa, the peripapillary sclera, and the peripheral sclera. The homogenized constrained mixture model was employed to simulate the adaptation of the sclera to alterations in its biomechanical environment over a duration of 13.7 years. G&R processes were triggered by reducing the shear stiffness of the ground matrix in the peripapillary sclera and lamina cribrosa by 85%. Three distinct G&R scenarios were investigated: (1) low mass turnover rate in combination with transmural volumetric growth; (2) high mass turnover rate in combination with transmural volumetric growth; and (3) high mass turnover rate in combination with mass density growth. In scenario 1, we observed a significant outpouching of the posterior pole, closely resembling the shape of a Type-III staphyloma. Additionally, we found a notable change in scleral curvature and a thinning of the peripapillary sclera by 84%. In contrast, scenario 2 and 3 exhibited less drastic deformations, with stable posterior staphylomas after approximately 7 years. Our proposed framework suggests that local scleral weakening is sufficient to trigger staphyloma formation under a normal level of intraocular pressure. Our model also reproduced characteristics of Type-III staphylomas. With patient-specific scleral geometries (as could be obtained with wide-field optical coherence tomography), our framework could be clinically translated to help us identify those at risks of developing posterior staphylomas., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To use finite element (FE) analysis to assess what morphologic and biomechanical factors of the iris and anterior chamber are more likely to influence angle narrowing during pupil dilation., Methods: The study consisted of 1344 FE models comprising the cornea, sclera, lens, and iris to simulate pupil dilation. For each model, we varied the following parameters: anterior chamber depth (ACD = 2-4 mm) and anterior chamber width (ACW = 10-12 mm), iris convexity (IC = 0-0.3 mm), iris thickness (IT = 0.3-0.5 mm), stiffness (E = 4-24 kPa), and Poisson's ratio (v = 0-0.3). We evaluated the change in (△∠) and the final dilated angles (∠f) from baseline to dilation for each parameter., Results: The final dilated angles decreased with a smaller ACD (∠f = 53.4° ± 12.3° to 21.3° ± 14.9°), smaller ACW (∠f = 48.2° ± 13.5° to 26.2° ± 18.2°), larger IT (∠f = 52.6° ± 12.3° to 24.4° ± 15.1°), larger IC (∠f = 45.0° ± 19.2° to 33.9° ± 16.5°), larger E (∠f = 40.3° ± 17.3° to 37.4° ± 19.2°), and larger v (∠f = 42.7° ± 17.7° to 34.2° ± 18.1°). The change in angles increased with larger ACD (△∠ = 9.37° ± 11.1° to 15.4° ± 9.3°), smaller ACW (△∠ = 7.4° ± 6.8° to 16.4° ± 11.5°), larger IT (△∠ = 5.3° ± 7.1° to 19.3° ± 10.2°), smaller IC (△∠ = 5.4° ± 8.2° to 19.5° ± 10.2°), larger E (△∠ = 10.9° ± 12.2° to 13.1° ± 8.8°), and larger v (△∠ = 8.1° ± 9.4° to 16.6° ± 10.4°)., Conclusions: The morphology of the iris (IT and IC) and its innate biomechanical behavior (E and v) were crucial in influencing the way the iris deformed during dilation, and angle closure was further exacerbated by decreased anterior chamber biometry (ACD and ACW).

Background: Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test a dedicated deep learning system (DLS) for binary classification of ODD vs papilledema (including various subgroups within each category), on conventional mydriatic digital ocular fundus photographs collected in a large international multiethnic population., Methods: This retrospective study included 4,508 color fundus images in 2,180 patients from 30 neuro-ophthalmology centers (19 countries) participating in the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) Group. For training and internal validation, we used 857 ODD images and 3,230 papilledema images, in 1,959 patients. External testing was performed on an independent data set (221 patients), including 207 images with ODD (96 visible and 111 buried), provided by 3 centers of the Optic Disc Drusen Studies Consortium, and 214 images of papilledema (92 mild-to-moderate and 122 severe) from a previously validated study., Results: The DLS could accurately distinguish between all ODD and papilledema (all severities included): area under the receiver operating characteristic curve (AUC) 0.97 (95% confidence interval [CI], 0.96-0.98), accuracy 90.5% (95% CI, 88.0%-92.9%), sensitivity 86.0% (95% CI, 82.1%-90.1%), and specificity 94.9% (95% CI, 92.3%-97.6%). The performance of the DLS remained high for discrimination of buried ODD from mild-to-moderate papilledema: AUC 0.93 (95% CI, 0.90-0.96), accuracy 84.2% (95% CI, 80.2%-88.6%), sensitivity 78.4% (95% CI, 72.2%-84.7%), and specificity 91.3% (95% CI, 87.0%-96.4%)., Conclusions: A dedicated DLS can accurately distinguish between ODD and papilledema caused by intracranial hypertension, even when considering buried ODD vs mild-to-moderate papilledema., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 by North American Neuro-Ophthalmology Society.)

Purpose: The purpose of this study was to investigate the effect of globe and optic nerve (ON) morphologies and tissue stiffnesses on gaze-induced optic nerve head deformations using parametric finite element modeling and a design of experiment (DOE) approach., Methods: A custom software was developed to generate finite element models of the eye using 10 morphological parameters: dural radius, scleral, choroidal, retinal, pial and peripapillary border tissue thicknesses, prelaminar tissue depth, lamina cribrosa (LC) depth, ON radius, and ON tortuosity. A central composite face-centered design (1045 models) was used to predict the effects of each morphological factor and their interactions on LC strains induced by 13 degrees of adduction. Subsequently, a further DOE analysis (1045 models) was conducted to study the effects and potential interactions between the top five morphological parameters identified from the initial DOE study and five critical tissue stiffnesses., Results: In the DOE analysis of 10 morphological parameters, the 5 most significant factors were ON tortuosity, dural radius, ON radius, scleral thickness, and LC depth. Further DOE analysis incorporating biomechanical parameters highlighted the importance of dural and LC stiffness. A larger dural radius and stiffer dura increased LC strains but the other main factors had the opposite effects. Notably, the significant interactions were found between dural radius with dural stiffness, ON radius, and ON tortuosity., Conclusions: This study highlights the significant impact of morphological factors on LC deformations during eye movements, with key morphological effects being more pronounced than tissue stiffnesses.

Purpose: To compare the prevalence, location and magnitude of optic nerve head (ONH) OCT-detected, exposed neural canal (ENC), externally oblique choroidal border tissue (EOCBT) and exposed scleral flange (ESF) regions in 122 highly myopic (Hi-Myo) versus 362 nonhighly myopic healthy (Non-Hi-Myo-Healthy) eyes., Design: Cross-sectional study., Methods: After OCT radial B-scan, ONH imaging, Bruch's membrane opening (BMO), the anterior scleral canal opening (ASCO), and the scleral flange opening (SFO) were manually segmented in each B-scan and projected to BMO reference plane. The direction and magnitude of BMO/ASCO offset and BMO/SFO offset as well as the location and magnitude of ENC, EOCBT and ESF regions, perineural canal (pNC) retinal nerve fiber layer thickness (RNFLT) and pNC choroidal thickness (CT) were calculated within 30° sectors relative to the Foveal-BMO (FoBMO) axis. Hi-ESF eyes were defined to be those with an ESF region ≥100 µms in at least 1 sector., Results: Hi-Myo eyes more frequently demonstrated Hi-ESF regions (87/122) than Non-Hi-myo-Healthy eyes (73/362) and contained significantly larger ENC, EOCBT, and ESF regions (P < .001) which were greatest in magnitude and prevalence within the inferior-temporal FoBMO sectors where Hi-Myo pNC-RNFLT and pNCCT were thinnest. BMO/ASCO offset and the BMO/SFO offset were both significantly increased (P < .001) in the Hi-Myo eyes, with the latter demonstrating a greater increase., Conclusions: ENC region tissue remodeling that includes the scleral flange is enhanced in Hi-Myo compared to Non-Hi-Myo-Healthy eyes. Longitudinal studies are necessary to determine whether the presence of an ENC region influences ONH susceptibility to aging and/or glaucoma., (Published by Elsevier Inc.)

