High-resolution stereoscopic digital fundus photography versus contact lens biomicroscopy for the detection of clinically significant macular edema

Ophthalmology ◽  
2002 ◽  
Vol 109 (2) ◽  
pp. 267-274 ◽  
Author(s):  
Christopher J Rudnisky ◽  
Brad J Hinz ◽  
Matthew T.S Tennant ◽  
Alexander R de Leon ◽  
Mark D.J Greve
2016 ◽  
Vol 7 (2) ◽  
pp. 142-147
Author(s):  
Barsha Suwal ◽  
Jeevan Kumar Shrestha ◽  
Sagun Narayan Joshi ◽  
Ananda Kumar Sharma

Introduction: Diabetic retinopathy is the commonest micro vascular complication in patients with diabetes and remains a leading cause of blindness in people of working age group. Objective: to determine the prevalence of clinically significant macular edema (CSME) and the influence of systemic risk factors Materials and methods: It is a hospital based comparative study conducted in 220 eyes of 110 diabetic patients. DR was graded according to International Clinical Diabetic Retinopathy Severity Scale and CSME was defined according to Early Treatment Diabetic Retinopathy Study (ETDRS) system. The patients were grouped as 1) CSME group (DR and CSME in one or both eyes) and 2) Non- CSME group(CSME in none of the eyes but with any grade of DR).Level of glycosylated hemoglobin (HbA1C), serum total cholesterol, triglyceride (TG), low density lipoprotein (LDL), high density lipoprotein (HDL) and urine for albumin were studied in both groups. Results: CSME was present in 36% of 110 patients. Poor glycemic control and high total cholesterol level showed positive association with CSME (p<0.05). LDL and TG levels were higher and HDL lower in CSME group. However, no statistical significance was found. Conclusion: The CSME is significantly associated with poorer glycemic control and elevated total cholesterol level.


Author(s):  
Marieta Dumitrache ◽  
Rodica Lascu

Management in D.R. through prophylactic treatment (maintaining a glycemic level as close as possible to normal, control hypertension <150/85 mmHg, hyperlipidemia) and curative treatment of D.R. does not cure the disease, but may slow the evolution of D.M. and D.R. AntiVEGF agents are indicated as adjuvant therapy in pan-photocoagulation laser and / or vitrectomy in patients with DR to block angiogenesis by inhibiting VEGF. All antiVEGF agents are an effective treatment for the clinically significant macular edema. Photocoagulation laser is a treatment of choice in preproliferative and proliferative DR and an effective treatment of diabetic macular edema. The indications for laser treatment in diabetic retinopathy are related to the incidence, evolution of neovessels, duration of diabetes, HbA1c level, presence of macular edema, stage of DR. The laser for macular lesions reduces the risk of vision loss in the eyes with incipient and moderate non-proliferative DR and macular edema concomitant; the laser should be applied to all patients with clinically significant macular edema. Vitrectomy in proliferative DR is indicated in vitreous hemorrhage, tractional retinal detachment in order to remove the vitreous hermorrhage and excision of tractional preretinal membranes.


Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2970 ◽  
Author(s):  
Bilal Hassan ◽  
Taimur Hassan ◽  
Bo Li ◽  
Ramsha Ahmed ◽  
Omar Hassan

Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.


2006 ◽  
Vol 41 (6) ◽  
pp. 727-732 ◽  
Author(s):  
Christopher J. Rudnisky ◽  
Matthew T.S. Tennant ◽  
Alexander R. de Leon ◽  
Bradley J. Hinz ◽  
Mark D.J. Greve

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