Detection Of Macular Edema Research Articles

Generating Synthesized Fluorescein Angiography Images From Color Fundus Images by Generative Adversarial Networks for Macular Edema Assessment.

To assess the feasibility of generating synthetic fluorescein angiography (FA) images from color fundus (CF) images using pixel-to-pixel generative adversarial network (pix2pixGANs) for clinical applications. Research questions addressed image realism to retinal specialists and utility for assessing macular edema (ME) in Retinal Vein Occlusion (RVO) eyes. We used a registration-guided pix2pixGANs method trained on the CF-FA dataset from Kham Eye Centre, Kandze Prefecture People's Hospital. A visual Turing test confirmed the realism of synthetic images without novel artifacts. We then assessed the synthetic FA images for assessing ME. Finally, we quantitatively evaluated the synthetic images using Fréchet Inception distance (FID) and structural similarity measures (SSIM). The raw development dataset had 881 image pairs from 349 subjects. Our approach is capable of generating realistic FA images because small vessels are clearly visible and sharp within one optic disc diameter around the macula. Two retinal specialists agreed that more than 85% of synthetic FA images have good or excellent image quality. For ME detection, accuracy was similar for real and synthetic images. FID demonstrated a 38.9% improvement over the previous state-of-the-art (SOTA), and SSIM reached 0.78 compared to the previous SOTA's 0.67. We developed a pix2pixGANs model translating FA images from label-free CF images, yielding reliable synthetic FA images. This suggests potential for noninvasive evaluation of ME in RVO eyes using pix2pix GANs techniques. Pix2pixGANs techniques have the potential to assist in the noninvasive clinical assessment of ME in RVO eyes.

Advancing Diabetic Macular Edema Detection from 3D Optical Coherence Tomography Scans: Integrating Privacy-Preserving AI and Generalizability Techniques — A Prospective Validation in Vietnam

Advancing Diabetic Macular Edema Detection from 3D Optical Coherence Tomography Scans: Integrating Privacy-Preserving AI and Generalizability Techniques — A Prospective Validation in Vietnam

A novel approach to macular edema detection: DeepLabv3+ segmentation and VGG with vision transformer classification

The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two deep learning models: Deeplabv3 + and VGG with a vision transformer. The Deeplabv3 + model is used to segment the macula region in the retinal images. The segmented macula region is then fed into the VGG for feature extraction with a vision transformer model for detection. This approach leverages the strengths of both models in detecting accurately and efficiently. The Deeplabv3 + model can accurately segment the macula region, which is crucial for accurate detection. The VGG combined with a vision transformer model proves highly efficient in detecting even subtle changes in the macular region, signifying the existence of macular edema. The results of our experiments with the dataset show that the proposed method outperforms current cutting-edge techniques. With an outstanding precision rate of 99.53%, the suggested approach firmly solidifies its superiority. The results highlight the effectiveness of the proposed technique in precisely and effectively detecting pathological fluid accumulation in retina images. This ability can have a substantial influence on the early detection and management of eye disorders.

Are optical coherence tomography and fluorescein angiography comparable for detection of macular edema in Behçet uveitis?

To investigate the role and agreement of spectral domain optical coherence tomography (SD-OCT) and fluorescein angiography (FA) for macular edema (ME) diagnosis in Behçet uveitis (BU). Eyes with ME detected by SD-OCT and/or FA were enrolled. Type of ME by SD-OCT and grade of macular leakage (ML) by FA were investigated. Eyes were divided into three groups according to the agreement of tests. Three groups and factors that could affect the compatibility of tests were statistically analyzed. Sixty-one eyes of 61 patients were included. Both imaging methods detected ME in 42.6% of eyes (group 1). In 47.5% of eyes, ME was revealed by OCT (group 2). The remaining 9.8% of the eyes had ME, detected only by FA (group 3). Grade 2 ML by FA was more common in group 1 (p = 0.01). The grade of ML by FA was similar in groups 2 and 3. In group 2, most of the eyes showed diffuse ME by OCT (p < 0.001). Epiretinal membrane was detected more frequently in group 3 (p = 0.005). Duration and activity of uveitis were similar between groups. SD-OCT was able to detect ME in 90% of the eyes with BU. Duration and activity of uveitis were not related to the compatibility of imaging methods.