Purpose: To assess intraocular pressure (IOP)-induced and gaze-induced optic nerve head (ONH) strains in subjects with high-tension glaucoma (HTG) and normal-tension glaucoma (NTG)., Design: Clinic-based cross-sectional study., Methods: The ONH from one eye of 228 subjects (114 subjects with HTG (pre-treatment IOP≥21 mm Hg) and 114 with NTG (pre-treatment IOP<21 mm Hg)) was imaged with optical coherence tomography (OCT) under the following conditions: (1) OCT primary gaze, (2) 20° adduction from OCT primary gaze, (3) 20° abduction from OCT primary gaze and (4) OCT primary gaze with acute IOP elevation (to approximately 33 mm Hg). We then performed digital volume correlation analysis to quantify IOP-induced and gaze-induced ONH tissue deformations and strains., Results: Across all subjects, adduction generated high effective strain (4.4%±2.3%) in the LC tissue with no significant difference (p>0.05) with those induced by IOP elevation (4.5%±2.4%); while abduction generated significantly lower (p=0.01) effective strain (3.1%±1.9%). The lamina cribrosa (LC) of HTG subjects exhibited significantly higher effective strain than those of NTG subjects under IOP elevation (HTG: 4.6%±1.7% vs NTG: 4.1%±1.5%, p<0.05). Conversely, the LC of NTG subjects exhibited significantly higher effective strain than those of HTG subjects under adduction (NTG: 4.9%±1.9% vs HTG: 4.0%±1.4%, p<0.05)., Conclusion: We found that NTG subjects experienced higher strains due to adduction than HTG subjects, while HTG subjects experienced higher strain due to IOP elevation than NTG subjects-and that these differences were most pronounced in the LC tissue., Competing Interests: Competing interests: MJAG is the co-founder of the AI start-up company Abyss Processing Pte Ltd., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background/aims: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness (ie, sensitivity of the ONH to changes in intraocular pressure (IOP)) from a single optical coherence tomography (OCT) volume scan of the ONH without the need for biomechanical testing and (3) identify what critical three-dimensional (3D) structural features dictate ONH robustness., Methods: 316 subjects had their ONHs imaged with OCT before and after acute IOP elevation through ophthalmo-dynamometry. IOP-induced lamina cribrosa (LC) deformations were then mapped in 3D and used to classify ONHs. Those with an average effective LC strain superior to 4% were considered fragile, while those with a strain inferior to 4% robust. Learning from these data, we compared three AI algorithms to predict ONH robustness strictly from a baseline (undeformed) OCT volume: (1) a random forest classifier; (2) an autoencoder and (3) a dynamic graph convolutional neural network (DGCNN). The latter algorithm also allowed us to identify what critical 3D structural features make a given ONH robust., Results: All three methods were able to predict ONH robustness from a single OCT volume scan alone and without the need to perform biomechanical testing. The DGCNN (area under the curve (AUC): 0.76±0.08) outperformed the autoencoder (AUC: 0.72±0.09) and the random forest classifier (AUC: 0.69±0.05). Interestingly, to assess ONH robustness, the DGCNN mainly used information from the scleral canal and the LC insertion sites., Conclusions: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT volume scan of the ONH, and without the need to perform biomechanical testing. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors., Precis: Using geometric deep learning, we can assess optic nerve head robustness (ie, sensitivity to a change in IOP) from a standard OCT scan that might help to identify fast visual field loss progressors., Competing Interests: Competing interests: MJAG and AHT are the co-founders of the AI start-up company Abyss Processing., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: We wanted to develop a deep-learning algorithm to automatically segment optic nerve head (ONH) and macula structures in three-dimensional (3D) wide-field optical coherence tomography (OCT) scans and to assess whether 3D ONH or macula structures (or a combination of both) provide the best diagnostic power for glaucoma., Methods: A cross-sectional comparative study was performed using 319 OCT scans of glaucoma eyes and 298 scans of nonglaucoma eyes. Scans were compensated to improve deep-tissue visibility. We developed a deep-learning algorithm to automatically label major tissue structures, trained with 270 manually annotated B-scans. The performance was assessed using the Dice coefficient (DC). A glaucoma classification algorithm (3D-CNN) was then designed using 500 OCT volumes and corresponding automatically segmented labels. This algorithm was trained and tested on three datasets: cropped scans of macular tissues, those of ONH tissues, and wide-field scans. The classification performance for each dataset was reported using the area under the curve (AUC)., Results: Our segmentation algorithm achieved a DC of 0.94 ± 0.003. The classification algorithm was best able to diagnose glaucoma using wide-field scans, followed by ONH scans, and finally macula scans, with AUCs of 0.99 ± 0.01, 0.93 ± 0.06 and 0.91 ± 0.11, respectively., Conclusions: This study showed that wide-field OCT may allow for significantly improved glaucoma diagnosis over typical OCTs of the ONH or macula., Translational Relevance: This could lead to mainstream clinical adoption of 3D wide-field OCT scan technology.

Purpose: The purpose of this study was to isolate the structural components of the ex vivo porcine iris tissue and to determine their biomechanical properties., Methods: The porcine stroma and dilator tissues were separated, and their dimensions were assessed using optical coherence tomography (OCT). The stroma underwent flow test (n = 32) to evaluate for permeability using Darcy's Law (ΔP = 2000 Pa, A = 0.0391 mm2), and both tissues underwent stress relaxation experiments (ε = 0.5 with initial ramp of δε = 0.1) to evaluate for their viscoelastic behaviours (n = 28). Viscoelasticity was characterized by the parameters β (half width of the Gaussian distribution), τm (mean relaxation time constant), E0 (instantaneous modulus), and E∞ (equilibrium modulus)., Results: For the stroma, the hydraulic permeability was 9.49 ± 3.05 × 10-6 mm2/Pa · s, and the viscoelastic parameters were β = 2.50 ± 1.40, and τm = 7.43 ± 4.96 s, with the 2 moduli calculated to be E0 = 14.14 ± 6.44 kPa and E∞ = 6.08 ± 2.74 kPa. For the dilator tissue, the viscoelastic parameters were β = 2.06 ± 1.33 and τm = 1.28 ± 1.27 seconds, with the 2 moduli calculated to be E0 = 9.16 ± 3.03 kPa and E∞ = 5.54 ± 1.98 kPa., Conclusions: We have established a new protocol to evaluate the biomechanical properties of the structural layers of the iris. Overall, the stroma was permeable and exhibited smaller moduli than those of the dilator muscle. An improved characterization of iris biomechanics may form the basis to further our understanding of angle closure glaucoma.

