Detection Of Macular Edema Research Articles

Detection of Macular Edema in Acute Phase through Optical Coherence Tomography using Local Binary Pattern Feature

Diabetic macular edema is one of the major causes for blindness and can be detected early by using OCT imaging technique. Optical Coherence Tomography is a fundamentally novel technique of optical imaging modality. In this paper we are mainly focused to extract local features by using different methods for detection of DME followed by its classification. Used the image processing techniques of removal of the speckle noise, proper alignment followed by local feature extraction by using Local binary pattern .Then extracted features are classified using SVM classifier. In this study for testing 30 OCT images datasets in that 15 normal and 15 DME images were used for classification. We have used SVM classifier it gives the best results, likes 98, 98 and 98.77 for specificity, sensitivity and accuracy respectively. Here SVM classifier with LBP features detected the diseases with an accuracy of 98.77% our proposed method shows that the SVM classifier with LBP features gives a better improved performance.

Infrared retinal images for flashless detection of macular edema

This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (AZ) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

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

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. 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. 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. 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.

Cost-effectiveness and diagnostic accuracy of telemedicine in macular disease and diabetic retinopathy: A systematic review and meta-analysis.

To determine cost-effectiveness and the diagnostic accuracy of teleophthalmology (TO) in the detection of macular edema (ME) and various grades of diabetic retinopathy (DR). MEDLINE, EMBASE, and Cochrane databases were searched for TO, ME, and DR on May 25, 2016. The search was updated on April 2, 2019. Pooled sensitivity and specificity for ME and various grades of DR were determined using Meta-Disc software. A systematic review of the articles discussing the cost-effectiveness of TO screening was also performed. Thirty-three articles on the diagnostic accuracy and 28 articles on the cost-effectiveness were selected. Telescreening is moderately sensitive but very specific for the diagnosis of diabetic retinopathy. Non-mydriatic Teleretinal screening services are cost-effective, decrease clinics workload, and increase patient compliance if provided free of cost in remote low socioeconomic regions.

Relation Between Retinal Vasculature and Retinal Thickness in Macular Edema.

This study has investigated the relationship of the retinal vasculature and the retinal thickness for Macular Edema (ME) subjects. Ninety sets of Fluorescein Angiograph (FA) and Optical Coherence Tomography (OCT) of 54 participants were analyzed. Multivariate analysis using the binary logistic regression model was used to study the association between vessel parameters and retinal thickness. The results reveal retinal vessel feature i.e. fractal dimension (FD) as the most sensitive parameter to the changes in retinal thickness associated with ME. Thus, indicating a direct relationship between the retinal vasculature and retinal thickness which is caused due to neovascular causing exudates, leakages and hemorrhages, with applications for alternate modality for detection of ME.

Validation of Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening.

Screening for diabetic retinopathy (DR) is cost-effective when compared with disability loss for those who go blind in the absence of a screening program. We aimed to evaluate the sensitivity and specificity of a smartphone-based device for the screening and detection of DR. A cross-sectional study of 220 patients with diabetes (440 eyes, all patients age 25 years or older) was completed. Tropicamide 0.5% was used for iris dilation followed by an indirect ophthalmoscopy using a 20-D lens. Retinal images were later obtained using a smartphone attached to an adaptable camera device. Retinal images permitted the visualization of the macular and papillary regions and were sent without compression via the internet to a retinal specialist for interpretation. Sensitivity and specificity were calculated for all cases and stages of DR. Using our standard examination method, the prevalence of DR and macular edema were 13.6% and 6.4%, respectively. With the smartphone-based retinal camera, the prevalence of DR and macular edema were 18.2% and 8.2%, respectively. Sensitivity and specificity for the detection of all stages of DR was 73.3% and 90.5%, respectively. For the detection of macular edema, sensitivity was 77.8%, and specificity was 95%. For severe nonproliferative DR (NPDR), sensitivity and specificity were 80% and 99%, respectively; for proliferative DR (PDR), they were both 100%. In the early stages of DR, specificity was 89.8% for mild NPDR and 97.1% for moderate NPDR. Sensitivity was 57.1% and 42.9%, respectively. Screening for DR using a smartphone-based retinal camera has a satisfactory specificity at all DR stages. Its sensitivity seems to be high only in the stages of DR necessitating a specific therapeutic decision (eg, macular edema, severe NPDR, and PDR). A smartphone-based retinal camera may be a useful device to screen for DR in resource-limited settings. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:S18-S22.].

Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images

Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular diseases. Optical coherence tomography (OCT) imaging is the most efficient technique used to observe eye diseases. This paper proposes an efficient algorithm to automatically classify normal as well as disease-affected (macular edema) retinal OCT images by using segmentation of Inner Limiting Membrane and the Choroid Layer. In the proposed method, preprocessing of the input image is done to improve the quality and reduce the speckle noise. The layer segmentation is done on the gradient image, and graph theory and dynamic programming algorithm is performed. The feature vectors from segmented image are in terms of thickness profile and cyst fluid parameter, and these features are applied to various classifiers. The proposed method was tested with the standard dataset collected from the Department of Ophthalmology, Duke University, and achieved a high accuracy rate of 99.4975%, sensitivity of 100%, and specificity of 99% for the SVM classifier. An efficient algorithm is proposed for macular edema detection from OCT images using segmentation based on graph theory and dynamic programming algorithm. The comparison with alternative methods yielded results that demonstrate the superiority of the proposed algorithm for macular edema detection.

Remote examination of exudates-impact of macular oedema.

One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.

Fundus Fluorescein Angiography Versus Optical Coherence Tomography Angiography in Evaluation of Retinal Changes in Cases of Retinal Vein Occlusion

AbstractBackground: Retinal Vein Occlusion (RVO) is the second most common retinal vascular disease and the fifth common cause of visual loss worldwide. Fundus Fluorescein Angiog-raphy (FFA) is the gold standard investigation for its evaluation. However, it has some limitations. Optical Coherence Tomog-raphy Angiography (OCTA) is a novel technique for evaluation of retinal vascular changes. It may overcome some of the limitations of FFA.Objective: Comparison between FFA and OCTA in eval-uation of retinal changes in retinal vein occlusion.Methods: This prospective observational case series study was carried out on 30 eyes of 29 patients attending Ophthal-mology Outpatient Clinic in Tanta University Hospital from July 2016 to May 2017. FFA and OCTA were done to all patients at time of diagnosis. The results of both modalities were compared regarding macular ischemia, macular edema, microaneurysms, collaterals and neovascularizations.Results: OCTA detected macular ischemia in the superficial plexus of 17 cases while it detected ischemia in the deep plexus of 25 cases. FFA detected macular ischemia in 9 cases and couldn't comment on ischemia in 5 cases. FFA detected macular edema in 23 cases while OCTA detected only 20 cases. Both collaterals and neovascularizations were detected in 3 separate cases by both OCTA and FFA. Microaneurysms were detected in 8 cases by FFA and in 4 cases by OCTA.Conclusion: OCTA was superior to FFA in detection of macular ischemia. Both modalities were equal in detection of macular edema, collaterals and neovascularizations. FFA was superior in detection of microaneurysms.

Fundus Images-Based Detection and Grading of Macular Edema Using Robust Macula Localization

The macula is an oval-shaped area near the center of the human retina, and at its center, there is a small pit known as the fovea. The fovea contains large concentrations of cone cells and is responsible for sharp, colored vision. Macular disorders are the group of diseases that damage the macula, resulting in blurred vision or even blindness. Macular edema (ME), one of the most common types of macular disorder, is caused by fluid accumulation beneath the macula. In this paper, we present an automated system for the detection of ME from fundus images. We introduce a new automated system for the detailed grading of the severity of disease using knowledge of exudates and maculae. A new set of features is used along with a minimum distance classifier for accurate localization of the fovea, which is important for the grading of ME. The proposed system uses different hybrid features and support vector machines for segmentation of exudates. The detailed grading of ME—as both clinically significant ME and non-clinically significant ME—is done using localized foveae and segmented exudates. The proposed algorithm is validated using public and local data sets. We have achieved an average accuracy of 96.1% in the detection and grading of ME with our proposed method.

Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces

Background and objectivesMacular diseases tend to damage macula within human retina due to which the central vision of a person is affected. Macular edema (ME) and central serous retinopathy (CSR) are two of the most common macular diseases. Many researchers worked on automated detection of ME from optical coherence tomography (OCT) and fundus images, whereas few researchers have worked on diagnosing central serous retinopathy. But this paper proposes a fully automated method for the classification of ME and CSR through robust reconstruction of 3D OCT retinal surfaces. MethodsThe proposed system uses structure tensors to extract retinal layers from OCT images. The 3D retinal surface is then reconstructed by extracting the brightness scan (B-scan) thickness profile from each coherent tensor. The proposed system extracts 8 distinct features (3 based on retinal thickness profile of right side, 3 based on thickness profile of left side and 2 based on top surface and cyst spaces within retinal layers) from 30 labeled volumes (10 healthy, 10 CSR and 10 ME) which are used to train the supervised support vector machines (SVM) classifier. ResultsIn this research we have considered 90 OCT volumes (30 Healthy, 30 CSR and 30 ME) of 73 patients to test the proposed system where our proposed system correctly classified 89 out of 90 cases and has promising receiver operator characteristics (ROC) ratings with accuracy, sensitivity and specificity of 98.88%, 100%, and 96.66% respectively. ConclusionThe proposed system is quite fast and robust in detecting all the three types of retinal pathologies from volumetric OCT scans. The proposed system is fully automated and provides an early and on fly diagnosis of ME and CSR syndromes. 3D macular thickness surfaces can further be used as decision support parameter in clinical studies to check the volume of cyst.

Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images.

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

Automated segmentation of subretinal layers for the detection of macular edema.

Macular edema (ME) is considered as one of the major indications of proliferative diabetic retinopathy and it is commonly caused due to diabetes. ME causes retinal swelling due to the accumulation of protein deposits within subretinal layers. Optical coherence tomography (OCT) imaging provides an early detection of ME by showing the cross-sectional view of macular pathology. Many researchers have worked on automated identification of macular edema from fundus images, but this paper proposes a fully automated method for extracting and analyzing subretinal layers from OCT images using coherent tensors. These subretinal layers are then used to predict ME from candidate images using a support vector machine (SVM) classifier. A total of 71 OCT images of 64 patients are collected locally in which 15 persons have ME and 49 persons are healthy. Our proposed system has an overall accuracy of 97.78% in correctly classifying ME patients and healthy persons. We have also tested our proposed implementation on spectral domain OCT (SD-OCT) images of the Duke dataset consisting of 109 images from 10 patients and it correctly classified all healthy and ME images in the dataset.

Discrepancy in Fluorescein Angiography and Optical Coherence Tomography Findings of Macular Edema in Intermediate Uveitis

ABSTRACTPurpose: To assess discrepancies between fluorescein angiography (FA) and optical coherence tomography (OCT) findings for macular edema (ME) in intermediate uveitis, and investigate treatment response differences corresponding to the discrepant patterns.Methods: Discrepant results for ME detection between FA and OCT were evaluated. After 6 months, differences in treatment responses were analyzed.Results: Discrepant findings for ME were found in 21 (44%) of 48 eyes. The best-corrected visual acuity significantly improved after treatment in the concordant group but not in the discrepant groups. Central retinal thickness significantly decreased after treatment in the FA+/OCT+ and FA−/OCT+ groups but not in the FA+/OCT− group. Disease duration was significantly longer in the FA+/OCT− group than in the other groups.Conclusions: Discrepancies in FA and OCT findings for ME were common in patients with intermediate uveitis. Our results suggest that treatment outcomes may differ according to the discrepant patterns.

