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

Computational study on the effects of central retinal blood vessels with asymmetric geometries on optic nerve head biomechanics

Optic nerve head (ONH) biomechanics are associated with glaucoma progression and have received considerable attention. Central retinal vessels (CRVs) oriented asymmetrically in the ONH are the single blood supply source to the retina and are believed to act as mechanically stable elements in the ONH in response to intraocular pressure (IOP). However, these mechanical effects are considered negligible in ONH biomechanical studies and received less attention. This study investigated the effects of CRVs on ONH biomechanics taking into consideration three-dimensional asymmetric CRV geometries. A CRV geometry was constructed based on CRV centerlines extracted from optical coherence tomography ONH images in eight healthy subjects and superimposed in the idealized ONH geometry established in previous studies. Mechanical analyses of the ONH in response to the IOP were conducted in the cases with and without CRVs for comparison. Obtained results demonstrated that the CRVs induced anisotropic ONH deformation, particularly in the lamina cribrosa and the associated upper neural tissues (prelamina) with wide ranges of spatial strain distributions. These results indicated that the CRVs result in anisotropic deformation with local strain concentration, rather than function to mechanically support in response to the IOP as in the conventional thinking in ophthalmology.

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.

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. 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.

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. 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.