Importance: The 3-dimensional (3-D) structural phenotype of glaucoma as a function of severity was thoroughly described and analyzed, enhancing understanding of its intricate pathology beyond current clinical knowledge., Objective: To describe the 3-D structural differences in both connective and neural tissues of the optic nerve head (ONH) between different glaucoma stages using traditional and artificial intelligence-driven approaches., Design, Setting, and Participants: This cross-sectional, clinic-based study recruited 541 Chinese individuals receiving standard clinical care at Singapore National Eye Centre, Singapore, and 112 White participants of a prospective observational study at Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania. The study was conducted from May 2022 to January 2023. All participants had their ONH imaged using spectral-domain optical coherence tomography and had their visual field assessed by standard automated perimetry., Main Outcomes and Measures: (1) Clinician-defined 3-D structural parameters of the ONH and (2) 3-D structural landmarks identified by geometric deep learning that differentiated ONHs among 4 groups: no glaucoma, mild glaucoma (mean deviation [MD], ≥-6.00 dB), moderate glaucoma (MD, -6.01 to -12.00 dB), and advanced glaucoma (MD, <-12.00 dB)., Results: Study participants included 213 individuals without glaucoma (mean age, 63.4 years; 95% CI, 62.5-64.3 years; 126 females [59.2%]; 213 Chinese [100%] and 0 White individuals), 204 with mild glaucoma (mean age, 66.9 years; 95% CI, 66.0-67.8 years; 91 females [44.6%]; 178 Chinese [87.3%] and 26 White [12.7%] individuals), 118 with moderate glaucoma (mean age, 68.1 years; 95% CI, 66.8-69.4 years; 49 females [41.5%]; 97 Chinese [82.2%] and 21 White [17.8%] individuals), and 118 with advanced glaucoma (mean age, 68.5 years; 95% CI, 67.1-69.9 years; 43 females [36.4%]; 53 Chinese [44.9%] and 65 White [55.1%] individuals). The majority of ONH structural differences occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using a deep neural network, 3-D ONH structural differences were found to be present in both neural and connective tissues. Specifically, a mean of 57.4% (95% CI, 54.9%-59.9%, for no to mild glaucoma), 38.7% (95% CI, 36.9%-40.5%, for mild to moderate glaucoma), and 53.1 (95% CI, 50.8%-55.4%, for moderate to advanced glaucoma) of ONH landmarks that showed major structural differences were located in neural tissues with the remaining located in connective tissues., Conclusions and Relevance: This study uncovered complex 3-D structural differences of the ONH in both neural and connective tissues as a function of glaucoma severity. Future longitudinal studies should seek to establish a connection between specific 3-D ONH structural changes and fast visual field deterioration and aim to improve the early detection of patients with rapid visual field loss in routine clinical care.

In myopic eyes, pathological remodelling of collagen in the posterior sclera has mostly been observed ex vivo. Here we report the development of triple-input polarization-sensitive optical coherence tomography (OCT) for measuring posterior scleral birefringence. In guinea pigs and humans, the technique offers superior imaging sensitivities and accuracies than dual-input polarization-sensitive OCT. In 8-week-long studies with young guinea pigs, scleral birefringence was positively correlated with spherical equivalent refractive errors and predicted the onset of myopia. In a cross-sectional study involving adult individuals, scleral birefringence was associated with myopia status and negatively correlated with refractive errors. Triple-input polarization-sensitive OCT may help establish posterior scleral birefringence as a non-invasive biomarker for assessing the progression of myopia., (© 2023. The Author(s).)

Purpose: The purpose of this study was to assess optic nerve head (ONH) deformations following acute intraocular pressure (IOP) elevations and horizontal eye movements in control eyes, highly myopic (HM) eyes, HM eyes with glaucoma (HMG), and eyes with pathologic myopia (PM) alone or PM with staphyloma (PM + S)., Methods: We studied 282 eyes, comprising of 99 controls (between +2.75 and -2.75 diopters), 51 HM (< -5 diopters), 35 HMG, 21 PM, and 75 PM + S eyes. For each eye, we imaged the ONH using spectral-domain optical coherence tomography (OCT) under the following conditions: (1) primary gaze, (2) 20 degrees adduction, (3) 20 degrees abduction, and (4) primary gaze with acute IOP elevation (to ∼35 mm Hg) achieved through ophthalmodynamometry. We then computed IOP- and gaze-induced ONH displacements and effective strains. Effective strains were compared across groups., Results: Under IOP elevation, we found that HM eyes exhibited significantly lower strains (3.9 ± 2.4%) than PM eyes (6.9 ± 5.0%, P < 0.001), HMG eyes (4.7 ± 1.8%, P = 0.04), and PM + S eyes (7.0 ± 5.2%, P < 0.001). Under adduction, we found that HM eyes exhibited significantly lower strains (4.8% ± 2.7%) than PM + S eyes (6.0 ± 3.1%, P = 0.02). We also found that eyes with higher axial length were associated with higher strains., Conclusions: Our study revealed that eyes with HMG experienced significantly greater strains under IOP compared to eyes with HM. Furthermore, eyes with PM + S had the highest strains on the ONH of all groups.

Purpose: To use optical coherence tomography (OCT) to characterize optic nerve head (ONH) peri-neural canal (pNC) scleral bowing (pNC-SB) and pNC choroidal thickness (pNC-CT) in 69 highly myopic and 138 healthy, age-matched, control eyes., Design: Cross-sectional, case control study., Methods: Within ONH radial B-scans, Bruch membrane (BM), BM opening (BMO), anterior scleral canal opening (ASCO), and pNC scleral surface were segmented. BMO and ASCO planes and centroids were determined. pNC-SB was characterized within 30° foveal-BMO (FoBMO) sectors by 2 parameters: pNC-SB-scleral slope (pNC-SB-SS), measured within 3 pNC segments (0-300, 300-700, and 700-1000 μm from the ASCO centroid); and pNC-SB-ASCO depth relative to a pNC scleral reference plane (pNC-SB-ASCOD). pNC-CT was calculated as the minimum distance between the scleral surface and BM at 3 pNC locations (300, 700, and 1100 μm from the ASCO)., Results: pNC-SB increased and pNC-CT decreased with axial length (P < .0133; P < .0001) and age (P < .0211; P < .0004) among all study eyes. pNC-SB was increased (P < .001) and pNC-CT was decreased (P < .0279) in the highly myopic compared to control eyes, and these differences were greatest in the inferior quadrant sectors (P < .0002). Sectoral pNC-SB was not related to sectoral pNC-CT in control eyes, but was inversely related to sectoral pNC-CT (P < .0001) in the highly myopic eyes., Conclusions: Our data suggest that pNC-SB is increased and pNC-CT is decreased in highly myopic eyes and that these phenomena are greatest in the inferior sectors. They support the hypothesis that sectors of maximum pNC-SB may predict sectors of greatest susceptibility to aging and glaucoma in future longitudinal studies of highly myopic eyes. NOTE: Publication of this article is sponsored by the American Ophthalmological Society., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Current management of glaucomatous optic neuropathy is limited to intraocular pressure control. Neuroglobin (Ngb) is an endogenous neuroprotectant expressed in neurons and astrocytes. We recently showed that exogenous intravitreal Ngb reduced inflammatory cytokines and microglial activation in a rodent model of hypoxia. We thus hypothesised that IVT-Ngb may also be neuroprotective in experimental glaucoma (EG) by mitigating optic nerve (ON) astrogliosis and microgliosis as well as structural damage. In this study using a microbead-induced model of EG in six Cynomolgus primates, optical coherence imaging showed that Ngb-treated EG eyes had significantly less thinning of the peripapillary minimum rim width, retinal nerve fibre layer thickness, and ON head cupping than untreated EG eyes. Immunohistochemistry confirmed that ON astrocytes overexpressed Ngb following Ngb treatment. A reduction in complement 3 and cleaved-caspase 3 activated microglia and astrocytes was also noted. Our findings in higher-order primates recapitulate the effects of neuroprotection by Ngb treatment in rodent EG studies and suggest that Ngb may be a potential candidate for glaucoma neuroprotection in humans.