DEXAMETHASONE IMPLANT FOR MACULAR EDEMA SECONDARY TO ISCHEMIC RETINAL VEIN OCCLUSIONS

To evaluate the effects of dexamethasone implant for the treatment of macular edema secondary to ischemic retinal vein occlusions (IRVO). Consecutive patients affected by macular edema related to IRVO were prospectively recruited. After a comprehensive ophthalmological examination, including visual acuity, fluorescein angiography, and optical coherence tomography, each patient received a first implant. Further re-treatments were performed on the basis of macular edema detection from the fourth month on. The primary outcome measure was the change in Early Treatment Diabetic Retinopathy Study letter score at the 12-month examination. Secondary outcome measures included changes in central foveal thickness and number of implants over the follow-up. Fifteen patients with central IRVO and 14 patients with branch IRVO were recruited. The median ETDRS letter score at baseline was 10 (Snellen Equivalent 20/640) in the central IRVO subgroup, improving to 37 (Snellen Equivalent 20/200²⁺) at the 12-month examination. The median ETDRS letter score in the branch retinal vein occlusion subgroup changed from 48 (Snellen equivalent 20/125³⁺) at baseline to 62 (Snellen equivalent 20/63²⁺) at the 12-month examination. The baseline median central foveal thickness was 749 μm in central IRVO, and 459 μm in branch IRVO, improving to 363 μm and 323 μm, respectively, at the 12-month examination. Mean number of implants was 2.8 in the central IRVO and 2 in the branch IRVO. Dexamethasone implant can reduce macular edema in eyes affected by IRVO, leading to a slight improvement in visual acuity.

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Macular edema is an advance stage of diabetic retinopathy which affects central vision of diabetes patients. The main cause of edema is the appearance of exudates near or on macular region in human retina. If the exudates are present in the macular region of retina, it will lead to diabetic macular edema. Early detection of macular edema in diabetic patients paves a path for prevention from blindness. The automatic system for early detection of diabetic macular edema should identify all possible exudates present on the surface of retina. In the proposed work, a computer added diagnosis system is design for the identification of the exudates in color retinal fundus images. The system consists of three stages; candidate exudates detection, feature extraction and classification. The system is designed with (i) background estimation, morphological reconstruction, normalization for candidate exudates detection (ii) Gray level co-occurrence matrix for feature extraction and (iii) support vector machine for classification. The classifier classifies in between the region of exudates and non-exudates. The system performance is evaluated in terms of the parameters such as sensitivity, specificity, mathews correlation coefficient, positive predicative value, and accuracy whose values are 88.23%, 100%, 88.23%, 100%, 93.75% respectively.

Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy.