Assessment of Retinal Side Effects of Fingolimod (Gilenya) Used in Treatment of Multiple Sclerosis Patients

Background Multiple sclerosis is an inflammatory demyelinating disease that affects CNS. It is thought that multiple factors were implicated in the pathology of MS such as immunological factors, vitamin d deficiency, vitamin b12 deficiency or infection by EBV or HHV6. It has a lot of ocular manifestations as optic neuritis, diplopia, oscillopsia, nystagmus and ophthalmoplegia. Fingolimod is the first approved oral drug for RRMS. Macular edema was reported as a side effect of fingolimod after 4 months of use. Aim of the Work The objectives of the study are screening of retinal side effects of fingolimod used for treatment of relapsing remitting multiple sclerosis patients having no previous ophthalmic complains before starting the treatment. To assess structural changes within the retina using SD-OCT and to correlate these structural changes with the function affection (visual acuity assessment and electrophysiological studies). Patients and Methods Thirty eyes of fifteen patients were included in this prospective one arm clinical trial study. These thirty eyes belonged to multiple sclerosis patients who will be candidate for treatment with fingolimod (Gilenya). Patients were recruited from MS unit, neurology department, Ain Shams University. Results We found that the mean best corrected visual acuity change after treatment slightly decreased but this was non-significant. IOP measurements were not changed after treatment. Despite the changes in visual acuity; fingolimod associated macular edema did not develop. Among 15 patients (30 eyes), 14 eyes have slightly increased MT, 5 eyes have stable MT and 11 eyes have slightly decreased MT. Regarding electrophysiology follow up there were significant change in P1 amplitude in MfERG in ring 1, and it happened without development of macular edema (ME), as 50% of patients had MfERG changes in spite of the absence of ME. So we recommend doing electrophysiological test (MfERG) in follow up protocol of fingolimod treatment. Conclusion Finglimod is an immunomodulatory drug used for treatment of RRMS, it is the first oral drug approved by FDA for MS. Fingolimod associated macular edema is a known complication with a low incidence. Patients on fingolimod should have baseline and 3 to 4 months ophthalmic examination as a part of their protocol OCT is non-invasive technique used for detection of macular edema and follow up, but in our study we found that after 4 months of treatment by fingolimod, affection of retinal function in same patients may happen before macular edema development, which spots the light on the importance of performing the mfERG as well in the follow up of patients who were treated with fingolimod. Further studies are needed to support our results.

Risk factors for the development of macular edema in children with uveitis.

To determine the risk factors for macular edema (ME) in children with uveitis. A retrospective study was conducted of 150 pediatric patients (264 eyes) with uveitis attending 2 tertiary medical centers. Data were collected from the medical files on demographics, type of uveitis, etiology, clinical findings, treatment, and time to development of ME. Risk factors for the development of ME were identified. ME developed in 63 eyes (23.9%) over a mean period of 15.3 ± 2.95 months from diagnosis of uveitis, at a rate of 0.08 eyes per eye-year. On univariate analysis, risk factors for the development of ME were the non-anterior location of the inflammation (p=0.002), band keratopathy (p <0.0001), posterior synechiae (p=0.003), cataract (p=0.002), and vision impairment at presentation (p <0.0001). On multivariate analysis, non-anterior uveitis, which includes intermediate, pan, and posterior-uveitis, and vision impairment retained significance as independent risk factors of ME. Within the pediatric population with uveitis, non-anterior location is associated with the highest risk of ME, followed by the presence of complications, such as band keratopathy and posterior synechiae. These findings indicate a need for close follow-up in children with uveitis for early detection of ME.

A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers.

Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations. Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011-2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R "bibliometrix" package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts. Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021-2022). Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.

Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94–0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90–0.96).

Fundus autofluorescence in the evaluation of diabetic macular edema treatment response.

Fundus autofluorescence (FAF) has shown sensitivity in the detection of macular edema. To evaluate indices formed with FAF and retinal anatomical-functional variables in patients with diabetic macular edema (DME) treated with ziv-aflibercept (ziv-AFL). Twenty-nine eyes of 15 DME patients who received ziv-AFL intravitreal injections were included in the study. Best-corrected visual acuity (BCVA), contrast sensitivity (CS), optical coherence tomography (OCT) and FAF were evaluated before treatment and at one and two months. OCT variables were central subfield thickness (CST), macular volume (MV) and macular cube average thickness (MCAT). FAF/BCVA, FAF/CS, FAF/CST, FAF/MV and AF/MCAT indices baseline values were obtained. Analysis was performed with Spearman's rank correlation coefficient and linear regression analysis. There was a significant correlation between baseline FAF/BCVA index and BCVA at second month (rs = - 0.78, p = 0.000), between baseline FAF/CS index and BCVA at second month (rs = -0.68, p = 0.0009) and between baseline FAF/CS index and MV at first month of follow-up (rs = 0.64, p = 0.002). In DME, composite indices with baseline FAF predict variables such as BCVA in the follow-up of patients receiving ziv-AFL.

Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema

BackgroundMacular edema (ME) is a major complication of retinal disease with multiple mechanisms involved in its development. This study aimed to investigate the metabolite profile of aqueous humor (AH) in patients with ME of different etiologies and identify potential metabolite biomarkers for early diagnosis of ME.MethodsSamples of AH were collected from 60 patients with ME and 20 age- and sex-matched controls and analyzed by liquid chromatography-mass spectrometry (LC/MS)-based metabolomics. A series of univariate and multivariate statistical analyses were performed to identify differential metabolites and enriched metabolite pathways.ResultsThe metabolic profile of AH differed significantly between ME patients and healthy controls, and differentially expressed metabolites were identified. Pathway analysis revealed that these differentially expressed metabolites are mainly involved in lipid metabolism and amino acid metabolism. Moreover, significant differences were identified in the metabolic composition of AH from patients with ME due to different retinal diseases including age-related macular degeneration (AMD-ME), diabetic retinopathy (DME) and branch retinal vein occlusion (BRVO-ME). In total, 39 and 79 etiology-specific altered metabolites were identified for AMD-ME and DME, respectively. Finally, an AH-derived machine learning-based diagnostic model was developed and successfully validated in the test cohort with an area under the receiver operating characteristic (ROC) curve of 0.79 for AMD-ME, 0.94 for DME and 0.77 for BRVO-ME.ConclusionsOur study illustrates the potential underlying metabolic basis of AH of different etiologies across ME populations. We also identify AH-derived metabolite biomarkers that may improve the differential diagnosis and treatment stratification of ME patients with different etiologies.

A Localized Feature Description Means Assisting Diabetic Macular Edema Detection and Classification

A Localized Feature Description Means Assisting Diabetic Macular Edema Detection and Classification

Classification and segmentation of OCT images for age-related macular degeneration based on dual guidance networks

Age-related macular degeneration (AMD) is one of the main causes of visual impairment in elderly people, with drusen and choroidal neovascularization (CNV) being two characterizing types of lesions. Based on optical coherence tomography (OCT), image classification can be used in AMD diagnosis, while image segmentation is necessary for quantitative assessment of the lesion area. In this paper, we propose a deep learning framework exploiting dual guidance between the two tasks. Firstly, a complementary mask guided convolutional neural network (CM-CNN) is proposed to perform classification of OCT B-scans with drusen or CNV from normal ones, where the guiding mask is generated by the auxiliary segmentation task. Secondly, a class activation map guided UNet (CAM-UNet) is proposed to achieve segmentation of drusen and CNV lesions, using CAM output from the CM-CNN. Tested on a subset of the public UCSD dataset, and compared with five classification networks, four segmentation networks, and three multi-task networks, the proposed dual guidance network has achieved higher accuracy both in classification and segmentation. The classification accuracy reaches 96.93% and the Dice coefficient for segmentation reaches 77.51%. Results on an extra dataset for detection of macular edema and segmentation of retinal fluids further show the generalizability of the proposed model.

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

Proteinuria in early referral to spectral domain optical coherence tomography for macular edema detection in type 2 diabetes individuals: results from the Brazilian diabetes study

Background Measurement of retinal thickness by optical coherence tomography (OCT) shows higher diagnostic accuracy for diabetic macular edema (DME) than fundus photography alone. The expanding gap between the rising number of type 2 diabetes (T2D) individuals and the availability of OCT devices demands a targeted selection of individuals at higher risk of DME who would benefit the most from early referral. We sought to appraise if proteinuria should be considered in a targeted referral of T2D individuals to OCT examination. Methods This study was a cross-sectional analysis of 576 consecutive patients enrolled in the Brazilian Diabetes Study between June/2016 and December/2021 who underwent OCT exam and urinalysis to assess ME and proteinuria status, respectively. Differences in the prevalence of DME between proteinuria groups and across a range of diabetic retinopathy (DR) stages were evaluated. Results Among 1134 eyes included in this analysis, the prevalence of proteinuria was 22% and 18.2% of eyes had DME. Proteinuria was related to an increased prevalence of DME (13.2% vs 38.7% for control vs proteinuria, respectively; p < .001), with an OR of 4.08 [95% confidence interval (CI): 2.50–6.64, p < .001), after adjustment for covariates. Proteinuria was independently related to DME also among eyes with non-apparent DR [OR: 2.82; 95%CI: 1.34–5.93; p = .003] and non-proliferative DR (OR of 5.94, 95%CI 2.13–16.62, p < .001). Fundus photography spotted only half of the DME cases detected by OCT. Conclusion In T2D individuals, early referral to OCT examination should be pursued for all individuals with concurrent proteinuria. Trial registration ClinicalTrials.gov Identifier: NCT04949152

