A novel approach for automatic classification of macular degeneration OCT images.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.

Visual Outcome after Anti-Vascular Epithelial Growth Factor Therapy Using New Classification of Diabetic Macular Edema by Optical Coherence Tomography.

INTRODUCTION: The aim of the study was to examine alterations in visual acuity in patients with diabetic macular edema (DME), classified according to the TCED-HFV optical coherence tomography (OCT) system, following anti-vascular epithelial growth factor (VEGF) therapy. METHODS: The medical records of patients with DME receiving anti-VEGF therapy were retrospectively reviewed. Patients were divided into four groups according to the TCED-HFV OCT classification. Patient demographic and clinical characteristics and best-corrected visual acuity (BCVA) before and after treatment were compared among the groups. RESULTS: The BCVA before treatment was 0.49 ± 0.18, 0.81 ± 0.41, 0.83 ± 0.41, and 0.82 ± 0.49 in the early DME, advanced DME, severe DME, and atrophic maculopathy groups, respectively. The BCVA in the early DME group was therefore significantly lower than that in the other three groups (p = 0.042). After treatment, the BCVA improved to 0.15 ± 0.17, 0.52 ± 0.31, 0.62 ± 0.32, and 0.69 ± 0.47 in the early DME, advanced DME, severe DME, and atrophic maculopathy groups, respectively (p < 0.005). There were some differences among patients in the four groups in terms of the duration of diabetes, percentage of hemoglobin A1c, and duration of hypertension. CONCLUSION: The TCED-HFV OCT classification of patients with DME is exact and functional and can allow the severity of DME, and its response to anti-VEGF therapy, to be estimated.

Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema.

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%.

Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study.

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

ESASO classification relevance in the diagnosis and evolution in diabetic macular edema patients after dexamethasone implant treatment.

PURPOSE: To assess the clinical relevance of The European School for Advanced Studies in Ophthalmology (ESASO) classification in patients with diabetic macular edema (DME) after their first dexamethasone implant (DEXI) treatment. METHODS: Retrospective real-world study conducted on consecutive DME patients who underwent DEXI treatment and were controlled at month-2. Subjects were initially classified according to the ESASO classification stages. The outcomes were anatomical biomarkers with spectral-domain optical coherence tomography (SD-OCT) and best-corrected visual acuity (BCVA). RESULTS: A total of 128 patients were classified according to ESASO classification stages as early (7; 5.5%), advanced (100; 78.1%), and severe (21; 16.4%). At baseline, there were significant differences between stages in BCVA, central macular thickness (CMT), and tomography anatomical biomarkers (p < 0.05). Initial BCVA (logMAR) was 0.33 ± 0.10, 0.58 ± 0.34, and 0.71 ± 0.35 in the early, advanced, and severe stages, respectively (p < 0.05). At month-2, BCVA was 0.17 ± 0.15, 0.46 ± 0.29, and 0.69 ± 0.27 in those classified as early, advanced, and severe stages, respectively. At month-2, DME was resolved or improved in 6 (85.7%), 60 (60%), and 12 (60%) patients classified as early, advanced, and severe stages, respectively. CONCLUSIONS: There was a good correlation between BCVA and ESASO classification stages. Patients in the severe stage did not achieve visual acuity improvement over the study period.

Self-supervised category selective attention classifier network for diabetic macular edema classification.

AIMS: This study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically, the objective is to introduce a novel architecture, SSCSAC-Net, that leverages self-supervised learning and category-selective attention mechanisms to improve the precision of DME classification. METHODS: The proposed SSCSAC-Net integrates self-supervised learning to effectively utilize unlabeled data for learning robust features related to DME. Additionally, it incorporates a category-specific attention mechanism and a domain-specific layer into the ResNet-152 base architecture. The model is trained using an ensemble of unsupervised and supervised learning techniques. Benchmark datasets are utilized for testing the model's performance, ensuring its robustness and generalizability across different data distributions. RESULTS: Evaluation of the SSCSAC-Net on multiple datasets demonstrates its superior performance compared to existing techniques. The model achieves high accuracy, precision, and recall rates, with an accuracy of 98.7%, precision of 98.6%, and recall of 98.8%. Furthermore, the incorporation of self-supervised learning reduces the dependency on extensive labeled data, making the solution more scalable and cost-effective. CONCLUSIONS: The proposed SSCSAC-Net represents a significant advancement in automated DME classification. By effectively using self-supervised learning and attention mechanisms, the model offers improved accuracy in identifying DME-related features within retinal images. Its robustness and generalizability across different datasets highlight its potential for clinical applications, providing a valuable tool for clinicians in diagnosing DME effectively.

DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explainable macular disease diagnosis at eye level in optical coherence tomography images. METHODS: A total of 26,815 optical coherence tomography images were collected from 865 eyes, and 9 retinal lesions and 3 macular diseases were labeled by ophthalmologists, including diabetic macular edema and dry/wet age-related macular degeneration. We applied deep learning to classify retinal lesions at image level and random forests to achieve an explainable disease diagnosis at eye level. The performance of the integrated two-stage framework was evaluated and compared with human experts. RESULTS: On testing data set of 2,480 optical coherence tomography images from 80 eyes, the deep learning model achieved an average area under curve of 0.978 (95% confidence interval, 0.971-0.983) for lesion classification. In addition, random forests performed accurate disease diagnosis with a 0% error rate, which achieved the same accuracy as one of the human experts and was better than the other three experts. It also revealed that the detection of specific lesions in the center of macular region had more contribution to macular disease diagnosis. CONCLUSION: The integrated method achieved high accuracy and interpretability in retinal lesion classification and macular disease diagnosis in optical coherence tomography images and could have the potential to facilitate the clinical diagnosis.

MHANet: A hybrid attention mechanism for retinal diseases classification.

With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.

En face image-based classification of diabetic macular edema using swept source optical coherence tomography.

This retrospective study was performed to classify diabetic macular edema (DME) based on the localization and area of the fluid and to investigate the relationship of the classification with visual acuity (VA). The fluid was visualized using en face optical coherence tomography (OCT) images constructed using swept-source OCT. A total of 128 eyes with DME were included. The retina was segmented into: Segment 1, mainly comprising the inner nuclear layer and outer plexiform layer, including Henle's fiber layer; and Segment 2, mainly comprising the outer nuclear layer. DME was classified as: foveal cystoid space at Segment 1 and no fluid at Segment 2 (n = 24), parafoveal cystoid space at Segment 1 and no fluid at Segment 2 (n = 25), parafoveal cystoid space at Segment 1 and diffuse fluid at Segment 2 (n = 16), diffuse fluid at both segments (n = 37), and diffuse fluid at both segments with subretinal fluid (n = 26). Eyes with diffuse fluid at Segment 2 showed significantly poorer VA, higher ellipsoid zone disruption rates, and greater central subfield thickness than did those without fluid at Segment 2 (P < 0.001 for all). These results indicate the importance of the localization and area of the fluid for VA in DME.

Intravitreal injections during COVID-19 outbreak: Real-world experience from an Italian tertiary referral center.

We report our experience during COVID-19 outbreak for intravitreal injections in patients with maculopathy. We proposed a treatment priority levels and timings; the "High" priority level includes all monocular patients; the "Moderate" is assigned to all patients with an active macular neovascularization; the patients affected by diabetic macular edema or retinal vein occlusion belong to the "Low" class. This organization allowed us to treat the most urgent patients although the injections performed had a 91.7% drop compared to the same period of 2019.

Classification of optical coherence tomography images using a capsule network.

BACKGROUND: Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional relations in a pooling layer. Therefore, because capsule networks can learn positional information from images, we attempted application of a capsule network to OCT images to overcome that shortcoming. This study is our attempt to improve classification accuracy by replacing CNN with a capsule network. METHODS: From an OCT dataset, we produced a training dataset of 83,484 images and a test dataset of 1000 images. For training, the dataset comprises 37,205 images with choroidal neovascularization (CNV), 11,348 with diabetic macular edema (DME), 8616 with drusen, and 26,315 normal images. The test dataset has 250 images from each category. The proposed model was constructed based on a capsule network for improving classification accuracy. It was trained using the training dataset. Subsequently, the test dataset was used to evaluate the trained model. RESULTS: Classification of OCT images using our method achieved accuracy of 99.6%, which is 3.2 percentage points higher than that of other methods described in the literature. CONCLUSION: The proposed method achieved classification accuracy results equivalent to those reported for other methods for CNV, DME, drusen, and normal images.

