OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) has significantly advanced the study and diagnosis of eye diseases. However, current clinical OCTA systems and software tools lack comprehensive quantitative analysis capabilities, limiting their full clinical utility. This paper introduces the OCTA Retinal Vessel Analyzer (OCTA-ReVA), a versatile open-source platform featuring a user-friendly graphical interface designed for the automated extraction and quantitative analysis of OCTA features.

Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.

Purpose: This study aimed to investigate the impact of distinctive capillary-large vessel (CLV) analysis in optical coherence tomography angiography (OCTA) on the classification performance of diabetic retinopathy (DR).

Methods: This multicenter study analyzed 212 OCTA images from 146 patients, including 28 controls, 36 diabetic patients without DR (NoDR), 31 with mild non-proliferative DR (NPDR), 28 with moderate NPDR, and 23 with severe NPDR. Quantitative features were derived from the whole image as well as the parafovea and perifovea regions.

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.

Significance: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the efficacy and safety of utilizing longer wavelengths, such as red or green for enhanced depth information and improved diagnostic capabilities.

Aim: This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP.

Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.

This study investigates the impact of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) on machine learning classification of diabetic retinopathy (DR). Leveraging deep learning for arterial-venous area (AVA) segmentation, six quantitative features, including perfusion intensity density (PID), blood vessel density (BVD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI) features, were derived from OCTA images before and after AV differentiation. A support vector machine (SVM) classifier was utilized to assess both binary and multiclass classifications of control, diabetic patients without DR (NoDR), mild DR, moderate DR, and severe DR groups.

Colour fusion effect on deep learning classification of uveal melanoma.

Background: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance.

Methods: A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal naevus.

Multispectral Fundus Photography of Choroidal Nevi With Trans-Palpebral Illumination.

Purpose: The purpose of this study was to investigate the spectral characteristics of choroidal nevi and assess the feasibility of quantifying the basal diameter of choroidal nevi using multispectral fundus images captured with trans-palpebral illumination.

Methods: The study used a widefield fundus camera with multispectral (625 nm, 780 nm, 850 nm, and 970 nm) trans-palpebral illumination to examine eight subjects diagnosed with choroidal nevi. Geometric features of nevi, including border clarity, overlying drusen, and lesion basal diameter, were characterized.

Adaptive vessel tracing and segmentation in OCT enables the robust detection of wall-to-lumen ratio abnormalities in 5xFAD mice.

The wall-to-lumen ratio (WLR) of retinal blood vessels promises a sensitive marker for the physiological assessment of eye conditions. However, measurement of vessel wall thickness and lumen diameter is still technically challenging, hindering the wide application of WLR in research and clinical settings. In this study, we demonstrate the feasibility of using optical coherence tomography (OCT) as one practical method for quantification of WLR in the retina.

Multispectral Fundus Photography of Choroidal Nevi with Trans-Palpebral Illumination.

Purpose: To investigate the spectral characteristics of choroidal nevi and assess the feasibility of quantifying the basal diameter of choroidal nevi using multispectral fundus images captured with trans-palpebral illumination.

Methods: The study employed a widefield fundus camera with multispectral (625 nm, 780 nm, 850 nm, and 970 nm) trans-palpebral illumination. Geometric features of choroidal nevi, including border clarity, overlying drusen, and lesion basal diameter, were characterized.

Simulation of visual impairment in persons with normal vision for scientific research.

Simulation of visual impairment in healthy eyes has multiple applications in students' training, research and product development. However, due to the absence of an existing standard protocol, the method of simulation was left to the discretion of the researcher. This review aimed to outline the various methods of simulating visual impairment and categorising them.

Central contrast sensitivity perimetry discriminates between glaucomatous and non-glaucomatous eyes.

Background: Glaucoma is an optic neuropathy which causes irreversible vision loss. Standard perimetry, which is essential for glaucoma diagnosis, can only detect glaucomatous visual filed loss when considerable structural damage has occurred. Contrast sensitivity is one of the visual function tests that is reduced in eyes with glaucoma.

Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.

The purpose of this study is to evaluate layer fusion options for deep learning classification of optical coherence tomography (OCT) angiography (OCTA) images. A convolutional neural network (CNN) end-to-end classifier was utilized to classify OCTA images from healthy control subjects and diabetic patients with no retinopathy (NoDR) and non-proliferative diabetic retinopathy (NPDR). For each eye, three en-face OCTA images were acquired from the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CC) layers.

Deep learning for artery-vein classification in optical coherence tomography angiography.

Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal imaging.

The effect of filters and varying illumination on contrast sensitivity in eyes with moderate to severe visual impairment.

Purpose: To investigate the effect of filters and illumination on contrast sensitivity in persons with cataract, pseudophakia, maculopathy and glaucoma to provide a guide for eye care providers in low vision rehabilitation.

Materials And Methods: A within-subjects experimental design with a counter-balanced presentation technique was employed in this study. The contrast sensitivity of eyes with cataract, pseudophakia, maculopathy and glaucoma was measured with filters (no filter, yellow, pink and orange) combined with increasing illumination levels (100 lx, 300 lx, 700 lx and 1000 lx) using the SpotChecks™ contrast sensitivity chart.

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography.

Background: Differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity.

Normalized Blood Flow Index in Optical Coherence Tomography Angiography Provides a Sensitive Biomarker of Early Diabetic Retinopathy.

Purpose: To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR).

Methods: Optical coherence tomography angiography (OCTA) images of healthy controls, diabetic patients without DR (NoDR), and patients with mild nonproliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm × 6 mm area.