Purpose: To compare the performance of 2 relatively recent geometric deep learning techniques in diagnosing glaucoma from a single optical coherence tomographic (OCT) scan of the optic nerve head (ONH); and to identify the 3-dimensional (3D) structural features of the ONH that are critical for the diagnosis of glaucoma., Design: Comparison and evaluation of deep learning diagnostic algorithms., Methods: In this study, we included a total of 2247 nonglaucoma and 2259 glaucoma scans from 1725 participants. All participants had their ONHs imaged in 3D with Spectralis OCT. All OCT scans were automatically segmented using deep learning to identify major neural and connective tissues. Each ONH was then represented as a 3D point cloud. We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from such 3D ONH point clouds and to identify the critical 3D structural features of the ONH for glaucoma diagnosis., Results: Both the DGCNN (area under the curve [AUC]: 0.97±0.01) and PointNet (AUC: 0.95±0.02) were able to accurately detect glaucoma from 3D ONH point clouds. The critical points (ie, critical structural features of the ONH) formed an hourglass pattern, with most of them located within the neuroretinal rim in the inferior and superior quadrant of the ONH., Conclusions: The diagnostic accuracy of both geometric deep learning approaches was excellent. Moreover, we were able to identify the critical 3D structural features of the ONH for glaucoma diagnosis that tremendously improved the transparency and interpretability of our method. Consequently, our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objectives: To report the processes used to design and implement an assessment tool to inform funding decisions for competing health innovations in a tertiary hospital., Methods: We designed an assessment tool for health innovation proposals with three components: "value to the institution," "novelty," and "potential for adoption and scaling." The "value to the institution" component consisted of twelve weighted value attributes identified from the host institution's annual report; weights were allocated based on a survey of the hospital's leaders. The second and third components consisted of open-ended questions on "novelty" and "barriers to implementation" to support further dialogue. Purposive literature review was performed independently by two researchers for each assessment. The assessment tool was piloted during an institutional health innovation funding cycle., Results: We used 17 days to evaluate ten proposals. The completed assessments were shared with an independent group of panellists, who selected five projects for funding. Proposals with the lowest scores for "value to the institution" had less perceived impact on the patient-related value attributes of "access," "patient centeredness," "health outcomes," "prevention," and "safety." Similar innovations were reported in literature in seven proposals; potential barriers to implementation were identified in six proposals. We included a worked example to illustrate the assessment process., Conclusions: We developed an assessment tool that is aligned with local institutional priorities. Our tool can augment the decision-making process when funding health innovation projects. The tool can be adapted by others facing similar challenges of trying to choose the best health innovations to fund.

Purpose: (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) convolutional neural network (CNN), and with a gold-standard parameter, namely, the retinal nerve fiber layer (RNFL) thickness., Methods: Scans of the optic nerve head were acquired with OCT for 477 glaucoma and 2296 nonglaucoma subjects. All volumes were automatically segmented using deep learning to identify seven major neural and connective tissues. Each optic nerve head was then represented as a 3D point cloud with approximately 1000 points. Geometric deep learning (PointNet) was then used to provide a glaucoma diagnosis from a single 3D point cloud. The performance of our approach (reported using the area under the curve [AUC]) was compared with that obtained with a 3D CNN, and with the RNFL thickness., Results: PointNet was able to provide a robust glaucoma diagnosis solely from a 3D point cloud (AUC = 0.95 ± 0.01).The performance of PointNet was superior to that obtained with a 3D CNN (AUC = 0.87 ± 0.02 [raw OCT images] and 0.91 ± 0.02 [segmented OCT images]) and with that obtained from RNFL thickness alone (AUC = 0.80 ± 0.03)., Conclusions: We provide a proof of principle for the application of geometric deep learning in glaucoma. Our technique requires significantly less information as input to perform better than a 3D CNN, and with an AUC superior to that obtained from RNFL thickness., Translational Relevance: Geometric deep learning may help us to improve and simplify diagnosis and prognosis applications in glaucoma.

Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. We performed a prospective, cross-sectional study, where 514 Asians (257 glaucoma/257 controls) were enrolled to construct ML models for glaucoma detection, which was then tested on 356 Asians (183 glaucoma/173 controls) and 138 Caucasians (57 glaucoma/81 controls). We used the retinal nerve fibre layer (RNFL) thickness values produced by the compensation model, which is a multiple regression model fitted on healthy subjects that corrects the RNFL profile for anatomical factors and the original OCT data (measured) to build two classifiers, respectively. Both the ML models (area under the receiver operating [AUC] = 0.96 and accuracy = 92%) outperformed the measured data (AUC = 0.93; P < 0.001) for glaucoma detection in the Asian dataset. However, in the Caucasian dataset, the ML model trained with compensated data (AUC = 0.93 and accuracy = 84%) outperformed the ML model trained with original data (AUC = 0.83 and accuracy = 79%; P < 0.001) and measured data (AUC = 0.82; P < 0.001) for glaucoma detection. The performance with the ML model trained on measured data showed poor reproducibility across different datasets, whereas the performance of the compensated data was maintained. Care must be taken when ML models are applied to patient cohorts of different ethnicities., (© 2023. The Author(s).)

Background and Objectives: The distinction of papilledema from other optic nerve head (ONH) lesions mimicking papilledema, such as optic disc drusen (ODD), can be difficult in clinical practice. We aimed the following: (1) to develop a deep learning algorithm to automatically identify major structures of the ONH in 3-dimensional (3D) optical coherence tomography (OCT) scans and (2) to exploit such information to robustly differentiate among ODD, papilledema, and healthy ONHs., Methods: This was a cross-sectional comparative study of patients from 3 sites (Singapore, Denmark, and Australia) with confirmed ODD, those with papilledema due to raised intracranial pressure, and healthy controls. Raster scans of the ONH were acquired using OCT imaging and then processed to improve deep-tissue visibility. First, a deep learning algorithm was developed to identify major ONH tissues and ODD regions. The performance of our algorithm was assessed using the Dice coefficient. Second, a classification algorithm (random forest) was designed to perform 3-class classifications (1: ODD, 2: papilledema, and 3: healthy ONHs) strictly from their drusen and prelamina swelling scores (calculated from the segmentations). To assess performance, we reported the area under the receiver operating characteristic curve for each class., Results: A total of 241 patients (256 imaged ONHs, including 105 ODD, 51 papilledema, and 100 healthy ONHs) were retrospectively included in this study. Using OCT images of the ONH, our segmentation algorithm was able to isolate neural and connective tissues and ODD regions/conglomerates whenever present. This was confirmed by an averaged Dice coefficient of 0.93 ± 0.03 on the test set, corresponding to good segmentation performance. Classification was achieved with high AUCs, that is, 0.99 ± 0.001 for the detection of ODD, 0.99 ± 0.005 for the detection of papilledema, and 0.98 ± 0.01 for the detection of healthy ONHs., Discussion: Our artificial intelligence approach can discriminate ODD from papilledema, strictly using a single OCT scan of the ONH. Our classification performance was very good in the studied population, with the caveat that validation in a much larger population is warranted. Our approach may have the potential to establish OCT imaging as one of the mainstays of diagnostic imaging for ONH disorders in neuro-ophthalmology, in addition to fundus photography., (© 2022 American Academy of Neurology.)

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data., Competing Interests: Conflict of interest LP: Consultant to Eyenovia, Skye Biosciences, Character Biosciences, and Twenty Twenty. MG: Abyss Processing Pte Ltd Co-founder and Consultant. YJ: Optovue, Inc F, P; Optos, Inc. P. MS: Seymour Vision I. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

Purpose: To study the associations between optic nerve head (ONH) strains under intraocular pressure (IOP) elevation with retinal sensitivity in patients with glaucoma., Design: Clinic-based cross-sectional study., Participants: Two hundred twenty-nine patients with primary open-angle glaucoma (subdivided into 115 patients with high-tension glaucoma [HTG] and 114 patients with normal-tension glaucoma [NTG])., Methods: For 1 eye of each patient, we imaged the ONH using spectral-domain OCT under the following conditions: (1) primary gaze and (2) primary gaze with acute IOP elevation (to approximately 35 mmHg) achieved through ophthalmodynamometry. A 3-dimensional strain-mapping algorithm was applied to quantify IOP-induced ONH tissue strain (i.e., deformation) in each ONH. Strains in the prelaminar tissue (PLT), the retina, the choroid, the sclera, and the lamina cribrosa (LC) were associated (using linear regression) with measures of retinal sensitivity from the 24-2 Humphrey visual field test (Carl Zeiss Meditec). This was performed globally, then locally according to a previously published regionalization scheme., Main Outcome Measures: Associations between ONH strains and values of retinal sensitivity from visual field testing., Results: For patients with HTG, we found (1) significant negative linear associations between ONH strains and retinal sensitivity (P < 0.001; on average, a 1% increase in ONH strains corresponded to a decrease in retinal sensitivity of 1.1 decibels [dB]), (2) that high-strain regions colocalized with anatomically mapped regions of high visual field loss, and (3) that the strongest negative associations were observed in the superior region and in the PLT. In contrast, for patients with NTG, no significant associations between strains and retinal sensitivity were observed except in the superotemporal region of the LC., Conclusions: We found significant negative associations between IOP-induced ONH strains and retinal sensitivity in a relatively large glaucoma cohort. Specifically, patients with HTG who experienced higher ONH strains were more likely to exhibit lower retinal sensitivities. Interestingly, this trend in general was less pronounced in patients with NTG, which could suggest a distinct pathophysiologic relationship between the two glaucoma subtypes., (Copyright © 2022. Published by Elsevier Inc.)