Spectral domain optical coherence tomography classification of diabetic macular edema: a new proposal to clinical practice.

PURPOSE: To classify the types of diabetic macular edema (DME) and evaluate its morphological features on spectral domain optical coherence tomography (SD-OCT) and determine correlations between visual acuity and OCT findings. METHODS: We assessed 406 eyes of 309 patients with a diagnosis of DME retrospectively. Three types based on SD-OCT were identified: diffuse macular edema, cystoid macular edema, and cystoid degeneration. Morphological features such as serous macular detachment (SMD), vitreomacular interface abnormalities (VMAI), hard exudates, photoreceptor status, and correlations between visual acuity and those morphological features were also evaluated by SD-OCT. RESULTS: The most common type of DME was cystoid edema (68.5%). No statistically significant difference was found between groups in sex (P = 0.40), type of diabetes (P = 0.50), or diabetic retinopathy (P = 0.78). However, the duration of symptoms and BCVA was significantly lower in the group with cystoid degeneration compared with the group with cystoid edema (P < 0.001) and the group with diffuse macular edema (P < 0.001). In the group with cystoid degeneration compared with the groups with cystoid and diffuse edema, the central fovea and central subfield were significantly thicker (both (P < 0.001), the subfoveal choroid was significantly thinner (P = 0.049), rate of serous macular detachment was significantly lower (P < 0.001), and the rate of outer retinal damage was significantly higher (P < 0.001). CONCLUSIONS: Cystoid macular degeneration, which is consistent with poor functional and morphological outcomes, should be differentiated from cystoid macular edema. Serous macular detachment, which is mostly seen in eyes with early stages of DME, should be evaluated as an accompanying morphological finding rather than a type of DME.

Classifications of diabetic macular edema.

Diabetic retinopathy is a major cause worldwide of vision loss from diabetic maculopathy or proliferative retinopathy. Without widely accepted classifications of diabetic retinopathy and diabetic maculopathy, it is difficult to compare results of clinical trials or monitor clinical care. The European School of Advanced Studies in Ophthalmology has developed an international classification of diabetic maculopathy based upon spectral domain optical coherence tomography, which could be helpful for both initial evaluation and subsequent follow-up of diabetic patients in both clinical practice and experimental trials.

An optical coherence tomography-based grading of diabetic maculopathy proposed by an international expert panel: The European School for Advanced Studies in Ophthalmology classification.

AIMS: To present an authoritative, universal, easy-to-use morphologic classification of diabetic maculopathy based on spectral domain optical coherence tomography. METHODS: The first draft of the project was developed based on previously published classifications and a literature search regarding the spectral domain optical coherence tomography quantitative and qualitative features of diabetic maculopathy. This draft was sent to an international panel of retina experts for a first revision. The panel met at the European School for Advanced Studies in Ophthalmology headquarters in Lugano, Switzerland, and elaborated the final document. RESULTS: Seven tomographic qualitative and quantitative features are taken into account and scored according to a grading protocol termed TCED-HFV, which includes foveal thickness (T), corresponding to either central subfoveal thickness or macular volume, intraretinal cysts (C), the ellipsoid zone (EZ) and/or external limiting membrane (ELM) status (E), presence of disorganization of the inner retinal layers (D), number of hyperreflective foci (H), subfoveal fluid (F), and vitreoretinal relationship (V). Four different stages of the disease, that is, early diabetic maculopathy, advanced diabetic maculopathy, severe diabetic maculopathy, and atrophic maculopathy, are based on the first four variables, namely the T, C, E, and D. The different stages reflect progressive severity of the disease. CONCLUSION: A novel grading system of diabetic maculopathy is hereby proposed. The classification is aimed at providing a simple, direct, objective tool to classify diabetic maculopathy (irrespective to the treatment status) even for non-retinal experts and can be used for therapeutic and prognostic purposes, as well as for correct evaluation and reproducibility of clinical investigations.

DISEASE CLASSIFICATION OF MACULAR OPTICAL COHERENCE TOMOGRAPHY SCANS USING DEEP LEARNING SOFTWARE: Validation on Independent, Multicenter Data.

