Absence Of Diabetic Macular Edema Research Articles

Anti-vascular endothelial growth factor therapy and retinal non-perfusion in diabetic retinopathy: A meta-analysis of randomised trials.

Retinal non-perfusion (RNP) is fundamental to disease onset and progression in diabetic retinopathy (DR). Whether anti-vascular endothelial growth factor (anti-VEGF) therapy can modify RNP progression is unclear. This investigation quantified the impact of anti-VEGF therapy on RNP progression compared with laser or sham at 12 months. A systematic review and meta-analysis of randomised controlled trials (RCTs) were performed; Ovid MEDLINE, EMBASE and CENTRAL were searched from inception to 4th March 2022. The change in any continuous measure of RNP at 12 months and 24 months was the primary and secondary outcomes, respectively. Outcomes were reported utilising standardised mean differences (SMD). The Cochrane Risk of Bias Tool version-2 and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines informed risk of bias and certainty of evidence assessments. Six RCTs (1296 eyes) and three RCTs (1131 eyes) were included at 12 and 24 months, respectively. Meta-analysis demonstrated that RNP progression may be slowed with anti-VEGF therapy compared with laser/sham at 12 months (SMD: -0.17; 95% confidence interval [CI]: -0.29, -0.06; p = 0.003; I2 = 0; GRADE rating: LOW) and 24-months (SMD: -0.21; 95% CI: -0.37, -0.05; p = 0.009; I2 = 28%; GRADE rating: LOW). The certainty of evidence was downgraded due to indirectness and due to imprecision. Anti-VEGF treatment may slightly impact the pathophysiologic process of progressive RNP in DR. The dosing regimen and the absence of diabetic macular edema may impact this potential effect. Future trials are needed to increase the precision of the effect and inform the association between RNP progression and clinically important events. CRD42022314418.

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.

Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia. In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920-0.954), 0.958 (0.930-0.977), and 0.965 (0.948-0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940-0.995), 0.951 (0.898-0.982), and 0.975 (0.947-0.991) for the primary data set and >0.894 for the external data sets. We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs.

Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detection, following which precision medication can help to mitigate the vision loss. Method: In order to automate the screening of DME, we propose a novel DMENet Algorithm which is built on the pillars of Convolutional Neural Networks (CNNs). DMENet analyses the preprocessed color fundus images and passes it through a two-stage pipeline. The first stage detects the presence or absence of DME whereas the second stage takes only the positive cases and grades the images based on severity. In both the stages, we use a novel Hierarchical Ensemble of CNNs (HE-CNN). This paper uses two of the popular publicly available datasets IDRiD and MESSIDOR for classification. Preprocessing on the images is performed using morphological opening and gaussian kernel. The dataset is augmented to solve the class imbalance problem for better performance of the proposed model. Results: The proposed methodology achieved an average Accuracy of 96.12%, Sensitivity of 96.32%, Specificity of 95.84%, and F−1 score of 0.9609 on MESSIDOR and IDRiD datasets. Conclusion: These excellent results establish the validity of the proposed methodology for use in DME screening and solidifies the applicability of the HE-CNN classification technique in the domain of biomedical imaging.

A Multi-Task Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multi-Centre Analysis

Background: Diabetic macular edema (DME) is the primary cause of irreversible vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. Methods: We trained and validated two versions of a multi-task network to classify DME (centre-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia. Findings: In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary datasets obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary datasets and greater than 0.894 for the external datasets. Interpretation: We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which will create a more effective triaging mechanism to tertiary eye clinics. Funding: Research Grants Council General Research Fund, Hong Kong; Research to Prevent Blindness; and National Institutes of Health Grant. Conflict of Interest: We report no potential conflicts of interest relevant to this article. Ethical Approval: The current study presented here was conducted in accordance with the 1964 Declaration of Helsinki and was approved by local research ethics committees. Because the study is a retrospective analysis of fully anonymized OCT images, the ethics committees waived the requirements for informed consent.