Importance: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection., Objective: To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection., Design, Setting, and Participants: Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively., Main Outcomes and Measures: Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications., Results: A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images., Conclusions and Relevance: DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.

Purpose: To assess whether the 3-dimensional (3D) structural configuration of the central retinal vessel trunk and its branches (CRVT&B) could be used as a diagnostic marker for glaucoma., Design: Retrospective, deep-learning approach diagnosis study., Methods: We trained a deep learning network to automatically segment the CRVT&B from the B-scans of the optical coherence tomography (OCT) volume of the optic nerve head. Subsequently, 2 different approaches were used for glaucoma diagnosis using the structural configuration of the CRVT&B as extracted from the OCT volumes. In the first approach, we aimed to provide a diagnosis using only 3D convolutional neural networks and the 3D structure of the CRVT&B. For the second approach, we projected the 3D structure of the CRVT&B orthographically onto sagittal, frontal, and transverse planes to obtain 3 two-dimensional (2D) images, and then a 2D convolutional neural network was used for diagnosis. The segmentation accuracy was evaluated using the Dice coefficient, whereas the diagnostic accuracy was assessed using the area under the receiver operating characteristic curves (AUCs). The diagnostic performance of the CRVT&B was also compared with that of retinal nerve fiber layer (RNFL) thickness (calculated in the same cohorts)., Results: Our segmentation network was able to efficiently segment retinal blood vessels from OCT scans. On a test set, we achieved a Dice coefficient of 0.81 ± 0.07. The 3D and 2D diagnostic networks were able to differentiate glaucoma from nonglaucoma subjects with accuracies of 82.7% and 83.3%, respectively. The corresponding AUCs for the CRVT&B were 0.89 and 0.90, higher than those obtained with RNFL thickness alone (AUCs ranging from 0.74 to 0.80)., Conclusions: Our work demonstrated that the diagnostic power of the CRVT&B is superior to that of a gold-standard glaucoma parameter, that is, RNFL thickness. Our work also suggested that the major retinal blood vessels form a "skeleton"-the configuration of which may be representative of major optic nerve head structural changes as typically observed with the development and progression of glaucoma., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: To develop a novel deep-learning approach that can describe the structural phenotype of the glaucomatous optic nerve head (ONH) and can be used as a robust glaucoma diagnosis tool., Design: Retrospective, deep-learning approach diagnosis study., Method: We trained a deep-learning network to segment 3 neural-tissue and 4 connective-tissue layers of the ONH. The segmented optical coherence tomography images were then processed by a customized autoencoder network with an additional parallel branch for binary classification. The encoder part of the autoencoder reduced the segmented optical coherence tomography images into a low-dimensional latent space (LS), whereas the decoder and the classification branches reconstructed the images and classified them as glaucoma or nonglaucoma, respectively. We performed principal component analysis on the latent parameters and identified the principal components (PCs). Subsequently, the magnitude of each PC was altered in steps and reported how it impacted the morphology of the ONH., Results: The image reconstruction quality and diagnostic accuracy increased with the size of the LS. With 54 parameters in the LS, the diagnostic accuracy was 92.0 ± 2.3% with a sensitivity of 90.0 ± 2.4% (at 95% specificity), and the corresponding Dice coefficient for the reconstructed images was 0.86 ± 0.04. By changing the magnitudes of PC in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a "nonglaucoma" to a "glaucoma" condition., Conclusions: Our network was able to identify novel biomarkers of the ONH for glaucoma diagnosis. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to investigate whether the lamina cribrosa (LC) curve changes in response to intraocular pressure (IOP) reduction following administration of topical ocular hypotensive eye drops in eyes with normal tension glaucoma (NTG)., Methods: Ninety-three eyes of 93 patients with treatment naïve NTG at initial examination and with ≥20% reduction from baseline IOP following administration of topical ocular hypotensive eye drops were included. Serial horizontal B-scan images of the optic nerve head (ONH) were obtained from each eye using enhanced depth imaging spectral domain optical coherence tomography (OCT) before and 1 year after IOP-lowering treatment. The LC curvature in each eye was assessed by measuring the LC curvature index (LCCI) in horizontal OCT B-scan images obtained at three (superior, central, and inferior) locations spaced equidistantly across the vertical optic disc diameter before and after IOP-lowering treatment. We evaluated the OCT detectible change in the LC curvature based on the interval change of LCCI to exceed the intersession standard deviation of 1.96 times and factors associated with the magnitude of the LCCI change in the eyes showing significant LC change., Results: IOP decreased from 15.7 ± 2.5 mm Hg at baseline to 11.2 ± 1.7 mm Hg after topical glaucoma medication. Among the 93 subjects, 62 (66.7%) eyes showed the significant reduction of the LCCI (interssetional change over 1.5) after the treatment; greater interssessional change of the LCCI after IOP reduction was associated with younger age (P = 0.020) and larger baseline LCCI (P < 0.001)., Conclusions: The OCT detectible changes in LC curvature occurred in response to a modest decrease in the IOP in the naïve NTG eyes. The therapeutic benefit of these changes need to be assessed in longitudinal studies.

Purpose: To assess gaze evoked deformations of the optic nerve head (ONH) in thyroid eye disease (TED), using computational modelling and optical coherence tomography (OCT)., Methods: Multiple finite element models were constructed: one model of a healthy eye, and two models mimicking effects of TED; one with proptosis and another with extraocular tissue stiffening. Two additional hypothetical models had extraocular tissue softening or no extraocular tissue at all. Horizontal eye movements were simulated in these models. OCT images of the ONH of 10 healthy volunteers and 1 patient with TED were taken in primary gaze. Additional images were recorded in the same subjects performing eye movements in adduction and abduction. The resulting ONH deformation in the models and human subjects was measured by recording the 'tilt angle' (relative antero-posterior deformation of the Bruch's membrane opening)., Results: In our computational models the eyes with proptosis and stiffer extraocular tissue had greater gaze-evoked deformations than the healthy eye model, while the models with softer or no extraocular tissue had lesser deformations, in both adduction and abduction. In healthy subjects, the mean tilt angle was 1.46°±0.25 in adduction and -0.42°±0.12 in abduction. The tilt angle measured in the subject with TED was 5.37° in adduction and -2.21° in abduction., Conclusion: Computational modelling and experimental observation suggest that TED can cause increased gaze-evoked deformations of the ONH., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: To evaluate the biomechanical properties of the iris by evaluating iris movement during pupil constriction and to compare such properties between healthy and primary angle-closure glaucoma (PACG) subjects., Methods: A total of 140 subjects were recruited for this study. In a dark room, the anterior segments of one eye per subject were scanned using anterior segment optical coherence tomography imaging during induced pupil constriction with an external white light source of 1700 lux. Using a custom segmentation code, we automatically isolated the iris segments from the AS-OCT images, which were then discretized and transformed into a three-dimensional point cloud. For each iris, a finite element (FE) mesh was constructed from the point cloud, and an inverse FE simulation was performed to match the clinically observed iris constriction in the AS-OCT images. Through this optimization process, we were able to identify the elastic modulus and permeability of each iris., Results: For all 140 subjects (95 healthy and 45 PACG of Indian/Chinese ethnicity; age 60.2 ± 8.7 for PACG subjects and 57.7 ± 10.1 for healthy subjects), the simulated deformation pattern of the iris during pupil constriction matched well with OCT images. We found that the iris stiffness was higher in PACG than in healthy controls (24.5 ± 8.4 kPa vs. 17.1 ± 6.6 kPa with 40 kPa of active stress specified in the sphincter region; P < 0.001), whereas iris permeability was lower (0.41 ± 0.2 mm2/kPa s vs. 0.55 ± 0.2 mm2/kPa s; p = 0.142)., Conclusions: This study suggests that the biomechanical properties of the iris in PACG are different from those in healthy controls. An improved understanding of the biomechanical behavior of the iris may have implications for the understanding and management of angle-closure glaucoma.

