Role of Vasomotion in Control of Retina Edema in Diabetic Retinopathy: Quantification of Fluid Transport Through Retinal Capillaries

2008 ◽  
Author(s):  
Liang Zhu ◽  
Robert Flower
Keyword(s):  
Diabetic Retinopathy ◽  
Fluid Transport ◽  
Vision Loss ◽  
Early Screening ◽  
Retinal Tissue ◽  
Visual Damage ◽  
Prevention Of Diabetes ◽  
Irreversible Nature ◽  
Retinal Capillaries

Diabetic retinopathy refers to diabetes-related complications in the retina, It is a progressive disease and its symptoms in the eyes can vary from non-vision threatening to vision loss, and it can lead to permanent damage to the neuronal retinal tissue. The irreversible nature of the damage suggests that prevention of diabetes by eliminating risk factors and early screening are the cornerstone of relevant treatment to stop or limit visual damage in those patients.

scholarly journals Rho-Kinase Inhibitors for the Treatment of Refractory Diabetic Macular Oedema

Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1683
Author(s):  
Milagros Mateos-Olivares ◽  
Luis García-Onrubia ◽  
Fco. Javier Valentín-Bravo ◽  
Rogelio González-Sarmiento ◽  
Maribel Lopez-Galvez ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Oedema ◽  
Standard Therapy ◽  
Vision Loss ◽  
Therapeutic Potential ◽  
Rho Kinase ◽  
Diabetic Macular Oedema ◽  
New Drugs ◽  
Anti Vegf

Diabetic macular oedema (DMO) is one of the leading causes of vision loss associated with diabetic retinopathy (DR). New insights in managing this condition have changed the paradigm in its treatment, with intravitreal injections of antivascular endothelial growth factor (anti-VEGF) having become the standard therapy for DMO worldwide. However, there is no single standard therapy for all patients DMO refractory to anti-VEGF treatment; thus, further investigation is still needed. The key obstacles in developing suitable therapeutics for refractory DMO lie in its complex pathophysiology; therefore, there is an opportunity for further improvements in the progress and applications of new drugs. Previous studies have indicated that Rho-associated kinase (Rho-kinase/ROCK) is an essential molecule in the pathogenesis of DMO. This is why the Rho/ROCK signalling pathway has been proposed as a possible target for new treatments. The present review focuses on the recent progress on the possible role of ROCK and its therapeutic potential in DMO. A systematic literature search was performed, covering the years 1991 to 2021, using the following keywords: “rho-Associated Kinas-es”, “Diabetic Retinopathy”, “Macular Edema”, “Ripasudil”, “Fasudil” and “Netarsudil”. Better insight into the pathological role of Rho-kinase/ROCK may lead to the development of new strategies for refractory DMO treatment and prevention.

scholarly journals Diabetic retinopathy: treatment and prevention

2007 ◽  
Vol 4 (3_suppl) ◽  
pp. S9-S11 ◽  
Author(s):  
Paul M Dodson
Keyword(s):  
Diabetic Retinopathy ◽  
Standard Therapy ◽  
Vision Loss ◽  
Early Stage ◽  
Developed Countries ◽  
Relative Reduction ◽  
Treatment And Prevention ◽  
Working Age ◽  
Capillary Occlusion

Diabetic eye disease is the major cause of blindness and vision loss among working-age people in developed countries. Microangiopathy and capillary occlusion underlie the pathogenesis of disease. While laser treatment is regarded as the standard therapy, intensive medical management of glycaemia and hypertension is also a priority in order to reduce the risk of diabetic retinopathy. Recent data have prompted a re-evaluation of the role of lipid-modifying therapy in reducing diabetic retinopathy. The Fenofibrate Intervention for Event Lowering in Diabetes (FIELD) study demonstrated a significant 30% relative reduction in the need for first retinal laser therapy in patients with (predominantly early-stage) type 2 diabetes treated with fenofibrate 200 mg daily, from 5.2% with placebo to 3.6% with fenofibrate, p=0.0003. The benefit of fenofibrate was evident within the first year of treatment. These promising data justify further evaluation of the mechanism and role of fenofibrate, in addition to standard therapy, in the management of diabetic retinopathy.

