Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy

IL-17 in type II diabetes mellitus (T2DM) immunopathogenesis and complications; molecular approaches

Chronic inflammation has long been considered the characteristic feature of type II diabetes mellitus (T2DM) Immunopathogenesis. Pro-inflammatory cytokines are considered the central drivers of the inflammatory cascade leading to β-cell dysfunction and insulin resistance (IR), two major pathologic events contributing to T2DM. Analyzing the cytokine profile of T2DM patients has also introduced interleukin-17 (IL-17) as an upstream regulator of inflammation, regarding its role in inducing the nuclear factor-kappa B (NF-κB) pathway. In diabetic tissues, IL-17 induces the expression of inflammatory cytokines and chemokines. Hence, IL-17 can deteriorate insulin signaling and β-cell function by activating the JNK pathway and inducing infiltration of neutrophils into pancreatic islets, respectively. Additionally, higher levels of IL-17 expression in patients with diabetic complications compared to non-complicated individuals have also proposed a role for IL-17 in T2DM complications. Here, we highlight the role of IL-17 in the Immunopathogenesis of T2DM and corresponding pathways, recent advances in preclinical and clinical studies targeting IL-17 in T2DM, and corresponding challenges and possible solutions.

Automated segmentation of ultra-widefield fluorescein angiography of diabetic retinopathy using deep learning

BACKGROUND/AIMS

Retinal capillary non-perfusion (NP) and neovascularisation (NV) are two of the most important angiographic changes in diabetic retinopathy (DR). This study investigated the feasibility of using deep learning (DL) models to automatically segment NP and NV on ultra-widefield fluorescein angiography (UWFA) images from patients with DR.

METHODS

Retrospective cross-sectional chart review study. In total, 951 UWFA images were collected from patients with severe non-proliferative DR (NPDR) or proliferative DR (PDR). Each image was segmented and labelled for NP, NV, disc, background and outside areas. Using the labelled images, DL models were trained and validated (80%) using convolutional neural networks (CNNs) for automated segmentation and tested (20%) on test sets. Accuracy of each model and each label were assessed.

RESULTS

The best accuracy from CNN models for each label was 0.8208, 0.8338, 0.9801, 0.9253 and 0.9766 for NP, NV, disc, background and outside areas, respectively. The best Intersection over Union for each label was 0.6806, 0.5675, 0.7107, 0.8551 and 0.924 and mean mean boundary F1 score (BF score) was 0.6702, 0.8742, 0.9092, 0.8103 and 0.9006, respectively.

CONCLUSIONS

DL models can detect NV and NP as well as disc and outer margins on UWFA with good performance. This automated segmentation of important UWFA features will aid physicians in DR clinics and in overcoming grader subjectivity.

Artificial intelligence in retinal image analysis: Development, advances, and challenges

Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.

Retinal Ischaemia in Diabetic Retinopathy: Understanding and Overcoming a Therapeutic Challenge

BACKGROUND

Retinal ischaemia is present to a greater or lesser extent in all eyes with diabetic retinopathy (DR). Nonetheless, our understanding of its pathogenic mechanisms, risk factors, as well as other characteristics of retinal ischaemia in DR is very limited. To date, there is no treatment to revascularise ischaemic retina.

METHODS

Review of the literature highlighting the current knowledge on the topic of retinal ischaemia in DR, important observations made, and underlying gaps for which research is needed.

RESULTS

A very scarce number of clinical studies, mostly cross-sectional, have evaluated specifically retinal ischaemia in DR. Interindividual variability on its natural course and consequences, including the development of its major complications, namely diabetic macular ischaemia and proliferative diabetic retinopathy, have not been investigated. The in situ, surrounding, and distance effect of retinal ischaemia on retinal function and structure and its change over time remains also to be elucidated. Treatments to prevent the development of retinal ischaemia and, importantly, to achieve retinal reperfusion once capillary drop out has ensued, are very much needed and remain to be developed.

CONCLUSION

Research into retinal ischaemia in diabetes should be a priority to save sight.