Background/aims: Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma., Method: In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing., Results: With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s., Conclusion: This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma., Competing Interests: Competing interests: The preprint version of this manuscript can be found at arXiv:1909.00331. MJAG and AHT are co-founders of Abyss Processing., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: To investigate the change of border tissue configuration during axial elongation in childhood., Methods: Fifty-four subjects (108 eyes; age range, 29.3-132.5 months) who had undergone a series of swept-source optical coherence tomography scans at intervals of 6 months or longer were classified into stable axial length (AXL) eyes (n = 55; AXL change of ≤0.36 mm) and elongating AXL eyes (n = 53; AXL change of >0.36 mm). The angle between the Bruch's membrane opening (BMO) reference plane and the border tissue of Elschnig was defined as the border tissue angle (BTA). The border tissue angle, BMO distance (BMOD) and minimum rim width (MRW) were measured in the temporal and nasal regions., Results: During 15.6 ± 7.2 months of follow-up, the AXL significantly increased from 22.8 ± 1.3 mm to 23.3 ± 1.4 mm (P < 0.001). Changes of border tissue angle and AXL showed a significant correlation only in the temporal region of elongating AXL eyes (r = -0.409; P = 0.002), but not in stable AXL eyes. Both BMOD and nasal MRW significantly increased from 1482.5 ± 153.0 to 1506.1 ± 154.6 µm and from 310.6 ± 83.2 to 324.6 ± 95.6 µm, respectively (all Ps < 0.001). The changes of BMOD and nasal MRW showed a significant positive correlation with changes of AXL in elongating AXL eyes but not in stable AXL eyes., Conclusions: During the axial elongation in childhood, temporal border tissue configuration change, BMO enlargement, and nasal peripapillary tissue elevation showed a significant correlation with changes in the AXL.

Aim: To investigate the determinants of lamina cribrosa depth (LCD) in healthy eyes of Chinese and Indian Singaporean adults., Methods: The optic nerve head (ONH) of the right eye of 1396 subjects (628 Chinese and 768 Indian subjects) was imaged with optical coherence tomography (OCT, Spectralis, Heidelberg, Germany). LCD was defined as the distance from the Bruch's membrane opening (LCD-BMO) or the peripapillary sclera (LCD-PPS) reference plane to the laminar surface. A linear regression model was used to evaluate the relationship between the LCD and its determinants., Results: Both LCDs were significantly different between the two races (LCD-BMO: 421.95 (95% CI 365.32 to 491.79) µm in Chinese vs 430.39 (367.46-509.81) µm in Indians, p=0.021; and LCD-PPS: 353.34 (300.98-421.45) µm in Chinese vs 376.76 (313.39-459.78) µm in Indians, p<0.001). In the multivariable regression analysis, the LCD-PPS of the whole cohort was independently associated with females (β=-31.93, p<0.001), Indians subjects (β=21.39, p=0.004) (Chinese as the reference), axial length (Axl) (β=-6.68, p=0.032), retinal nerve fibre layer thickness (RNFL) (β=0.71, p=0.019), choroidal thickness (ChT) (β=0.41, p<0.001), vertical cup disc ratio (VCDR) (β=24.42, p<0.001) and disc size (β=-60.75, p=0.001). For every 1 year older in age, the LCD-PPS was deeper on average by 1.95 µm in Chinese subjects (p=0.01) but there was no association in Indians subjects (p=0.851)., Conclusions: The LCD was influenced by age, gender, race, Axl, RNFL, ChT, VCDR and disc size. This normative LCD database may facilitate a more accurate assessment of ONH cupping using OCT in Asian populations., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: To investigate the association between the laminar dot sign (LDS) and the deep optic nerve head (ONH) structure in eyes with primary-open-angle glaucoma (POAG)., Methods: Eighty-four eyes of 84 patients with POAG were prospectively included. All of the patients underwent stereo optic disc photography (SDP), red-free retinal nerve fibre layer (RNFL) photography, SS-OCT, and standard automated perimetry. By evaluating the SDP, patients were classified into laminar dot sign (LDS) and non-LDS groups. The deep structure of the ONH including the anterior prelaminar depth (APLD) and prelaminar tissue thickness (PTT) were quantitated using SS-OCT. Progression was assessed by structural or functional deterioration during the average 4.3 ± 1.2 years of follow-up., Results: The LDS group had deeper APLD (405.47 ± 107.55 vs. 302.45 ± 149.51, P < 0.001) and thinner PTT (74.34 ± 24.46 vs. 137.29 ± 40.07, P = 0.001) relative to the non-LDS group. By multivariate analysis, thin PTT was significantly associated with the presence of LDS (odds ratio = 0.939, P < 0.001). Structural progression was detected in 45 eyes (84.9%) in the LDS group and 8 eyes (25.8%) in the non-LDS group. Functional progression was demonstrated in 29 eyes (34.5%) in the LDS group and 6 eyes (19.4%) in the non-LDS group. The eyes with LDS had a significantly higher risk of glaucoma progression (χ 2  = 5.00, degree of freedom = 1, P = 0.033)., Conclusions: In eyes with POAG, the presence of LDS was associated with thinner prelaminar tissue and faster disease progression.

Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 ± 0.133 to 0.142 ± 0.102, 0.449 ± 0.116 to 0.0904 ± 0.0769, 0.381 ± 0.100 to 0.0590 ± 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images., Competing Interests: Haris Cheong: None, Sripad Krishna Devalla: None, Thanadet Chuangsuwanich: None, Tin A. Tun: None, Xiaofei Wang: None, Tin Aung: None, Leopold Schmetterer: None, Martin L. Buist: None, Craig Boote: None, Alexandre H. Thiery: Abyss Processing (Co-Founder), Michael J. A. Girard: Abyss Processing (Co-Founder), (© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Recently proposed deep learning (DL) algorithms for the segmentation of optical coherence tomography (OCT) images to quantify the morphological changes to the optic nerve head (ONH) tissues during glaucoma have limited clinical adoption due to their device specific nature and the difficulty in preparing manual segmentations (training data). We propose a DL-based 3D segmentation framework that is easily translatable across OCT devices in a label-free manner (i.e. without the need to manually re-segment data for each device). Specifically, we developed 2 sets of DL networks: the 'enhancer' (enhance OCT image quality and harmonize image characteristics from 3 devices) and the 'ONH-Net' (3D segmentation of 6 ONH tissues). We found that only when the 'enhancer' was used to preprocess the OCT images, the 'ONH-Net' trained on any of the 3 devices successfully segmented ONH tissues from the other two unseen devices with high performance (Dice coefficients > 0.92). We demonstrate that is possible to automatically segment OCT images from new devices without ever needing manual segmentation data from them., Competing Interests: Dr. Michaël J. A. Girard and Dr. Alexandre H. Thiéry are co-founders of Abyss Processing., (© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Purpose: To measure the magnitude and direction of anterior scleral canal opening (ASCO) offset relative to the Bruch membrane opening (BMO) (ASCO/BMO offset) to characterize neural canal obliqueness and minimum cross-sectional area (NCMCA) in 69 highly myopic and 138 healthy, age-matched, control eyes., Design: Cross-sectional study., Methods: Using optical coherence tomography (OCT) scans of the optic nerve head (ONH), BMO and ASCO were manually segmented and their centroids and size and shape were calculated. ASCO/BMO offset magnitude and direction were measured after projecting the ASCO/BMO centroid vector onto the BMO plane. Neural canal axis obliqueness was defined as the angle between the ASCO/BMO centroid vector and the vector perpendicular to the BMO plane. NCMCA was defined by projecting BMO and ASCO points onto a plane perpendicular to the neural canal axis and measuring their overlapping area., Results: ASCO/BMO offset magnitude was greater (highly myopic eyes 264.3 ± 131.1 μm; healthy control subjects 89.0 ± 55.8 μm, P < .001, t test) and ASCO centroid was most frequently nasal relative to BMO centroid (94.2% of eyes) in the highly myopic eyes. BMO and ASCO areas were significantly larger (P < .001, t test), NCMCA was significantly smaller (P < .001), and all 3 were significantly more elliptical (P ≤ .001) in myopic eyes. Neural canal obliqueness was greater in myopic (65.17° ± 14.03°) compared with control eyes (40.91° ± 16.22°; P < .001, t test)., Conclusions: Our data suggest that increased temporal displacement of BMO relative to the ASCO, increased BMO and ASCO area, decreased NCMCA, and increased neural canal obliqueness are characteristic components of ONH morphology in highly myopic eyes., (Copyright © 2020 Elsevier Inc. All rights reserved.)

