Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.

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.

AI-based clinical assessment of optic nerve head robustness superseding biomechanical testing.

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.

Adduction induces large optic nerve head deformations in subjects with normal-tension glaucoma.

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.

Three-Dimensional Structural Phenotype of the Optic Nerve Head as a Function of Glaucoma Severity.

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.

How Myopia and Glaucoma Influence the Biomechanical Susceptibility of the Optic Nerve Head.

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.

Differing Associations between Optic Nerve Head Strains and Visual Field Loss in Patients with Normal- and High-Tension Glaucoma.

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.

The three-dimensional structural configuration of the central retinal vessel trunk and branches as a glaucoma biomarker.

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.

OCT-GAN: single step shadow and noise removal from optical coherence tomography images of the human optic nerve head.

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.

Morphometric, Hemodynamic, and Biomechanical Factors Influencing Blood Flow and Oxygen Concentration in the Human Lamina Cribrosa.

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.

Photoacoustic imaging of lamina cribrosa microcapillaries in porcine eyes.

Due to the embedded nature of the lamina cribrosa (LC) microcapillary network, conventional imaging techniques have failed to obtain the high-resolution images needed to assess the perfusion state of the LC. In this study, both optical resolution (OR) and acoustic resolution (AR) photoacoustic microscopy (PAM) techniques were used to obtain static and dynamic information about LC perfusion in ex vivo porcine eyes. The OR-PAM system could resolve a perfused LC microcapillary network with a lateral resolution of 4.2 µm and also provided good depth information (33 µm axial resolution) to visualize through-thickness vascular variations. The AR-PAM system was capable of detecting time-dependent perfusion variations. This study represents the first step towards using an emerging imaging modality (PAM) to study the LC's perfusion, which could be a basis for further investigation of the hemodynamic aspects of glaucomatous optic neuropathy.

Factors Influencing Lamina Cribrosa Microcapillary Hemodynamics and Oxygen Concentrations.

PURPOSE: To identify and rank the lamina cribrosa (LC) morphologic factors that influence LC microcapillary hemodynamics and oxygen concentrations using computational fluid dynamics (CFD). METHODS: We generated 12,000 'artificial' LC microcapillary networks and predicted blood flow velocities and oxygen concentrations within the microcapillaries using CFD. Across models, we varied the average pore size of the LC (5500 ± 2400 µm2), the microcapillary arrangement (radial, isotropic, or circumferential), the LC diameter (1.9 ± 0.3 mm), the inferior-superior curvature (340 ± 116 m-1), and the nasal-temporal curvature (-78 ± 130 m-1). We assumed that blood flow originated from the Circle of Zinn-Haller, fed the LC uniformly at its periphery, and was drained into the central retinal vein. Arterial (50 ± 6 mm Hg) and venous (17.7 ± 6 mm Hg) pressures were applied as boundary conditions and were also varied within our simulations. Finally, we performed linear regression analysis to rank the influence of each factor on LC hemodynamics and oxygen concentrations. RESULTS: The factors influencing LC hemodynamics and oxygen concentrations the most were: LC diameter, arterial pressure, and venous pressure, and to a lesser extent: the microcapillary arrangement (anisotropy) and nasal-temporal curvature. Lamina cribrosa pore size and superior-inferior curvature had almost no impact. Specifically, we found that LCs with a smaller diameter, a radial arrangement of the microcapillaries, an elevated arterial pressure and a decreased venous pressure had higher oxygen concentrations across their networks. CONCLUSION: This study described LC hemodynamics using a computational modeling approach. Our study may provide clinically relevant information for the understanding of ischemia-induced neuronal cell death in optic neuropathies.