Semi-Supervised Dual Stream Segmentation Network for Fundus Lesion Segmentation

Accurate segmentation of retinal images can assist ophthalmologists to determine the degree of retinopathy and diagnose other systemic diseases. However, the structure of the retina is complex, and different anatomical structures often affect the segmentation of fundus lesions. In this paper, a new segmentation strategy called a dual stream segmentation network embedded into a conditional generative adversarial network is proposed to improve the accuracy of retinal lesion segmentation. First, a dual stream encoder is proposed to utilize the capabilities of two different networks and extract more feature information. Second, a multiple level fuse block is proposed to decode the richer and more effective features from the two different parallel encoders. Third, the proposed network is further trained in a semi-supervised adversarial manner to leverage from labeled images and unlabeled images with high confident pseudo labels, which are selected by the dual stream Bayesian segmentation network. An annotation discriminator is further proposed to reduce the negativity that prediction tends to become increasingly similar to the inaccurate predictions of unlabeled images. The proposed method is cross-validated in 384 clinical fundus fluorescein angiography images and 1040 optical coherence tomography images. Compared to state-of-the-art methods, the proposed method can achieve better segmentation of retinal capillary non-perfusion region and choroidal neovascularization.

Diabetic retinopathy with extensively large area of capillary non-perfusion: characteristics and treatment outcomes

Background

Capillary non-perfusion is an important characteristic for diabetic retinopathy (DR) indicating microvascular damage and ischemia. Data on the description and treatment outcomes of DR with large area of non-perfusion are lacking to date. We aim to describe the characteristics and treatment outcomes in a series of patients with DR who presented extensively large area of capillary non-perfusion (LACNP).

Methods

Fundus fluorescein angiograms from medical charts in patients diagnosed with DR between Jan 2017 and Dec 2019 were retrospectively reviewed. Clinical data in eyes with LACNP including imaging and laboratory findings at the first presentation were analyzed. The LACNP was defined as over 70% area of capillary non-perfusion throughout the whole image retina. The mean follow-up duration was 12.4 ± 16.7 months. Follow-up data including extensive pan-retinal photocoagulation and surgical intervention and treatment outcomes were evaluated.

Results

A total of 43 eyes in 24 patients with LACNP were included, accounting for 3.3% of DR populations in the same period. The overall percentage of non-perfusion area was 79.1 ± 8.1%. All patients received proper control of diabetes and hypertension, and extensive pan-retinal laser photocoagulation. During the follow-up periods, 20 eyes (46.5%) developed severe neovascular complications, of which 15 eyes (34.9%) underwent vitrectomy and/or anti-glaucoma surgeries. Conservative therapies including glycemic control and supplemental laser photocoagulation were conducted in 23 eyes (53.5%) without neovascular complications. In the final follow-up, best corrected visual acuity improved or maintained stable in 19 eyes (44.2%) while deteriorated in 24 eyes (55.8%).

Conclusions

The presence of LACNP is the hallmark of advanced DR and often indicates a poor visual outcome, although aggressive treatments may slow DR progression and maintain central vision for some time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-022-02508-6.

Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method's performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.

Evans blue staining to detect deep blood vessels in peripheral retina for observing retinal pathology in early-stage diabetic rats

AIM

To observe and compare the statistical significance of superficial and deep vascular leakage in the pathological changes of the diabetic rats retina after the Evans blue (EB) perfusion, and utilize the modified whole-retina spreading method to make the slides while protecting the periphery of the retina.

METHODS

The Sprague-Dawley (SD) rats were randomly divided into 6 groups. Each group named as the normal groups for 4, 8, and 12wk and the diabetic groups for 4, 8, and 12wk. The EB was injected into the cardiovascular system of the rats at the different time points. The retina of each group was obtained for observation.

RESULTS

The superficial vascular leakage was found in all 6 groups. The size of leakage area of superficial retinal blood vessels was (0.54±0.23)%, (0.65±0.11)%, and (0.58±0.10)% in normal group. No notable leakage was found in the deep blood vessels [(0.03±0.04)%, (0.03±0.05)%, and (0.03±0.05)%]. The deep retinal vascular leakage was found in the peripheral retina of diabetic rats. The size of leakage area of superficial retinal blood vessels in diabetic group were (0.53±0.22)%, (0.69±0.16)%, and (0.52±0.11)%. The leakage areas of deep blood vessels were (0.54±0.50)%, (1.42±0.16)%, and (1.80±0.07)% at 4, 8, and 12wk, respectively. There was a statistically difference of the leakage area between the 8th week and the 4th week of diabetes group (P=0.003). The statistically significant difference between the diabetes and the control groups was noted at 4wk and 8wk (P<0.001).