scholarly journals Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Silky Goel ◽  
Siddharth Gupta ◽  
Avnish Panwar ◽  
Sunil Kumar ◽  
Madhushi Verma ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Fundus Photography ◽  
Huge Amount ◽  
Early Screening ◽  
Image Metadata ◽  
Very High

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.

scholarly journals Screening for Diabetic Retinopathy in Endocrinology Clinics by Using Handheld Cameras and Applying Artificial Intelligence Algorithms

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A419-A420
Author(s):  
Zack Dvey-Aharon ◽  
Petri Huhtinen
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Ground Truth ◽  
World Health ◽  
Diabetic Patients ◽  
Early Screening ◽  
Exclusion Criteria ◽  
Fundus Camera ◽  
The World

Abstract According to estimations of the World Health Organization (WHO), there are almost 500M people in the world that suffer from diabetes. Projections suggest this number will surpass 700M by 2045 with global prevalence surpassing 7%. This huge population, alongside people with pre-diabetics, is prone to develop diabetic retinopathy, the leading cause of vision loss in the working age. While early screening can help prevent most cases of vision loss caused by diabetic retinopathy, the vast majority of patients are not being screened periodically as the guidelines instruct. The challenge is to find a reliable and convenient method to screen patients so that efficacy in detection of referral diabetic retinopathy is sufficient while integration with the flow of care is smooth, easy, simple, and cost-efficient. In this research, we described a screening process for more-than-mild retinopathy through the application of artificial intelligence (AI) algorithms on images obtained by a portable, handheld fundus camera. 156 patients were screened for mtmDR indication. Four images were taken per patient, two macula centered and two optic disc centered. The 624 images were taken using the Optomed Aurora fundus camera and were uploaded using Optomed Direct-Upload. Fully blinded and independently, a certified, experienced ophthalmologist (contracted by Optomed and based in Finland) reviewed each patient to determine ground truth. Indications that are different than mtmDR were also documented by the ophthalmologist to meet exclusion criteria. Data was obtained from anonymized images uploaded to the cloud-based AEYE-DS system and analysis results from the AI algorithm were promptly returned to the users. Of the 156 patients, a certified ophthalmologist determined 100% reached sufficient quality of images for grading, and 36 had existing retinal diseases that fall under exclusion criteria, thus, 77% of the participants met the participation criteria. Of the remaining 120 patients, the AEYE-DS system determined that 2 patients had at least one insufficient quality image. AEYE-DS provided readings for each of the 118 remaining patients (98.3% of all patients). These were statistically compared to the output of the ground truth arm. The patient ground truth was defined as the most severe diagnosis from the four patient images; the ophthalmologist diagnosed 54 patients as mtmDR+ (45% prevalence). Of the 54 patients with referable DR, 50 were diagnosed and of the 64 mtmDR- patients, 61 were correctly diagnosed by the AI. In summary, the results of the study in terms of sensitivity and specificity were 92.6% and 95.3%, respectively. The results indicated accurate classification of diabetic patients that required referral to the ophthalmologist and those who did not. The results also demonstrated the potential of efficient screening and easy workflow integration into points of care such as endocrinology clinics.

scholarly journals Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 749 ◽  
Author(s):  
Imran Qureshi ◽  
Jun Ma ◽  
Qaisar Abbas
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Morphological Changes ◽  
Detection Methods ◽  
Computer Assisted ◽  
Early Screening ◽  
Statistical Parameters ◽  
Cad Systems ◽  
Retinal Microvasculature ◽  
Comparison Results

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

scholarly journals Diabetic Retinopathy, lncRNAs, and Inflammation: A Dynamic, Interconnected Network