PURPOSE: To evaluate Pegasus optical coherence tomography (OCT), a clinical decision support software for the identification of features of retinal disease from macula OCT scans, across heterogenous populations involving varying patient demographics, device manufacturers, acquisition sites, and operators. METHODS: Five thousand five hundred and eighty-eight normal and anomalous macular OCT volumes (162,721 B-scans), acquired at independent centers in five countries, were processed using the software. Results were evaluated against ground truth provided by the data set owners. RESULTS: Pegasus-OCT performed with areas under the curve of the receiver operating characteristic of at least 98% for all data sets in the detection of general macular anomalies. For scans of sufficient quality, the areas under the curve of the receiver operating characteristic for general age-related macular degeneration and diabetic macular edema detection were found to be at least 99% and 98%, respectively. CONCLUSION: The ability of a clinical decision support system to cater for different populations is key to its adoption. Pegasus-OCT was shown to be able to detect age-related macular degeneration, diabetic macular edema, and general anomalies in OCT volumes acquired across multiple independent sites with high performance. Its use thus offers substantial promise, with the potential to alleviate the burden of growing demand in eye care services caused by retinal disease.

Development of "Predict ME," an online classifier to aid in differentiating diabetic macular edema from pseudophakic macular edema.

PURPOSE: Differentiating the underlying pathology of macular edema in patients with diabetic retinopathy following cataract surgery can be challenging. In 2015, Munk and colleagues trained and tested a machine learning classifier which uses optical coherence tomography variables in order to distinguish the underlying pathology of macular edema between diabetic macular edema and pseudophakic cystoid macular edema. It was able to accurately diagnose the underlying pathology in 90%-96% of cases. However, actually using the trained classifier required dedicated software and advanced technical skills which hindered its accessibility to most clinicians. Our aim was to package the classifier in an easy to use web-tool and validate the web-tool using a new cohort of patients. METHODS: We packaged the classifier in a web-tool intended for use on a personal computer or mobile phone. We first ensured that the results from the web-tool coincide exactly with the results from the original algorithm and then proceeded to test it using data of 14 patients. RESULTS: The etiology was accurately predicted in 12 out of 14 cases (86%). The cases with diabetic macular edema were accurately diagnosed in 7 out of 7 cases. Of the pseudophakic cystoid macular edema cases, 5 out of 6 were correctly interpreted and 1 case with a mixed etiology was interpreted as pseudophakic cystoid macular edema. Variable input was reported to be easy and took on average 7 ± 3 min. CONCLUSION: The web-tool implementation of the classifier seems to be a valuable tool to support research into this field.

RETINAL HYPERREFLECTIVE FOCI IN TYPE 1 DIABETES MELLITUS.

PURPOSE: To investigate hyperreflective foci (HF) on spectral-domain optical coherence tomography in patients with Type 1 diabetes mellitus across different stages of diabetic retinopathy (DR) and diabetic macular edema (DME) and to study clinical and morphological characteristics associated with HF. METHODS: Spectral-domain optical coherence tomography scans and color fundus photographs were obtained of 260 patients. Spectral-domain optical coherence tomography scans were graded for the number of HF and other morphological characteristics. The distribution of HF across different stages of DR and DME severity were studied. Linear mixed-model analysis was used to study associations between the number of HF and clinical and morphological parameters. RESULTS: Higher numbers of HF were found in patients with either stage of DME versus patients without DME (P < 0.001). A trend was observed between increasing numbers of HF and DR severity, although significance was only reached for moderate nonproliferative DR (P = 0.001) and proliferative DR (P = 0.019). Higher numbers of HF were associated with longer diabetes duration (P = 0.029), lower high-density lipoprotein cholesterol (P = 0.005), and the presence of microalbuminuria (P = 0.005). In addition, HF were associated with morphological characteristics on spectral-domain optical coherence tomography, including central retinal thickness (P = 0.004), cysts (P < 0.001), subretinal fluid (P = 0.001), and disruption of the external limiting membrane (P = 0.018). CONCLUSION: The number of HF was associated with different stages of DR and DME severity. The associations between HF and clinical and morphological characteristics can be of use in further studies evaluating the role of HF as a biomarker for disease progression and treatment response.

