Deep Learning Model for Automated Classification of Macular Neovascularization Subtypes in AMD.

Purpose: To develop a deep learning algorithm capable of accurately classifying macular neovascularization (MNV) subtypes in patients with treatment-naïve exudative neovascular age-related macular degeneration (AMD) using structural optical coherence tomography (OCT) images.

Methods: In this retrospective cohort study, a total of 193 eyes with treatment-naïve neovascular AMD were included. Each case was classified into MNV subtypes (type 1, 2, or 3) based on structural OCT features.

Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.

Purpose: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR).

Methods: A retrospective cohort of 249 patients (498 eyes) diagnosed with NPDR was analysed. Structural OCT scans were obtained using the Heidelberg Spectralis HRA + OCT device.

Topographical Quantification of Retinal Fluid in Type 3 MNV and Associations With Short-Term Visual Outcomes.

Purpose: This study aims to quantify the volume of intraretinal fluid (IRF), subretinal fluid (SRF), and subretinal pigment epithelium (sub-RPE) fluid in treatment-naïve Type 3 macular neovascularization (MNV) eyes with age-related macular degeneration (AMD) and to investigate the correlation of these fluid volumes with visual acuity (VA) outcomes at baseline and following antivascular endothelial growth factor (VEGF) treatment.

Design: Retrospective, clinical cohort study.

Methods: In this study, we analyzed patients diagnosed with exudative AMD and treatment-naïve Type 3 MNV undergoing a loading dose of anti-VEGF therapy.

Remission of corneal and peripheral neuropathy after bariatric surgery in people with diabetes.

Purpose: Diabetic peripheral neuropathy can be detected using non-invasive in vivo confocal microscopy of the cornea (IVCM) and such abnormalities may precede the development of clinical neuropathy. The current study aimed to assess any progression or remission of corneal and peripheral neuropathy in patients with type 2 diabetes undergoing bariatric surgery.

Methods: People with known type 2 diabetes for at least five years and listed for bariatric surgery were recruited.

Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

Purpose: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).

Methods: In this IRB-approved study, optical coherence tomography (OCT) data from 50 patients (50 eyes) with exudative nAMD were retrospectively analysed. Two models, A1 and A2, were created based on gradings from two masked readers, R1 and R2.

QUANTITATIVE ASSESSMENT OF AUTOMATED OPTICAL COHERENCE TOMOGRAPHY IMAGE ANALYSIS USING A HOME-BASED DEVICE FOR SELF-MONITORING NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

Purpose: To evaluate a prototype home optical coherence tomography device and automated analysis software for detection and quantification of retinal fluid relative to manual human grading in a cohort of patients with neovascular age-related macular degeneration.

Methods: Patients undergoing anti-vascular endothelial growth factor therapy were enrolled in this prospective observational study. In 136 optical coherence tomography scans from 70 patients using the prototype home optical coherence tomography device, fluid segmentation was performed using automated analysis software and compared with manual gradings across all retinal fluid types using receiver-operating characteristic curves.

Corneal Nerve Changes Observed by In Vivo Confocal Microscopy in Patients Receiving Oxaliplatin for Colorectal Cancer: The COCO Study.

An objective method of early identification of people at risk of chemotherapy-induced peripheral neuropathy is needed to minimize long-term toxicity and maximize dose intensity. The aims of the study were to observe corneal nerve microstructure and corneal sensitivity changes and peripheral neuropathy in patients receiving oxaliplatin, and to determine its association with corneal parameters at different stages of treatment and assess utility as non-invasive markers to detect and monitor peripheral neuropathy. Twenty-three patients scheduled to receive oxaliplatin chemotherapy with intravenous 5-FU for gastro-intestinal cancer were recruited and followed up with for 12 months.

Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume.

Background And Objective: To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images.

Patients And Methods: This was a proof-of-concept consecutive case series. Patients with an idiopathic full-thickness MH undergoing pars plana vitrectomy surgery with 1 year of follow-up were considered for inclusion.

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.

Purpose: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data.

Methods: In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52.

Combining In Vivo Corneal Confocal Microscopy With Deep Learning-Based Analysis Reveals Sensory Nerve Fiber Loss in Acute Simian Immunodeficiency Virus Infection.

Purpose: To characterize corneal subbasal nerve plexus features of normal and simian immunodeficiency virus (SIV)-infected macaques by combining in vivo corneal confocal microscopy (IVCM) with automated assessments using deep learning-based methods customized for macaques.

Methods: IVCM images were collected from both male and female age-matched rhesus and pigtailed macaques housed at the Johns Hopkins University breeding colony using the Heidelberg HRTIII with Rostock Corneal Module. We also obtained repeat IVCM images of 12 SIV-infected animals including preinfection and 10-day post-SIV infection time points.

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

Purpose: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance.

Methods: There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth.

Deep learning-based analysis of macaque corneal sub-basal nerve fibers in confocal microscopy images.

Background: To develop and validate a deep learning-based approach to the fully-automated analysis of macaque corneal sub-basal nerves using in vivo confocal microscopy (IVCM).

Methods: IVCM was used to collect 108 images from 35 macaques. 58 of the images from 22 macaques were used to evaluate different deep convolutional neural network (CNN) architectures for the automatic analysis of sub-basal nerves relative to manual tracings.

Deep Learning for Prediction of AMD Progression: A Pilot Study.

Purpose: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.