Diabetic macular oedema (DMO) is a thickening of the central retina, or the macula, and is associated with long-term visual loss in people with diabetic retinopathy (DR). Clinically significant macular oedema (CSMO) is the most severe form of DMO. Almost 30 years ago, the Early Treatment Diabetic Retinopathy Study (ETDRS) found that CSMO, diagnosed by means of stereoscopic fundus photography, leads to moderate visual loss in one of four people within three years. It also showed that grid or focal laser photocoagulation to the macula halves this risk. Recently, intravitreal injection of antiangiogenic drugs has been investigated to try to improve vision in people with macular oedema due to DR.Optical coherence tomography (OCT) is based on optical reflectivity and is able to image retinal thickness and structure producing cross-sectional and three-dimensional images of the central retina. It is already widely used because it provides objective and quantitative assessment of macular oedema unlike the subjectivity of fundus biomicroscopic assessment, which is routinely used by ophthalmologists instead of photography. Optical coherence tomography is also used for quantitative follow up of the effects of treatment of CSMO. Disadvantages of using OCT are the cost to purchase it and the need for trained personnel to perform the examinations.To determine the diagnostic accuracy of OCT for detecting macular oedema in people with DR. A secondary objective is to compare the diagnostic accuracy by study-specific characteristics, such as factors related to methodology, patients or OCT.We searched the Cochrane Database of Systematic Reviews (CDSR), the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment Database (HTA) and the NHS Economic Evaluation Database (NHSEED) (The Cochrane Library 2011, Issue 5), MEDLINE (January 1950 to May 2011), EMBASE (January 1950 to May 2011), ISI Web of Science (January 1970 to May 2011), BIOSIS Previews (January 1969 to May 2011), MEDION and the Aggressive Research Intelligence Facility database (ARIF). There were no date or language restrictions in the electronic search for trials. The electronic databases were last searched on 16 May 2011. We checked bibliographies of relevant studies for additional references.We selected studies that assessed the diagnostic accuracy of any OCT model for detecting DMO or CSMO in patients with DR who were referred to eye clinics. Diabetic macular oedema and CSMO were diagnosed by means of fundus biomicroscopy by ophthalmologists or stereophotography by ophthalmologists or other trained personnel.Three authors independently extracted data on study characteristics and measures of accuracy. We assessed data using random-effects hierarchical sROC meta-analysis models.Nine studies (768 participants, 1325 eyes) were included. Prevalence of CSMO was 19% to 65% (median 50%). Study quality was good for half the QUADAS items, whereas unclear or inadequate quality was found in some studies regarding selection criteria, index and reference test masking (blinding), availability of clinical information, uninterpretable results and withdrawals. There was a specific 'unit of analysis' issue because both eyes of the majority of participants were included in the analyses as if they were independent.Central CSMO was the target condition in all but one study and thus our results cannot be applied to non-central CSMO. In eight studies providing data on CSMO (697 participants, 1241 eyes), pooled sensitivity was 0.78 (95% confidence interval (CI) 0.72 to 0.83) and specificity was 0.86 (95% CI 0.76 to 0.93). The median central retinal thickness cut-off we selected for data extraction was 250 µm (range 230 µm to 300 µm).Data from three studies reporting accuracy for detection of DMO (180 participants, 343 eyes) were not pooled. Sensitivities and specificities were about 0.80 in two studies and were both 1.00 in the third study.Central retinal thickness measured with OCT cannot be used as a stand-alone test to diagnose the central type of CSMO and decide on the use of laser photocoagulation in patients who are referred to retina clinics. In fact, there is a substantial disagreement of OCT with the ETDRS definition of CSMO based on clinical examination. Some researchers have observed that OCT can detect macular thickening earlier than clinical examination, but also found that such cases did not necessarily progress to CSMO and need photocoagulation.Care should be taken in applying the conclusions of this review to other test-treatment pathways. In fact, OCT will become an essential tool to manage antiangiogenic therapy, an expanding therapeutic option for patients with macular oedema due to DR, because OCT is a component of the diagnostic algorithms of studies on this new treatment.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.

Optical coherence tomography imaging of macular oedema

Macular oedema (ME) occurs in a wide variety of pathological conditions and accounts for different degrees of vision loss. Early detection of ME is therefore critical for diagnosis and therapeutic...

SAFETY AND EFFICACY OF ORAL FLUORESCEIN ANGIOGRAPHY IN DETECTING MACULAR EDEMA IN COMPARISON WITH SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY

To evaluate the safety of oral fluorescein angiography (FA) and to compare its efficacy in detection of macular edema (ME) with spectral-domain optical coherence tomography (SD-OCT). Results of imaging studies for 1,928 eyes of 1,019 patients who had simultaneously undergone both oral FA and SD-OCT by a confocal laser ophthalmoscope were reviewed. Sensitivity in detecting ME, discrepancy rate, and "kappa" agreement were determined for both the techniques and with eyes stratified by disease diagnosis. No allergic reactions occurred after oral FA. Mild gastric discomfort was noted in <1% of the patients; 1,840 eyes (95.4%) showed concordance between the two techniques, and kappa agreement was 90.3%. For ME, oral FA showed an overall sensitivity of 0.97 and SD-OCT of 0.91. Equivalent sensitivity was found in cases of wet age-related macular degeneration (0.99). Oral FA was more sensitive than SD-OCT in cases of retinovascular diseases. The SD-OCT showed higher sensitivity in cases of macular holes. Detection of ME by SD-OCT was significantly higher in cases of intense leakage on oral FA (P < 0.001). Oral FA proved to be a safe and an adequate technique to evaluate ME. It is more sensitive than SD-OCT in detection of ME in cases of retinovascular diseases but can fail to detect ME in cases of macular holes. A noninvasive examination with simultaneous oral FA and SD-OCT may be considered to obtain a comprehensive evaluation of the presence of ME from different pathologies.