Classification of fundus autofluorescence abnormal patterns in diabetic macular edema

Introduction: Patients with diabetic macular edema can develop fundus autofluorescence alterations; thus far, these alterations have been more widely studied with scanning or confocal laser systems. Objective: To describe and classify fundus autofluorescence abnormal patterns in patients with diabetic macular edema using the fundus autofluorescence system with a flash camera. Method: Observational, retrospective, cross-sectional, descriptive study. Fundus autofluorescence digital images of non-comparative cases with untreated diabetic macular edema, obtained and stored with a flash camera system, were assessed. Inter-observer variability was evaluated. Results: 37 eyes of 20 patients were included. Lens opacity was the most common cause of inadequate image quality. Five different fundus autofluorescence patterns were observed: decreased (13%), normal (40%), single-spot hyper-autofluorescent (17 %), multiple-spot hyper-autofluorescent (22 %) and plaque-like hyper-autofluorescent (8 %). The kappa coefficient was 0.906 (p = 0.000). Conclusions: Different fundus autofluorescence phenotypic patterns are observed with flash camera systems in patients with diabetic macular edema. A more accurate phenotypic classification could help establish prognostic factors for visual loss or for the design of clinical trials for diabetic macular edema.

The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes.

Background: Current manual diabetic retinopathy (DR) screening using eye care experts cannot scale to screen the growing population of diabetes patients who are at risk for vision loss. EyeArt system is an automated, cloud-based artificial intelligence (AI) eye screening technology designed to easily detect referral-warranted DR immediately through automated analysis of patient's retinal images. Methods: This retrospective study assessed the diagnostic efficacy of the EyeArt system v2.0 analyzing 850,908 fundus images from 101,710 consecutive patient visits, collected from 404 primary care clinics. Presence or absence of referral-warranted DR (more than mild nonproliferative DR [NPDR]) was automatically detected by the EyeArt system for each patient encounter, and its performance was compared against a clinical reference standard of quality-assured grading by rigorously trained certified ophthalmologists and optometrists. Results: Of the 101,710 visits, 75.7% were nonreferable, 19.3% were referable to an eye care specialist, and in 5.0%, the DR level was unknown as per the clinical reference standard. EyeArt screening had 91.3% (95% confidence interval [CI]: 90.9-91.7) sensitivity and 91.1% (95% CI: 90.9-91.3) specificity. For 5446 encounters with potentially treatable DR (more than moderate NPDR and/or diabetic macular edema), the system provided a positive "refer" output to 5363 encounters achieving sensitivity of 98.5%. Conclusions: This study captures variations in real-world clinical practice and shows that an AI DR screening system can be safe and effective in the real world. This study demonstrates the value of this easy-to-use, automated tool for endocrinologists, diabetologists, and general practitioners to address the growing need for DR screening and monitoring.

Classification of Regions of Nonperfusion on Ultra-widefield Fluorescein Angiography in Patients with Diabetic Macular Edema.

PURPOSE: To classify retinal nonperfusion regions (NPRs) in patients with diabetic macular edema (DME) and assess the relationship with severity of DME. DESIGN: Prospective, observational case series. METHODS: Forty eyes of 29 patients with treatment-naïve center-involved macular edema secondary to diabetes mellitus were included (The DAVE study, NCT01552408) in this analysis. Ultra-widefield fluorescein angiography (UWF FA) images were transmitted to the Doheny Image Reading Center, where they were corrected using stereographic projection to adjust for peripheral distortion. Two independent, certified graders manually evaluated the NPR and classified the nonperfusion as being associated with leakage or without leakage. The size of these 2 subtypes of NPR were computed in mm2 and assessed across the entire retina and within 3 concentric retinal zones. The relationship between subtype of NPR and the severity of DME was assessed. RESULTS: In 40 eyes with treatment-naïve DME, visual acuity was significantly correlated with central macular thickness (CMT) and macular volume (MV). The NPR with leakage was positively correlated with CMT (R = 0.408, P = .009) and MV (R = 0.399, P = .011), whereas the NPR without leakage was negatively correlated with CMT (R = -0.468, P = .002) and MV (R = -0.473, P = .002). The NPR with leakage in the posterior region was significantly greater compared to the mid-periphery and the far periphery (P < .001), whereas the NPR without leakage was significantly greater in the mid-periphery compared with the far periphery or the posterior region (P = .001). CONCLUSION: In patients with DME, the severity of DME appears to be positively correlated with NPR with leakage but negatively correlated with NPR without leakage. These findings may have implications for the pathophysiology of DME and the design of protocols for targeted laser in these eyes.