CONCLUSION

The main retinal pathological changes of early-stage diabetic rats are the vascular leakage of the periphery of deep retina. Diabetic rats modeled after 8wk have semi-quantitative statistical difference compared with the normal rats, thus early intervention treatment research can start at this time point.

SEVERITY OF DIABETIC MACULAR EDEMA CORRELATES WITH RETINAL VASCULAR BED AREA ON ULTRA-WIDE FIELD FLUORESCEIN ANGIOGRAPHY: DAVE Study

PURPOSE

To quantify retinal nonperfusion area and retinal vascular bed area (RVBA) in mm on ultra-widefield fluorescein angiography in eyes with diabetic macular edema (DME) and explore their relationship with the severity of DME.

METHODS

Prospective, observational case series. Baseline ultra-widefield fluorescein angiography images of 40 eyes from 29 patients with treatment-naive DME who participated in the DAVE study (NCT01552408) were stereographically projected at Doheny Image Reading Center. The retinal vasculature was automatically extracted to calculate RVBA. Nonperfusion area was manually delineated by two masked certified graders. Retinal vascular bed area and nonperfusion area were computed in mm automatically by adjusting for peripheral distortion and then correlated with the severity of DME.

RESULTS

The global RVBA for the entire retina in eyes with DME was increased compared with healthy controls (54.7 ± 16.6 mm vs. 37.2 ± 9.9 mm, P < 0.001) and correlated with the severity of DME (P < 0.05). Retinal ischemia (nonperfusion area) was nonuniformly distributed and not related to DME extent (P > 0.05).

CONCLUSION

Eyes with DME have an increased RVBA compared with healthy controls. The severity of DME appears to be related to global RVBA, but not to retinal ischemia.

Automated Grading of Diabetic Retinopathy with Ultra-Widefield Fluorescein Angiography and Deep Learning

PURPOSE

The objective of this study was to establish diagnostic technology to automatically grade the severity of diabetic retinopathy (DR) according to the ischemic index and leakage index with ultra-widefield fluorescein angiography (UWFA) and the Early Treatment Diabetic Retinopathy Study (ETDRS) 7-standard field (7-SF).

METHODS

This is a cross-sectional study. UWFA samples from 280 diabetic patients and 119 normal patients were used to train and test an artificial intelligence model to differentiate PDR and NPDR based on the ischemic index and leakage index with UWFA. A panel of retinal specialists determined the ground truth for our data set before experimentation. A confusion matrix as a metric was used to measure the precision of our algorithm, and a simple linear regression function was implemented to explore the discrimination of indexes on the DR grades. In addition, the model was tested with simulated 7-SF.

RESULTS

The model classification of DR in the original UWFA images achieved 88.50% accuracy and 73.68% accuracy in the simulated 7-SF images. A simple linear regression function demonstrated that there is a significant relationship between the ischemic index and leakage index and the severity of DR. These two thresholds were set to classify the grade of DR, which achieved 76.8% accuracy.

CONCLUSIONS

The optimization of the cycle generative adversarial network (CycleGAN) and convolutional neural network (CNN) model classifier achieved DR grading based on the ischemic index and leakage index with UWFA and simulated 7-SF and provided accurate inference results. The classification accuracy with UWFA is slightly higher than that of simulated 7-SF.

Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks

PURPOSE

Retinal vein occlusion (RVO) is the second most common cause of vision loss after diabetic retinopathy due to retinal vascular disease. Retinal nonperfusion (RNP), identified on fluorescein angiograms (FA) and appearing as hypofluorescence regions, is one of the most significant characteristics of RVO. Quantification of RNP is crucial for assessing the severity and progression of RVO. However, in current clinical practice, it is mostly conducted manually, which is time-consuming, subjective, and error-prone. The purpose of this study is to develop fully automated methods for segmentation of RNP using convolutional neural networks (CNNs).

METHODS

FA images from 161 patients were analyzed, and RNP areas were annotated by three independent physicians. The optimal method to use multi-physicians' labeled data to train the CNNs was evaluated. An adaptive histogram-based data augmentation method was utilized to boost the CNN performance. CNN methods based on context encoder module were developed for automated segmentation of RNP and compared with existing state-of-the-art methods.

RESULTS

The proposed methods achieved excellent agreements with physicians for segmentation of RNP in FA images. The CNN performance can be improved significantly by the proposed adaptive histogram-based data augmentation method. Using the averaged labels from physicians to train the CNNs achieved the best consensus with all physicians, with a mean accuracy of 0.883±0.166 with fivefold cross-validation.

