Ciliary muscle traction during accommodation is able to induce optic nerve head deformation.

Objectives: To use finite element (FE) modeling and in vivo optical coherence tomography (OCT) imaging to explore the effect of ciliary muscle traction on optic nerve head (ONH) deformation during accommodation.

Methods: We developed a FE model to mimic the ciliary muscle traction during accommodation, and varied the stiffness of the sclera, choroid, Bruch's membrane (BM), prelaminar neural tissue and lamina cribrosa (LC) to assess their effects on accommodation-induced ONH strains. To validate the FE model, OCT images of the right eyes' ONHs from 20 subjects (25 ± 1.

3D growth and remodeling theory supports the hypothesis of staphyloma formation from local scleral weakening under normal intraocular pressure.

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.

Iris Morphological and Biomechanical Factors Influencing Angle Closure During Pupil Dilation.

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.

Effect of Eye Globe and Optic Nerve Morphologies on Gaze-Induced Optic Nerve Head Deformations.

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.

OCT Optic Nerve Head Morphology in Myopia IV: Neural Canal Scleral Flange Remodeling in Highly Myopic Eyes.

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.

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.

The Structural Layers of the Porcine Iris Exhibit Inherently Different Biomechanical Properties.

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.

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.

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.

Intravitreal Neuroglobin Mitigates Primate Experimental Glaucomatous Structural Damage in Association with Reduced Optic Nerve Microglial and Complement 3-Astrocyte Activation.

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.

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).

OCT Optic Nerve Head Morphology in Myopia II: Peri-Neural Canal Scleral Bowing and Choroidal Thickness in High Myopia-An American Ophthalmological Society Thesis.

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.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.

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.

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma.

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.

A decision-support tool for funding health innovations at a tertiary academic medical center.

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.

Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis.

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.

Assessing the external validity of machine learning-based detection of 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.

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.

Discriminating Between Papilledema and Optic Disc Drusen Using 3D Structural Analysis of the Optic Nerve Head.

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.

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.

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.

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]).