Quantitative Ultra-Widefield Angiographic Features and Associations with Diabetic Macular Edema

To examine the relationship between diabetic macular edema (DME) and quantitative ultra-widefield fluorescein angiography (UWFA) metrics of ischemia, leakage, and microaneurysms. Retrospective image analysis study. Eyes with diabetic retinopathy that had undergone spectral-domain OCT, UWFA, and ultra-widefield fundus photography. OCT images were analyzed to determine the presence or absence of DME, central subfield thickness (CST), and subretinal fluid. Using a semiautomated analysis platform, UWFA images were segmented for ischemia, leakage, and microaneurysms with manual correction as needed. Clinical variables, including age, gender, race, hemoglobin A1C levels, blood pressure, cholesterol levels, use of blood thinners, smoking status, and lens status also were evaluated. Factors associated with the presence and severity of DME. A total of 304 eyes (156 right eyes, 148 left eyes) from 178 diabetic patients were analyzed in the study. Panretinal leakage index, microaneurysm count, and ischemic index were not significantly different between eyes with and without DME in univariate assessment. Zonal assessments of macular microaneurysms and macular leakage index values revealed that eyes with DME showed a significantly higher microaneurysm count (P= 0.001) and leakage index (P < 0.0001) in the posterior pole compared with eyes without DME. Severity of macular thickening (i.e., CST) was associated significantly with macular leakage index and posterior pole microaneurysm count (P= 0.0002 and P= 0.03, respectively). In addition to posterior pole leakage index and microaneurysm count, DME was associated with older age (P < 0.01), higher systolic blood pressure (P < 0.001), and white race (P= 0.03). Multivariate assessment confirmed the independent association of presence of DME with macular leakage index and macular microaneurysm count (P < 0.01). Quantitative measures of leakage index and microaneurysm count in the posterior pole on UWFA images were associated with the presence and severity of DME. Panretinal analyses were not linked to DME as strongly. Additional research is needed to determine the role of quantitative UWFA in predicting DME development and characterizing patient prognosis.

Peripheral blood metabolic and inflammatory factors as biomarkers to ocular findings in diabetic macular edema

AimsTo study the association between peripheral blood metabolic and inflammatory factors and presence of diabetic macular edema (DME) and its related anatomic features in type 2 diabetic mellitus (T2DM) patients.Material and methodsObservational cross-sectional study on a proof of concept basis. Seventy-six T2DM included patients were divided based on the presence (n = 58) or absence of DME (n = 18) according to optical coherence tomography (OCT). Ultra-widefield fluorescein angiography (UWFA) was performed in DME patients. Fasting peripheral blood sample testing included glycemia, glycated hemoglobin, creatinin and lipid levels among others. Serum levels of a broad panel of cytokines and inflammatory mediators were also analysed. OCT findings included central subfoveal thickness, diffuse retinal thickness (DRT), cystoid macular edema (CME), serous retinal detachment and epirretinal membrane. UWFA items included pattern of DME, presence of peripheral retinal ischemia and enlarged foveal avascular zone (FAZ).ResultsMetabolic and inflammatory factors did not statistically differ between groups. However, several inflammatory mediators did associate to certain ocular items of DME cases: IL-6 was significantly higher in patients with DRT (p = 0.044), IL-10 was decreased in patients with CME (p = 0.012), and higher IL-8 (p = 0.031) and VEGF levels (p = 0.031) were observed in patients with enlarged FAZ.ConclusionInflammatory and metabolic peripheral blood factors in T2DM may not be differentially associated to DME when compared to non-DME cases. However, some OCT and UWFA features of DME such as DRT, CME and enlarged FAZ may be associated to certain systemic inflammatory mediators.

Quantification of Diabetic Macular Ischemia Using Optical Coherence Tomography Angiography and Its Relationship with Visual Acuity

To quantify foveal avascular zone (FAZ) area and macular vascular density objectively using optical coherence tomography angiography (OCTA) and to examine correlations with visual acuity in eyes with diabetic retinopathy (DR) in the absence of diabetic macular edema. Retrospective observational case series. Eighty-four eyes from 55 patients with DR and 34 control eyes from 27 age-matched healthy participants. All eyes underwent OCTA (RTVue-XR Avanti; Optovue, Inc, Fremont, CA). Integrated automated algorithms were used to quantify FAZ area and macular vascular density. FAZ area, vessel area density (VAD), vessel length density (VLD), and visual acuity. In each study eye, DR was classified as mild nonproliferative DR (NPDR; n= 32 [38%]), moderate-to-severe NPDR (n= 31 [37%]), or proliferative DR (n= 21 [25%]). Mean FAZ area was greater in diabetic eyes compared with control eyes both in the superficial (0.427 mm2 vs. 0.275 mm2; P < 0.001) and deep (0.616 mm2 vs. 0.372 mm2; P < 0.001) vascular networks. Mean VAD was lower in diabetic eyes compared with control eyes in both the superficial (49.44% vs. 55.09%; P < 0.001) and deep (56.65% vs. 61.32%; P < 0.001) networks. Mean VLD was also lower in diabetic eyes compared with control eyes in both the superficial (17.68mm-1 vs. 21.55 mm-1; P < 0.001) and deep (21.19 mm-1 vs. 24.38 mm-1; P < 0.001) networks. In all eyes, there was a statistically significant negative correlation between the logarithm of the minimum angle of resolution (logMAR) visual acuity and the vascular density in both the superficial (VAD, ρ=-0.52; VLD, ρ=-0.54; P < 0.001) and deep (VAD, ρ=-0.50; VLD, ρ=-0.50; P < 0.001) networks. A positive correlation was found between logMAR visual acuity and FAZ area in both the superficial (ρ= 0.29; P<0.01) and deep (ρ= 0.48; P < 0.001) networks. Automated quantitative algorithms allow for objective assessment of retinal vascular changes in eyes with DR that are correlated to visual acuity. These methods may prove useful in monitoring disease progression and identifying parameters that affect visual function.