CONCLUSIONS

We reported CNN methods to segment RNP in RVO in FA images. Our work can help improve clinical workflow, and can be useful for further investigating the association between RNP and retinal disease progression, as well as for evaluating the optimal treatments for the management of RVO.

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.

Novel imaging biomarkers in diabetic retinopathy and diabetic macular edema

Diabetic retinopathy is one of the major microvascular complications of diabetes mellitus. The most common causes of vision loss in diabetic retinopathy are diabetic macular edema and proliferative diabetic retinopathy. Recent developments in ocular imaging have played a significant role in early diagnosis and management of these complications. Color fundus photography is an imaging modality, which is helpful for screening patients with diabetic eye disease and monitoring its progression as well as response to treatment. Fundus fluorescein angiography (FFA) is a dye-based invasive test to detect subtle neovascularization, look for areas of capillary non-perfusion, diagnose macular ischemia, and differentiate between focal and diffuse capillary bed leak in cases of macular edema. Recent advances in retinal imaging like the introduction of spectral-domain and swept source-based optical coherence tomography (OCT), fundus autofluorescence (FAF), OCT angiography, and ultrawide field imaging and FFA have helped clinicians in the detection of certain biomarkers that can identify disease at an early stage and predict response to treatment in diabetic macular edema. This article will summarize the role of different imaging biomarkers in characterizing diabetic retinopathy and their potential contribution in its management.

Deep Learning-Based Segmentation and Quantification of Retinal Capillary Non-Perfusion on Ultra-Wide-Field Retinal Fluorescein Angiography

Reliable outcome measures are required for clinical trials investigating novel agents for preventing progression of capillary non-perfusion (CNP) in retinal vascular diseases. Currently, accurate quantification of topographical distribution of CNP on ultrawide field fluorescein angiography (UWF-FA) by retinal experts is subjective and lack standardisation. A U-net style network was trained to extract a dense segmentation of CNP from a newly created dataset of 75 UWF-FA images. A subset of 20 images was also segmented by a second expert grader for inter-grader reliability evaluation. Further, a circular grid centred on the FAZ was used to provide standardised CNP distribution analysis. The model for dense segmentation was five-fold cross-validated achieving area under the receiving operating characteristic of 0.82 (0.03) and area under precision-recall curve 0.73 (0.05). Inter-grader assessment on the 20 image subset achieves: precision 59.34 (10.92), recall 76.99 (12.5), and dice similarity coefficient (DSC) 65.51 (4.91), and the centred operating point of the automated model reached: precision 64.41 (13.66), recall 70.02 (16.2), and DSC 66.09 (13.32). Agreement of CNP grid assessment reached: Kappa 0.55 (0.03), perfused intraclass correlation (ICC) 0.89 (0.77, 0.93), non-perfused ICC 0.86 (0.73, 0.92), inter-grader agreement of CNP grid assessment values are Kappa 0.43 (0.03), perfused ICC 0.70 (0.48, 0.83), non-perfused ICC 0.71 (0.48, 0.83). Automated dense segmentation of CNP in UWF-FA images achieves performance levels comparable to inter-grader agreement values. A grid placed on the deep learning-based automatic segmentation of CNP generates a reliable and quantifiable method of measurement of CNP, to overcome the subjectivity of human graders.

RORγt Inhibitor-SR1001 Halts Retinal Inflammation, Capillary Degeneration, and the Progression of Diabetic Retinopathy

Diabetic retinopathy is a diabetes-mediated retinal microvascular disease that is the leading cause of blindness in the working-age population worldwide. Interleukin (IL)-17A is an inflammatory cytokine that has been previously shown to play a pivotal role in the promotion and progression of diabetic retinopathy. Retinoic acid-related orphan receptor gammaT (RORγt) is a ligand-dependent transcription factor that mediates IL-17A production. However, the role of RORγt in diabetes-mediated retinal inflammation and capillary degeneration, as well as its potential therapeutic attributes for diabetic retinopathy has not yet been determined. In the current study, we examined retinal inflammation and vascular pathology in streptozotocin-induced diabetic mice. We found RORγt expressing cells in the retinal vasculature of diabetic mice. Further, diabetes-mediated retinal inflammation, oxidative stress, and retinal endothelial cell death were all significantly lower in RORγt-/- mice. Finally, when a RORγt small molecule inhibitor (SR1001) was subcutaneously injected into diabetic mice, retinal inflammation and capillary degeneration were ameliorated. These findings establish a pathologic role for RORγt in the onset of diabetic retinopathy and identify a potentially novel therapeutic for this blinding disease.

