Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus' inferior whorl in patients with neuropathic corneal pain and dry eye disease.

Purpose: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal microscopy (IVCM) images.

Methods: Subbasal nerve plexus (SNP) images of the inferior whorl of patients with DED (n = 49, 77 eyes), NCP (n = 14, 24 eyes), and controls (n = 41, 59 eyes) were taken with IVCM and further analyzed using an open-source artificial intelligence (AI)-based algorithm previously developed by our group. This algorithm automatically segments nerves, immune cells, and neuromas in the SNP.

Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus With Accuracy on Par With Human Segmentation.

Purpose: The aim of this study was to perform automated segmentation of corneal nerves and other structures in corneal confocal microscopy (CCM) images of the subbasal nerve plexus (SNP) in eyes with ocular surface diseases (OSDs).

Methods: A deep learning-based 2-stage algorithm was designed to perform segmentation of SNP features. In the first stage, to address applanation artifacts, a generative adversarial network-enabled deep network was constructed to identify 3 neighboring corneal layers on each CCM image: epithelium, SNP, and stroma.

Test of a Retinal Nerve Fiber Bundle Trajectory Model Using Eyes With Glaucomatous Optic Neuropathy.

Purpose: To test a model of retinal nerve fiber bundle trajectories that predicts the arcuate-shaped patterns seen on optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability/deviation maps (p-maps) in glaucomatous eyes.

Methods: Thirty-one glaucomatous eyes from a database of 250 eyes had clear arcuate-shaped patterns on RNFL p-maps derived from an OCT cube scan. The borders of the arcuate patterns were extracted from the RNFL p-maps.

Distinguishing Healthy From Glaucomatous Eyes With Optical Coherence Tomography Global Circumpapillary Retinal Nerve Fiber Thickness in the Bottom 5th Percentile.

Prcis: Two novel, quantitative metrics, and 1 traditional metric were able to distinguish between many, but not all healthy and glaucomatous eyes in the bottom 5th percentile of global circumpapillary retinal nerve fiber layer (cpRNFL) thickness.

Purpose: To test the hypothesis that objective optical coherence tomography measures can distinguish between a healthy control with global cpRNFL thickness within the lower 5% of normal and a glaucoma patient with an equivalent cpRNFL thickness.

Patients And Methods: A total of 37 healthy eyes from over 700 normative eyes fell within the bottom 5th percentile in global cpRNFL thickness.

The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage.

Purpose: The purpose of this study was to improve the diagnostic ability of the optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability (p-) map by understanding the frequency and pattern of artifacts seen on the p-maps of healthy control (HC) eyes resembling glaucomatous damage.

Methods: RNFL p-maps were generated from wide-field OCT cube scans of 2 groups of HC eyes, 200 from a commercial normative group (HC-norm) and 54 from a prospective study group, as well as from 62 patient eyes, which included 32 with early glaucoma (EG). These 32 EG eyes had 24-2 mean deviation (MD) better than -6 dB and perimetric glaucoma as defined by 24-2 and 10-2 criteria.

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans.

Purpose: To develop and evaluate methods to improve the generalizability of convolutional neural networks (CNNs) trained to detect glaucoma from optical coherence tomography retinal nerve fiber layer probability maps, as well as optical coherence tomography circumpapillary disc (circle) b-scans, and to explore impact of reference standard (RS) on CNN accuracy.

Methods: CNNs previously optimized for glaucoma detection from retinal nerve fiber layer probability maps, and newly developed CNNs adapted for glaucoma detection from optical coherence tomography b-scans, were evaluated on an unseen dataset (i.e.

Detecting Progression in Advanced Glaucoma: Are Optical Coherence Tomography Global Metrics Viable Measures?

Significance: Optical coherence tomography (OCT) summary measures have been suggested as a way to detect progression in eyes with advanced glaucoma. Here, we show that these measures have serious flaws largely due to segmentation errors. However, inspection of the images and thickness maps can be clinically useful.

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression.

Purpose: To assess the effects of local defects, segmentation errors, and improper image alignment on the performance of the commonly used optical coherence tomography (OCT) measure of progression, that is the change in global (average) circumpapillary retinal nerve fiber layer (cpRNFL) thickness (ΔG).

Methods: One hundred fifty eyes suspected of, or with, early glaucoma had OCT circle and cube scans obtained using eye tracking on two occasions at least 1 year apart. Statistical progression was defined by fixed values of ΔG (3-8 um) and quantile regression.

Optical Coherence Tomography Can Be Used to Assess Glaucomatous Optic Nerve Damage in Most Eyes With High Myopia.

Precis: It is generally assumed that optical coherence tomography (OCT) cannot be used to diagnose glaucomatous optic neuropathy (GON) in high myopes. However, this study presents evidence that there is sufficient information in OCT scans to allow for accurate diagnosis of GON in most eyes with high myopia.

Purpose: The purpose of this study was to test the hypothesis that glaucomatous damage can be accurately diagnosed in most high myopes via an assessment of the OCT results.

A Topographic Comparison of OCT Minimum Rim Width (BMO-MRW) and Circumpapillary Retinal Nerve Fiber Layer (cRNFL) Thickness Measures in Eyes With or Suspected Glaucoma.

Unlabelled: PRéCIS:: Bruch's membrane opening-minimum rim width (BMO-MRW) and circumpapillary retinal nerve fiber layer (cRNFL) thickness measures may be improved by comparing probability levels and accounting for blood vessel locations.

Purpose: To understand the differences between 2 optical coherence tomography measures of glaucomatous damage: the BMO-MRW and cRNFL thickness.

Materials And Methods: Optical coherence tomography circle scans were obtained for an early glaucoma group (EG) of 88 eyes (88 patients) with 24-2 mean deviation better than -6.

Improving the Detection of Glaucoma and Its Progression: A Topographical Approach.

Glaucoma is typically defined as a progressive optic neuropathy characterized by a specific (arcuate) pattern of visual field (VF) and anatomic changes. Therefore, we should be comparing arcuate patterns of damage seen on VFs with those seen on optical coherence tomography (OCT) maps. Instead, clinicians often use summary metrics such as VF pattern standard deviation, OCT retinal nerve fiber (RNF) global thickness, etc.

Qualitative evaluation of neuroretinal rim and retinal nerve fibre layer on optical coherence tomography to detect glaucomatous damage.

Purpose: To understand the added value of Bruch's membrane opening-minimum rim width (BMO-MRW) measurements to conventional circumpapillary retinal nerve fibre layer (cpRNFL) thickness measurements on optical coherence tomography (OCT) imaging for discriminating between perimetric glaucoma and healthy eyes, evaluated through a qualitative evaluation.

Methods: 384 healthy eyes and 188 glaucoma eyes were evaluated, and glaucoma eyes were categorised as perimetric (n=107) based on a history of ≥3 consecutive abnormal 24-2 visual field tests or suspected glaucoma if they did not (n=81). OCT-derived BMO-MRW and cpRNFL reports were qualitatively evaluated by two experienced graders in isolation at first, and then by using both reports combined.