In humans, the longitudinal characterisation of early optic nerve head (ONH) damage in ocular hypertension (OHT) is difficult as patients with glaucoma usually have structural ONH damage at the time of diagnosis. Previous studies assessed glaucomatous ONH cupping by measuring the anterior lamina cribrosa depth (LCD) and minimal rim width (MRW) using optical coherence tomography (OCT). In this study, we induced OHT by repeated intracameral microbead injections in 16 cynomolgus primates (10 unilateral; 6 bilateral) and assessed the structural changes of the ONH longitudinally to observe early changes. Elevated intraocular pressure (IOP) in OHT eyes was maintained for 7 months and serial OCT measurements were performed during this period. The mean IOP was significantly elevated in OHT eyes when compared to baseline and compared to the control eyes. Thinner MRW and deeper LCD values from baseline were observed in OHT eyes with the greatest changes seen between month 1 and month 2 of OHT. Both the mean and maximum IOP values were significant predictors of MRW and LCD changes, although the maximum IOP was a slightly better predictor. We believe that this model could be useful to study IOP-induced early ONH structural damage which is important for understanding glaucoma pathogenesis.

Purpose: To compare lamina cribrosa (LC) morphology between eyes with nonarteritic anterior ischemic optic neuropathy (NAION) and eyes with normal tension glaucoma (NTG) in the Korean population., Methods: This retrospective study included 48 eyes with NAION, 48 eyes with NTG, and 48 healthy control eyes matched by age, intraocular pressure, axial length, and optic disc area. Eyes with NAION and NTG were also matched by retinal nerve fiber layer (RNFL) thickness in the affected sector. Optic nerve heads were scanned using enhanced depth imaging spectral-domain optical coherence tomography. LC depth (LCD) and the LC curvature index (LCCI) were measured at seven locations spaced equidistantly across the vertical optic disc diameter. LCD and the LCCI were compared in the three groups., Results: RNFL thicknesses of the matched affected sectors did not differ between the NAION and NTG groups (P = 0.347). LCD and the LCCI were significantly larger in the NTG group than in the NAION and healthy control groups at all seven planes (P < 0.001 each), but were comparable in the NAION and healthy control groups. The LCCI was larger in the affected than in the unaffected sector of NTG eyes (P = 0.010) but did not differ in NAION eyes (P = 1.000). LCD did not differ between affected and unaffected sectors in either NAION (P = 0.600) or NTG (P = 0.098) eyes., Conclusions: LC morphology differed in eyes with NAION and NTG, despite a similar degree of RNFL damage. Evaluation of LC morphology may help to understand the distinctive pathophysiology of NAION and to differentiate NAION from NTG eyes.

Purpose: The purpose of this study was to investigate changes in lamina cribrosa (LC) depth after trabeculectomy in myopic eyes using enhanced depth imaging (EDI) spectral-domain optical coherence tomography (SD-OCT)., Methods: Serial horizontal B-scans of the optic nerve head of 41 myopic eyes with primary open-angle glaucoma (POAG) were obtained before and 3 months after trabeculectomy using EDI SD-OCT. LC depth, defined as the distance from the opening plane of Bruch's membrane to the level of the anterior LC surface, was measured before and 3 months after trabeculectomy at 7 locations spaced equidistantly across the vertical optic disc diameter on B-scan images. The mean of the measurements at these seven planes was defined as the average LC depth. Factors associated with changes in LC depth were identified by linear regression., Results: Intraocular pressure (IOP) decreased from 26.3 ± 9.3 millimeters of mercury (mm Hg) preoperatively to 10.6 ± 3.5 mm Hg 3 months after trabeculectomy. LC depth was significantly lower 3 months after trabeculectomy than preoperatively (P < 0.001, all planes). The magnitude of LC depth reduction was significantly associated with younger age, higher preoperative LC depth, and greater magnitude of IOP reduction (all P ≤ 0.016)., Conclusions: LC depth reduction was observed after trabeculectomy in myopic eyes. The degree of LC depth reduction was not related to the degree of myopia.

Focal lamina cribrosa (LC) defects have been found to play an important role in the development and progression of glaucomatous optic neuropathy. However, the mechanism of generation of focal LC defects is largely unknown. This cross-sectional study was performed to investigate LC curvature and the frequency of parapapillary choroidal microvascular dropout (MvD) in glaucomatous eyes with focal LC defects. This study was conducted by a retrospective review of patients with primary open-angle glaucoma (POAG) included in an ongoing prospective study being performed at the Seoul National University Bundang Hospital (Investigating Glaucoma Progression Study). A total of 118 eyes of 118 patients with POAG, 59 with and 59 without focal LC defects, with eyes matched by age, axial length, and severity of visual field (VF) damage were included. Posterior LC bowing was assessed by calculating LC curvature index (LCCI), as the inflection of a curve representing a section of the LC, on the optic nerve head images obtained by enhanced-depth-imaging (EDI) spectral-domain optical coherence tomography (OCT). MvD was detected by OCT angiography. LCCI and MvD frequency were compared between eyes with and without focal LC defects. Mean LCCI was significantly smaller than in eyes with than without focal LC defects (9.75 ± 1.29 vs. 11.25 ± 1.39, P < 0.001). MvD was significantly more frequent in eyes with than without focal LC defects (84.7% vs. 49.2%, P < 0.001). MvD in eyes with focal LC defects showed a strong topographic correlation with the focal LC defects. These findings suggest that focal LC defects may primarily result from vascular factors rather than from mechanical strain.

Purpose: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH)., Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device for both eyes of 13 subjects. A custom generative adversarial network (named DeshadowGAN) was designed and trained with 2328 B-scans in order to remove blood vessel shadows in unseen B-scans. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast-a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow). This was computed in the retinal nerve fiber layer (RNFL), the inner plexiform layer (IPL), the photoreceptor (PR) layer, and the retinal pigment epithelium (RPE) layer. The performance of DeshadowGAN was also compared with that of compensation, the standard for shadow removal., Results: DeshadowGAN decreased the intralayer contrast in all tissue layers. On average, the intralayer contrast decreased by 33.7 ± 6.81%, 28.8 ± 10.4%, 35.9 ± 13.0%, and 43.0 ± 19.5% for the RNFL, IPL, PR layer, and RPE layer, respectively, indicating successful shadow removal across all depths. Output images were also free from artifacts commonly observed with compensation., Conclusions: DeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a preprocessing step to improve the performance of a wide range of algorithms including those currently being used for OCT segmentation, denoising, and classification., Translational Relevance: DeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies., Competing Interests: Disclosure: H. Cheong, None; S.K. Devalla, None; T.H. Pham, None; L. Zhang, None; T.A. Tun, None; X. Wang, None; S. Perera, None; L. Schmetterer, None; T. Aung, None; C. Boote, None; A. Thiery, Abyss Processing (I); M.J.A. Girard, Abyss Processing (I), (Copyright 2020 The Authors.)