Relationships among Retinal Nonperfusion, Neovascularization, and Vascular Endothelial Growth Factor Levels in Quiescent Proliferative Diabetic Retinopathy

Purpose: To investigate the relationships among the retinal nonperfusion (NP) area, neovascularization (NV) area, and aqueous humor vascular endothelial growth factor (VEGF) levels in quiescent proliferative diabetic retinopathy (PDR). Methods: Forty-seven eyes from 47 patients with treatment-naïve PDR that did not show macular edema or vitreous hemorrhage were enrolled. NP area, NV number, and NV area were quantitatively measured using ultra-widefield fluorescein angiography in an automated manner. Aqueous humor VEGF level was measured using a bead assay. Results: The NP areas of the total, posterior pole, peripheral retinae, and NV area positively correlated with each other (all p < 0.034). NV number correlated with total NP area, peripheral NP area, and NV area (all p ≤ 0.001). VEGF levels were significantly positively correlated with total, posterior polar, and peripheral NP areas and NV area (r = 0.575, 0.422, 0.558, and 0.362, respectively; all p ≤ 0.012). In eyes with NV in the disc area, the VEGF level was higher compare to eyes without NV in the disc area (208.89 ± 192.77 pg/mL vs. 103.34 ± 132.66, p = 0.010). A multiple linear regression model using NP area, NV area, and NVD demonstrated good prediction for VEGF level (R2 = 0.417, p < 0.001) and revealed a significant contribution of the peripheral NP area in predicting the VEGF level (β = 0.497, p = 0.002). Conclusions: Aqueous humor VEGF levels in quiescent PDR eyes were associated with NP and NV areas, which had positive correlations with each other. In addition, the NP area of the peripheral retina was the most important predictor of VEGF level.

Diabetic vasculopathy: macro and microvascular injury

PURPOSE OF REVIEW

Diabetes is a common and prevalent medical condition as it affects many lives around the globe. Specifically, type-2 Diabetes (T2D) is characterized by chronic systemic inflammation alongside hyperglycemia and insulin resistance in the body, which can result in atherosclerotic legion formation in the arteries and thus progression of related conditions called diabetic vasculopathies. T2D patients are especially at risk for vascular injury; adjunct in many of these patients heir cholesterol and triglyceride levels reach dangerously high levels and accumulate in the lumen of their vascular system.

RECENT FINDINGS

Microvascular and macrovascular vasculopathies as complications of diabetes can accentuate the onset of organ illnesses, thus it is imperative that research efforts help identify more effective methods for prevention and diagnosis of early vascular injuries. Current research into vasculopathy identification/treatment will aid in the amelioration of diabetes-related symptoms and thus reduce the large number of deaths that this disease accounts annually.

SUMMARY

This review aims to showcase the evolution and effects of diabetic vasculopathy from development to clinical disease as macrovascular and microvascular complications with a concerted reference to sex-specific disease progression as well.

Ultra-Widefield Protocol Enhances Automated Classification of Diabetic Retinopathy Severity with OCT Angiography

PURPOSE

To assess the diagnostic usefulness of retinal nonperfusion to classify eyes based on diabetic retinopathy (DR) severity on OCT angiography (OCTA) and determine whether wider field of view (FOV) OCTA protocols enhance the diagnostic usefulness of retinal nonperfusion in the classification of DR severity.

DESIGN

Retrospective cross-sectional study.

PARTICIPANTS

Diabetic patients undergoing ultra-widefield (UWF) OCTA imaging at 1 academic retina practice.

METHODS

Ultra-widefield OCTA images with 100° FOV were obtained from 60 eyes. Eyes were grouped as those with diabetes without retinopathy (DWR), those with nonproliferative diabetic retinopathy (NPDR), or those with proliferative diabetic retinopathy (PDR). The ratio of nonperfusion (RNP) was expressed as the percent area of capillary nonperfusion within the FOV. The RNP was obtained in the FOV 100° image and concentric sectors encompassing 10°, 10° to 30°, 30° to 50°, and 50° to 100°.