The compatibility between optical coherence tomography and fundus fluorescein angiography in the detection of macular edema involving the fovea

Objective: To investigate the compatibility between optical coherence tomography (OCT) and fundus fluorescein angiography (FFA) in the detection of macular edema (ME) excluding subretinal fluid (SRF) involving the fovea. Materials and methods: In this retrospective study in the Ophthalmology Department at our university hospital, interpretation of OCT and FFA images was performed for 200 eyes that underwent simultaneous FFA and OCT to rule out ME. The cases having SRF were excluded from the study. Results: Of 200 eyes, 193 eyes (96.5%) had the finding of ME confirmed by both techniques. There were 2 eyes (1%) for which FFA revealed ME involving fovea with hyperfluorescence in the macula while OCT demonstrated no intraretinal edema or subretinal fluid in the macula. For 5 eyes in the study (2.5%), OCT revealed intraretinal edema in the fovea, while these were not detected by FFA. Conclusion: This study suggests that FFA and OCT are highly sensitive techniques for the detection of ME and they correlate with each other. The little discrepancy between OCT and FFA for determining ME may be due to intracellular or extracellular edema and fast or slower development of retinal edema.

Assessment for Macular Thickness after Uncomplicated Phacoemulsification Using Optical Coherence Tomography.

PurposeMacular edema including cystoid macular edema is one of the main causes of unfavorable visual outcomes after cataract surgery. The macular thickness and the occurrence of macular edema after uncomplicated cataract surgery was evaluated using optical coherence tomography (OCT) in this study.MethodsMacular map images were taken by OCT before surgery and at 1 week, 1 month, and 2 months postsurgery. The subjects were classified into two groups (group 1, patients with no macular edema; group 2, patients with macular edema). Group 2 was defined as increase in central macular thickness (CMT) by 30% compared with that before surgery. The risk factors for macular edema were evaluated. Group 2 was divided into two subgroups: subclinical macular edema (group 2A) and cystoid macular edema (group 2B) and they were assessed in terms of the clinical course of best-corrected visual acuity and CMT.ResultsA total of 376 patients were enrolled in this study, of which 36 (9.57%, group 2) showed macular edema measured by OCT after the surgery. Univariate analysis for group 1 and 2 revealed that intracameral injection of epinephrine during phacoemulsification was associated with the development of macular edema. In group 2, five patients (1.33%) developed cystoid macular edema. Statistically significant differences in the clinical course of CMT were observed at 2 months (201.2 ± 23.1, 250.0 ± 29.8, and 371.0 ± 160.3 in group 1, group 2A, and group 2B, respectively; p < 0.001) and 1 month postoperatively (198.5 ± 23.6, 237.8 ± 40.9, and 314.0 ± 104.5 in group 1, group 2A, and group 2B, respectively; p < 0.001). Group 2B required additional treatment and eventually achieved best-corrected visual acuity of >0.2 with CMT in the normal range.ConclusionsThe intracameral injection of epinephrine may cause macular edema after uncomplicated cataract surgery. Examination of CMT using OCT is recommended for the early detection of macular edema.