Purpose: We developed a combined biomechanical and hemodynamic model of the human eye to estimate blood flow and oxygen concentration within the lamina cribrosa (LC) and rank the factors that influence LC oxygen concentration., Methods: We generated 5000 finite-element eye models with detailed microcapillary networks of the LC and computed the oxygen concentration of the lamina retinal ganglion cell axons. For each model, we varied the intraocular pressure (IOP) from 10 mm Hg to 55 mm Hg in 5-mm Hg increments, the cerebrospinal fluid pressure (13 ± 2 mm Hg), cup depth (0.2 ± 0.1 mm), scleral stiffness (±20% of the mean values), LC stiffness (0.41 ± 0.2 MPa), LC radius (1.2 ± 0.12 mm), average LC pore size (5400 ± 2400 µm2), the microcapillary arrangement (radial, isotropic, or circumferential), and perfusion pressure (50 ± 9 mm Hg). Blood flow was assumed to originate from the LC periphery and drain via the central retinal vein. Finally, we performed linear regressions to rank the influence of each factor on the LC tissue oxygen concentration., Results: LC radius and perfusion pressure were the most important factors in influencing the oxygen concentration of the LC. IOP was another important parameter, and eyes with higher IOP had higher compressive strain and slightly lower oxygen concentration. In general, superior-inferior regions of the LC had significantly lower oxygen concentration than the nasal-temporal regions, resulting in an hourglass pattern of oxygen deficiency., Conclusions: To the best of our knowledge, this study is the first to implement a comprehensive hemodynamical model of the eye that accounts for the biomechanical forces and morphological parameters of the LC. The results provide further insight into the possible relationship of biomechanical and vascular pathways leading to ischemia-induced optic neuropathy.

Purpose: To characterize differences in the lamina cribrosa (LC) morphology between healthy, ocular hypertension (OHT), and naive normal-tension glaucoma (NTG) eyes., Methods: Each group consisted of 80 eyes of 80 participants who were matched for age, sex, and axial length. The participants underwent enhanced-depth-imaging volume scanning of the optic nerve head using spectral-domain optical coherence tomography. The lamina cribrosa curvature index (LCCI) and lamina cribrosa thickness (LCT) were measured in horizontal B-scan images spaced equidistantly across the vertical diameter of the optic disc., Results: The LCCIs in all seven planes were smaller in both OHT and healthy eyes than in NTG eyes (all P < 0.001), and did not differ significantly between the OHT and healthy eyes. The LCTs in all three planes were greatest in OHT eyes followed by healthy and then NTG eyes (all P < 0.001). Overall, the larger LCCI was associated with smaller LCT (P < 0.001)., Conclusions: The LC was thin and steeply curved in NTG eyes than in healthy and OHT eyes. In OHT eyes, the LC was thick, and its curvature was comparable to healthy eyes. Longitudinal studies are required to examine whether the straight and thickened LCs in OHT eyes precede the onset of OHT or are a protective response to elevated intraocular pressure.

Purpose: To study the effect of changing heart rate on the ocular pulse and the dynamic biomechanical behavior of the optic nerve head (ONH) using a comprehensive mathematical model., Methods: In a finite element model of a healthy eye, a biphasic choroid consisted of a solid phase with connective tissues and a fluid phase with blood, and the lamina cribrosa (LC) was viscoelastic as characterized by a stress-relaxation test. We applied arterial pressures at 18 ocular entry sites (posterior ciliary arteries), and venous pressures at four exit sites (vortex veins). In the model, the heart rate was varied from 60 to 120 bpm (increment: 20 bpm). We assessed the ocular pulse amplitude (OPA), pulse volume, ONH deformations, and the dynamic modulus of the LC at different heart rates., Results: With an increasing heart rate, the OPA decreased by 0.04 mm Hg for every 10 bpm increase in heart rate. The ocular pulse volume decreased linearly by 0.13 µL for every 10 bpm increase in heart rate. The storage modulus and the loss modulus of the LC increased by 0.014 and 0.04 MPa, respectively, for every 10 bpm increase in heart rate., Conclusions: In our model, the OPA, pulse volume, and ONH deformations decreased with an increasing heart rate, whereas the LC became stiffer. The effects of blood pressure/heart rate changes on ONH stiffening may be of interest for glaucoma pathology.

Purpose: To develop and use a custom virtual fields method (VFM) to assess the biomechanical properties of human prelamina and lamina cribrosa (LC) in vivo., Methods: Clinical data of 20 healthy, 20 ocular hypertensive (OHT), 20 primary open-angle glaucoma, and 16 primary angle-closure glaucoma eyes were analyzed. For each eye, the intraocular pressure (IOP) and optical coherence tomography (OCT) images of the optic nerve head (ONH) were acquired at the normal state and after acute IOP elevation. The IOP-induced deformation of the ONH was obtained from the OCT volumes using a three-dimensional tracking algorithm and fed into the VFM to extract the biomechanical properties of the prelamina and the LC in vivo. Statistical measurements and P values from the Mann-Whitney-Wilcoxon tests were reported., Results: The average shear moduli of the prelamina and the LC were 64.2 ± 36.1 kPa and 73.1 ± 46.9 kPa, respectively. The shear moduli of the prelamina of healthy subjects were significantly lower than those of the OHT subjects. Comparisons between healthy and glaucoma subjects could not be made robustly due to a small sample size., Conclusions: We have developed a methodology to assess the biomechanical properties of human ONH tissues in vivo and provide preliminary comparisons in healthy and OHT subjects. Our proposed methodology may be of interest for glaucoma management.

Background/aims: To investigate the relationship between the lamina cribrosa (LC) curvature and the microvasculature within the LC in treatment-naïve eyes with normal-tension glaucoma (NTG) and in healthy eyes., Methods: Forty-one eyes with treatment-naïve NTG and 41 age and sex-matched healthy control eyes were included. The optic nerve head (ONH) area was scanned using spectral-domain optical coherence tomography (OCT) to examine the LC curvature quantified as the LC curvature index (LCCI). OCT angiography of the ONH area was performed to determine the LC vessel density (LCVD) in the en face images obtained from the layer segmented at the level of the LC. The LCVD was calculated as the percentage area occupied by vessels within the measured region., Results: The LCCI was larger (9.53±1.33 vs 6.55±1.02, p<0.001) and LCVD was smaller (28.0%±6.1% vs 35.2±6.3%, p<0.001) in NTG eyes than in healthy eyes. There were overall significant associations of a smaller retinal nerve fibre layer (RNFL) thickness (p<0.001), a smaller visual field mean deviation (MD) (p=0.003) and a larger LCCI (p≤0.004) with a smaller LCVD. In NTG group, the LCVD was positively associated with the RNFL thickness (p=0.012) and visual field MD (p=0.023), and negatively associated with the axial length (p≤0.013) and LCCI (p≤0.007). In healthy group, a smaller RNFL thickness (p=0.023) was associated with a smaller LCVD., Conclusion: A larger LCCI was significantly associated with a smaller LCVD in treatment-naïve NTG eyes but not in healthy eyes, indicating that mechanical strain potentially influences the perfusion within the LC in eyes with NTG., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Glaucoma is a result of irreversible damage to the retinal ganglion cells. While an early intervention could minimise the risk of vision loss in glaucoma, its asymptomatic nature makes it difficult to diagnose until a late stage. The diagnosis of glaucoma is a complicated and expensive effort that is heavily dependent on the experience and expertise of a clinician. The application of artificial intelligence (AI) algorithms in ophthalmology has improved our understanding of many retinal, macular, choroidal and corneal pathologies. With the advent of deep learning, a number of tools for the classification, segmentation and enhancement of ocular images have been developed. Over the years, several AI techniques have been proposed to help detect glaucoma by analysis of functional and/or structural evaluations of the eye. Moreover, the use of AI has also been explored to improve the reliability of ascribing disease prognosis. This review summarises the role of AI in the diagnosis and prognosis of glaucoma, discusses the advantages and challenges of using AI systems in clinics and predicts likely areas of future progress., Competing Interests: Competing interests: MJAG and AHT are co-founders of Abyss Processing., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)