MAIN OUTCOME MEASURES

Mean RNP among DR groups, mean RNP measured among FOV sectors, and area under the curve (AUC) of the receiver operating characteristics when using RNP as a cutoff value to distinguish between DR groups.

RESULTS

Mean RNP from the FOV 50° to 100° sector was different among all groups: DWR, 14.6±5.1%; NPDR, 27.5±7.5%; and PDR, 41.5±19.1% (P < 0.01). Within each DR group, field of view from 50° to 100° measured higher RNP than all other sectors (P < 0.01). Field of view from 50° to 100° showed the highest optimal sensitivity and specificity to distinguish NPDR from DWR with an RNP cutoff value of 21.2% (89.5% and 88.2%; AUC, 0.944) and PDR from NPDR with an RNP cutoff value of 31.6% (79.2% and 78.9%; AUC, 0.752).

CONCLUSIONS

Ratio of nonperfusion on average is higher in more severe DR. The most peripheral sector of the widefield OCTA (FOV 50°-100°) showed on average higher RNP and showed more diagnostic usefulness in determining DR severity compared with more central sectors and the FOV 100 image as a whole.

Changes in Retinal Microcirculation Precede the Clinical Onset of Diabetic Retinopathy in Children With Type 1 Diabetes Mellitus

PURPOSE

To investigate whether abnormal glucose metabolism in diabetes mellitus (DM) affects the retinal microcirculation of children with well-controlled type 1 DM and to compare these results with those obtained from healthy children.

DESIGN

Cross-sectional prospective study.

METHODS

This study enrolled 60 patients with DM without clinically detectable diabetic retinopathy (DR) and 57 age-matched control subjects. Optical coherence tomography angiography (OCT-A) was performed using AngioVue (Avanti, Optivue). Foveal avascular zone (FAZ) area, nonflow area, superficial and deep vessel densities, FAZ perimeter, acircularity index of FAZ (AI; the ratio of the perimeter of FAZ and the perimeter of a circle with equal area), and foveal density (FD-300; vessel density in 300 μm around FAZ) were analyzed. Correlations between the investigated OCT-A parameters with DM duration and glycated hemoglobin (HbA1c) levels were evaluated among patients with type 1 DM.

RESULTS

Differences in the mean values for FAZ perimeter, AI, and FD-300 were statistically significant between DM group and control group (P < .001, P = .001, and P = .009, respectively). There were also statistically significant differences between the groups for vessel densities of deep superior hemi-parafovea, deep temporal parafovea, and deep superior parafoveal zones (P = .008, P = .015, and P = .005, respectively). There were no significant correlations between DM duration and HbA1c levels with the investigated OCT-A parameters.

CONCLUSION

Diabetic eyes without clinically detectable DR exhibited alterations in FD-300, AI, perimeter, and vessel density of parafoveal capillaries in deep capillary plexus preceding the enlargement of FAZ; therefore, these new parameters might be sensitive imaging biomarkers to define early DR.

Efficacy of intravitreal dexamethasone implant in persistent diabetic macular edema after primary treatment with intravitreal ranibizumab

PURPOSE

To evaluate the efficiency and possible complications of intravitreal dexamethasone (IVD) implant in diabetic macular edema (DME) resistant to treatment of three consecutive intravitreal ranibizumab (IVR) injections.

METHODS

Fifty eyes of 38 patients were considered in this study. The best corrected visual acuity (BCVA), central macular thickness (CMT), and values of intraocular pressure (IOP) were examined preoperatively and postoperatively in the 1st, 2nd, 4th, and 6th months of IVD implantation.

RESULTS

Twenty of the patients were women, and 18 of the patients were men. Mean age was 64.63 ± 7.15 (52-83) years. Mean number of IVR injection before IVD implantation was 3.4 ± 0.38. Mean BCVA (logMAR) was 0.874 ± 0.398 before IVD implantation, 0.598 ± 0.306 at the 1st month, 0.602 ± 0.340 at the 2nd month, 0.708 ± 0.359 at 4th month, and 0.800 ± 0.370 at 6th month. Mean of CMT was 519.700 ± 155.802 μm before IVD implantation, 274.000 ± 73.112 μm at the 1st month, 307.98 ± 87.869 μm at the 2nd month, 387.82 ± 110.503 μm at 4th month, and 478.54 ± 163.743 μm at 6th month. Improvements in BCVA and CMT were statistically significant (P < 0.05) at 1st, 2nd, and 4th months; however, these values were not statistically significant at 6 months. At 1st day, 1st and 2nd months, the values of IOP were increased significantly after IVD. Cataract progression was observed in just 1 of the 22 phakic patients.