Macular dysfunction is common in both type 1 and type 2 diabetic patients without macular edema.

To study retinal function in asymptomatic Type 1 and Type 2 diabetic patients with nonproliferative diabetic retinopathy (NPDR) and no clinical signs of diabetic macular edema. Thirty-six consecutive Type 1 and Type 2 diabetic patients with nonproliferative diabetic retinopathy and no diabetic macular edema and 28 healthy controls underwent a complete ophthalmologic examination, including spectral domain optical coherence tomography and microperimetry. Seventy-one eyes (17 patients with Type 1 and 19 with Type 2 diabetes) were tested, and data from 36 (17 Type 1 and 19 Type 2) eyes were analyzed. Mean best-corrected visual acuity was 0.00 ± 0.01 logMAR and 0.00 ± 0.02 logMAR for Type 1 and Type 2 diabetic patients, respectively (P = 0.075). Mean central foveal thickness was 234.5 ± 13.7 μm and 256.3 ± 12.7 μm for Type 1 and Type 2 diabetic patients, respectively (P = 0.04); the central foveal thickness was statistically different compared with the control groups (P = 0.04 and P = 0.01, respectively). Mean retinal sensitivity was 18.9 ± 0.5 dB and 17.7 ± 0.4 dB for Type 1 and Type 2 diabetic patients, respectively; it was statistically different compared with control groups (P < 0.0001 and P < 0.0001, respectively). We demonstrated a significantly reduced sensitivity in both nonproliferative diabetic retinopathy groups without diabetic macular edema compared with healthy controls; this reduction was greater in Type 2 diabetic patients. Central foveal thickness was increased in all diabetic patients compared with healthy controls, despite the absence of diabetic macular edema.

Screening for Diabetic Retinopathy: 1 and 3 Nonmydriatic 45-degree Digital Fundus Photographs vs 7 Standard Early Treatment Diabetic Retinopathy Study Fields

To evaluate if simple- or multiple-field digital color nonmydriatic (NM) retinal images can replace 7 standard stereoscopic fundus photographs in the screening of diabetic retinopathy (DR). Prospective, masked, comparative case series. One hundred and eight eyes of 55 diabetics were studied to determine single lesions and to grade clinical levels of DR and diabetic macular edema (DME) using both 1 and 3 NM digital color retinal images compared with the Early Treatment Diabetic Retinopathy Study (ETDRS) 7 standard 35-mm stereoscopic color fundus photographs (7F-ETDRS). All eyes underwent NM 45-degree field images of 1 central field (1F-NM), NM 45-degree field images of 3 fields (3F-NM), and, after pupil dilatation, 30-degree 7F-ETDRS photography. Images were analyzed by 2 independent, masked retinal specialists (S.V. and E.B.), lesion-by-lesion according to the ETDRS protocol and for clinical severity level of DR and DME according to the international classification of DR. Using 7F-ETDRS as the gold standard, agreement was substantial for grading clinical levels of DR and DME (kappa = 0.69 and kappa = 0.75) vs 3F-NM; moderate for DR level (kappa = 0.56) and substantial for DME (kappa = 0.66) vs 1F-NM; almost perfect for detecting presence or absence of DR (kappa = 0.88) vs both 1F-NM and 3F-NM; and almost perfect for presence or absence of DME (kappa = 0.97) vs 3F-NM and substantial (kappa = 0.75) vs 1F-NM. Sensitivity and specificity for detecting referable levels of DR were 82% and 92%, respectively, for 3F-NM and 71% and 96%, respectively, for 1F-NM. Three color 45-degree NM fundus fields may be an effective tool in a screening setting to determine critical levels of DR and DME for prompt specialist referral. One central 45-degree image is sufficient to determine absence or presence of DR and DME, but not for grading it.