Comparative Analysis of Optical Coherence Tomographic Study of Macula in Preoperative and Postoperative Diabetic Patients Undergoing Small Incision Cataract Surgery

Background and Aim: Macular oedema (ME) occurs in a variety of pathological conditions and accounts for differentdegrees of vision loss. Early detection of ME is therefore critical for diagnosis and management. Optical CoherenceTomography (OCT) is particularly useful and accurate for measuring retinal nerve fiber layer (RNFL) thickness andmacular thickness. This study is undertaken to evaluate the quantitative changes in macular thickness using spectraldomain optical coherence tomography in diabetic patients undergoing cataract surgery pre and post operatively and itsrelation with diabetic retinopathy.Material and Methods: Study participants included 100 diabetic patients irrespective of presence or absence of retinopathywho underwent cataract surgery. Each eye underwent fundus examination with indirect ophthalmoscopy and OCT ofmacula i.e.,preoperatively and at postoperatively at day 1, 1 week, 4 weeks and at 12 weeks. Best-corrected visual acuity(BCVA) was recorded at each visit.Results: Visual acuity improved postoperatively at 4th and 12th week after small incision cataract surgery to 6/12 and 6/9in majority of the patients. Post operatively 1 month 75% patients achieved vision of 6/6– 6/12. Mean values of centralsubfield macular thickness at preoperatively was 268.9 μm which increased to 278.2 μm at postoperative day 1, 281.6 μmat postoperative 1 week, 286.1 μm at postoperative 4 weeks and 297.2 μm at postoperative 12 weeks.Conclusion: There was no difference in median macular thickness between the groups, and no cases in either group hadan increase in macular thickness to reach this threshold. diabetic macular edema generally worsens after cataract surgery,and that the worsening of macular edema is more prominent in eyes with DR.

Screening strategy in ocular diabetes with automatic detection system in the Chilean public health system

Summary: Diabetic retinopathy (DR) is the leading cause of blindness among working-age persons in high-income countries. A public system strategy was developed to improve screening, using telemedicine, automatic detection using artificial intelligence (A/I) and medical reporting. In the current work, we evaluated program efficiency. Material: We conducted a cross-sectional study using information from an institutional database of retinographies submitted to the A/I platform in 2019. With a positive test, a medical report was made using the international scale.Results: In 2019, 220,994 retinographies were reported, corresponding to 24.0% of diabetic patients. Around half (53.0%) of cases were discarded by A/I, being different in each regional health service. The medical analysis discarded diabetic retinopathy in 30.2% of exams, 11.5% had diabetic retinopathy, including 2.3% with risk of blindness, while 3.7% could not be evaluated.Discussion: The use of A/I allowed optimizing the medical resources, discarded 53% of cases, which helped in the screening of diabetic retinopathy. Coverage is still insufficient, and detection of macular edema must be improved.

Metabolomics Analysis of the Aqueous Humour Uncovers Potential Metabolite Biomarkers for Early Differential Detection of Macular Edema

Metabolomics Analysis of the Aqueous Humour Uncovers Potential Metabolite Biomarkers for Early Differential Detection of Macular Edema

Correlation of Volume of Macular Edema with Retinal Tomography Features in Diabetic Retinopathy Eyes.

Optical coherence tomography (OCT) enables the detection of macular edema, a significant pathological outcome of diabetic retinopathy (DR). The aim of the study was to correlate edema volume with the severity of diabetic retinopathy and response to treatment with intravitreal injections (compared to baseline). Diabetic retinopathy (DR; n = 181) eyes were imaged with OCT (Heidelberg Engineering, Germany). They were grouped as responders (a decrease in thickness after intravitreal injection of Bevacizumab), non-responders (persistent edema or reduced decrease in thickness), recurrent (recurrence of edema after injection), and treatment naïve (no change in edema at follow-up without any injection). The post-treatment imaging of eyes was included for all groups, except for the treatment naïve group. All eyes underwent a 9 × 6 mm raster scan to measure the edema volume (EV). Central foveal thickness (CFT), central foveal volume (CFV), and total retinal volume (TRV) were obtained from the early treatment diabetic retinopathy study (ETDRS) map. The median EV increased with DR severity, with PDR having the greatest EV (4.01 mm3). This correlated positively with TRV (p < 0.001). Median CFV and CFT were the greatest in severe NPDR. Median EV was the greatest in the recurrent eyes (4.675 mm3) and lowest (1.6 mm3) in the treatment naïve group. Responders and non-responders groups had median values of 3.65 and 3.93 mm3, respectively. This trend was not observed with CFV, CFT, and TRV. A linear regression yielded threshold values of CFV (~0.3 mm3), CFT (~386 µm), and TRV (~9.06 mm3), above which EV may be detected by the current scanner. In this study, EV provided a better distinction between the response groups when compared to retinal tomography parameters. The EV increased with disease severity. Thus, EV can be a more precise parameter to identify subclinical edema and aid in better treatment planning.