CONCLUSIONS

In DME resistant to treatment of consecutive IVR, IVD implantation has been observed to be effective in increasing BCVA and decreasing CMT in the first 3 months. IVD implantation can be considered an alternative method in the treatment of resistant DME.

Diabetes induces IL-17A-Act1-FADD-dependent retinal endothelial cell death and capillary degeneration

PURPOSE

Diabetes leads to progressive complications such as diabetic retinopathy, which is the leading cause of blindness within the working-age population worldwide. Interleukin (IL)-17A is a cytokine that promotes and progresses diabetes. The objective of this study was to determine the role of IL-17A in retinal capillary degeneration, and to identify the mechanism that induces retinal endothelial cell death. These are clinically meaningful abnormalities that characterize early-stage non-proliferative diabetic retinopathy.

METHODS

Retinal capillary degeneration was examined in vivo using the streptozotocin (STZ) diabetes murine model. Diabetic-hyperglycemia was sustained for an 8-month period in wild type (C57BL/6) and IL-17A^-/- mice to elucidate the role of IL-17A in retinal capillary degeneration. Further, ex vivo studies were performed in retinal endothelial cells to identify the IL-17A-dependent mechanism that induces cell death.

RESULTS

It was determined that diabetes-induced retinal capillary degeneration was significantly lower in IL-17A^-/- mice. Further, retinal endothelial cell death occurred through an IL-17A/IL-17R ➔ Act1/FADD signaling cascade, which caused caspase-mediated apoptosis.

CONCLUSION

These are the first findings that establish a pathologic role for IL-17A in retinal capillary degeneration. Further, a novel IL-17A-dependent apoptotic mechanism was discovered, which identifies potential therapeutic targets for the early onset of diabetic retinopathy.

An automated framework to quantify areas of regional ischemia in retinal vascular diseases with OCT angiography

In this observational and cross-sectional study, capillary nonperfusion (CNP) and vascular changes in branch retinal vein occlusion (BRVO, sample size [n] = 26) and choroidal neovascularization (CNV, n = 29) were evaluated. Subjects underwent imaging using Optical coherence tomography angiography (Angiovue OCTA, RTVue XR, Optovue Inc., Fremont, California). Local fractal analysis was applied to the OCTA images of superficial, deep and choriocapillaris layer. CNP area (BRVO eyes) and vascular parameters were computed using local fractal-based method. Sensitivity and specificity of vascular parameters were assessed with receiver operating characteristics curve. Automated CNP area showed excellent agreement with manually quantified CNP areas in both superficial (intraclass coefficient [ICC] = 0.96) and deep (ICC = 0.96) layers. BRVO eyes showed significantly altered (P < .05) vascular parameters in both superficial and deep layer as compared to normal eyes (n = 30). CNVM eyes had significantly higher capillary free zones (P < .001) as compared to normal eyes. In normal vs BRVO eyes, vessel density and spacing between the large vessels had similar area under the curve (AUC) (P > .05) in both superficial (0.97 and 0.97, respectively) and deep layer (0.99 and 0.98, respectively). Further, capillary free zones showed high AUC (0.92) in differentiating CNV eyes from normal eyes.

Use of OCTA, FA, and Ultra-Widefield Imaging in Quantifying Retinal Ischemia: A Review

As ischemia remains a key prognostic factor in the management of various diseases including diabetic retinopathy, an increasing amount of research has been dedicated to its quantification as a potential biomarker. Advancements in the quantification of retinal ischemia have been made with the imaging modalities of fluorescein angiography (FA), ultra-widefield imaging (UWF), and optical coherence tomography angiography (OCTA), with each imaging modality offering certain benefits over the others. FA remains the gold standard in assessing the extent of ischemia. UWF imaging has allowed for the assessment of peripheral ischemia via FA. It is, however, OCTA that offers the best visualization of retinal vasculature with its noninvasive depth-resolved imaging and therefore has the potential to become a mainstay in the assessment of retinal ischemia. The primary purpose of this article is to review the use of FA, UWF, and OCTA to quantify retinal ischemia and the various methods described in the literature by which this is achieved.