Methods: Seventy-one eyes of 71 patients with confirmed early/intermediate AMD with contralateral wet AMD were imaged with OCT three times over 2 years (baseline, year 1, year 2). These eyes were divided into two groups: eyes that had not converted to wet AMD (n = 40) at year 2 and those that had (n = 31).

Changes in volume of various retinal layers over time in early and intermediate age-related macular degeneration.

Purpose: To evaluate longitudinally volume changes in inner and outer retinal layers in early and intermediate age-related macular degeneration (AMD) compared to healthy control eyes using optical coherence tomography (OCT).

Methods: 71 eyes with AMD and 31 control eyes were imaged at two time points: baseline and after 2 years. Automated OCT layer segmentation was performed using Orion.

Changes in macular layers in the early course of non-arteritic ischaemic optic neuropathy.

Purpose: To characterise the changes of the retinal layers in patients with acute anterior ischaemic optic neuropathy (AION), aiming to identify imaging markers for predicting the residual visual function.

Methods: This was a retrospective review of consecutive patients with unilateral AION from January 2010 to December 2013. We analysed affected eyes at baseline and 1 month later, compared to fellow healthy eyes.

Loss of corneal sensory nerve fibers in SIV-infected macaques: an alternate approach to investigate HIV-induced PNS damage.

Peripheral neuropathy is the most frequent neurological complication of HIV infection, affecting more than one-third of infected patients, including patients treated with antiretroviral therapy. Although emerging noninvasive techniques for corneal nerve assessments are increasingly being used to diagnose and monitor peripheral neuropathies, corneal nerve alterations have not been characterized in HIV. Here, to determine whether SIV infection leads to corneal nerve fiber loss, we immunostained corneas for the nerve fiber marker βIII tubulin.

Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning.

Objective: To determine the effect of clinical and radiologic disease activity on the rate of thinning of the ganglion cell/inner plexiform (GCIP) layer and the retinal nerve fiber layer in patients with multiple sclerosis (MS) using optical coherence tomography (OCT).

Methods: One hundred sixty-four patients with MS and 59 healthy controls underwent spectral-domain OCT scans every 6 months for a mean follow-up period of 21.1 months.

In vivo assessment of retinal neuronal layers in multiple sclerosis with manual and automated optical coherence tomography segmentation techniques.

Macular optical coherence tomography (OCT) segmentation, enabling quantification of retinal axonal and neuronal subpopulations, may help elucidate the neuroretinal pathobiology of multiple sclerosis (MS). This study aimed to determine the agreement, reproducibility, and visual correlations of retinal layer thicknesses measured by different OCT segmentation techniques, on two spectral-domain OCT devices. Macular scans of 52 MS patients and 30 healthy controls from Spectralis OCT and Cirrus HD-OCT were segmented using fully manual (Spectralis), computer-aided manual (Spectralis and Cirrus), and fully automated (Cirrus) segmentation techniques.

Optical coherence tomography segmentation reveals ganglion cell layer pathology after optic neuritis.

Post-mortem ganglion cell dropout has been observed in multiple sclerosis; however, longitudinal in vivo assessment of retinal neuronal layers following acute optic neuritis remains largely unexplored. Peripapillary retinal nerve fibre layer thickness, measured by optical coherence tomography, has been proposed as an outcome measure in studies of neuroprotective agents in multiple sclerosis, yet potential swelling during the acute stages of optic neuritis may confound baseline measurements. The objective of this study was to ascertain whether patients with multiple sclerosis or neuromyelitis optica develop retinal neuronal layer pathology following acute optic neuritis, and to systematically characterize such changes in vivo over time.

Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma.

Purpose: To demonstrate the capability of SD-OCT to measure macular retinal ganglion cell-inner plexiform layer (GCIPL) thickness and to assess its reproducibility in glaucomatous eyes.

Methods: Fifty-one glaucomatous eyes (26 mild, 11 moderate, 14 severe) of 51 patients underwent macular scanning using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) macula 200×200 acquisition protocol. Five scans were obtained on 5 days within 2 months.

Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness.

Background: Post-mortem analyses of multiple sclerosis (MS) eyes demonstrate prominent retinal neuronal ganglion cell layer (GCL) loss, in addition to related axonal retinal nerve fiber layer (RNFL) loss. Despite this, clinical correlations of retinal neuronal layers remain largely unexplored in MS.

Objectives: To determine if MS patients exhibit in vivo retinal neuronal GCL loss, deeper retinal neuronal loss, and investigate correlations between retinal layer thicknesses, MS clinical subtype and validated clinical measures.

Comparison of automated analysis of Cirrus HD OCT spectral-domain optical coherence tomography with stereo photographs of the optic disc.

Objective: To evaluate a new automated analysis of optic disc images obtained by spectral-domain optical coherence tomography (SD OCT). Areas of the optic disc, cup, and neural rim in SD OCT images were compared with these areas from stereoscopic photographs to represent the current traditional optic nerve evaluation. The repeatability of measurements by each method was determined and compared.

Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography.

Optical coherence tomography studies in multiple sclerosis have primarily focused on evaluation of the retinal nerve fibre layer. The aetiology of retinal changes in multiple sclerosis is thought to be secondary to optic nerve demyelination. The objective of this study was to use optical coherence tomography to determine if a subset of patients with multiple sclerosis exhibit primary retinal neuronopathy, in the absence of retrograde degeneration of the retinal nerve fibre layer and to ascertain if such patients may have any distinguishing clinical characteristics.