2019 ◽  
Vol 8 (7) ◽  
pp. 1033 ◽  
Author(s):  
Saumik Biswas ◽  
Marie Sarabusky ◽  
Subrata Chakrabarti
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Interconnected Network ◽  
Working Age ◽  
Modulation Of Gene Expression ◽  
Non Coding Rnas ◽  
Working Age Population ◽  
Prevalence Of Diabetes Mellitus

Diabetic retinopathy (DR) is reaching epidemic levels globally due to the increase in prevalence of diabetes mellitus (DM). DR also has detrimental effects to quality of life, as it is the leading cause of blindness in the working-age population and the most common cause of vision loss in individuals with DM. Over several decades, many studies have recognized the role of inflammation in the development and progression of DR; however, in recent years, accumulating evidence has also suggested that non-coding RNAs, especially long non-coding (lncRNAs), are aberrantly expressed in diabetes and may play a putative role in the development and progression of DR through the modulation of gene expression at the transcriptional, post-transcriptional, or epigenetic level. In this review, we will first highlight some of the key inflammatory mediators and transcription factors involved in DR, and we will then introduce the critical roles of lncRNAs in DR and inflammation. Following this, we will discuss the implications of lncRNAs in other epigenetic mechanisms that may also contribute to the progression of inflammation in DR.

scholarly journals Detection of Diabetic Retinopathy Based on Convolutional Neural Networks: A Review

2021 ◽  
pp. 1-15
Author(s):  
Halbast Rashid Ismael ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Networks ◽  
Vision Loss ◽  
Human Vision ◽  
Early Screening ◽  
Slowing Down ◽  
Manual Diagnostics ◽  
Patients With Diabetes ◽  
Paper Review

A major cause of human vision loss worldwide is Diabetic retinopathy (DR). The disease requires early screening for slowing down the progress. However, in low-resource settings where few ophthalmologists are available to care for all patients with diabetes, the clinical diagnosis of DR will be a considerable challenge. This paper, review the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural Networks (CNN), which highly used to detect DR features from retinal images. CNNs approach to DR detection saves time and expense, and is more efficient and accurate than manual diagnostics. Therefore, CNN is essential and beneficial for DR detection.

scholarly journals The Regulatory Role of Rac1, a Small Molecular Weight GTPase, in the Development of Diabetic Retinopathy

2019 ◽  
Vol 8 (7) ◽  
pp. 965 ◽  
Author(s):  
Nikhil Sahajpal ◽  
Anjan Kowluru ◽  
Renu A. Kowluru
Keyword(s):  
Molecular Weight ◽  
Diabetic Retinopathy ◽  
Transcriptional Activation ◽  
Structural Changes ◽  
Vision Loss ◽  
Mitochondrial Damage ◽  
Early Event ◽  
Small Molecular Weight ◽  
Complex Biological Process

Diabetic retinopathy, a microvascular complication of diabetes, remains the leading cause of vision loss in working age adults. Hyperglycemia is considered as the main instigator for its development, around which other molecular pathways orchestrate. Of these multiple pathways, oxidative stress induces many metabolic, functional and structural changes in the retinal cells, leading to the development of pathological features characteristic of this blinding disease. An increase in cytosolic reactive oxygen species (ROS), produced by cytosolic NADPH oxidase 2 (Nox2), is an early event in the pathogenesis of diabetic retinopathy, which leads to mitochondrial damage and retinal capillary cell apoptosis. Activation of Nox2 is mediated through an obligatory small molecular weight GTPase, Ras-related C3 botulinum toxin substrate 1 (Rac1), and subcellular localization of Rac1 and its activation are regulated by several regulators, rendering it a complex biological process. In diabetes, Rac1 is functionally activated in the retina and its vasculature, and, via Nox2-ROS, contributes to mitochondrial damage and the development of retinopathy. In addition, Rac1 is also transcriptionally activated, and epigenetic modifications play a major role in this transcriptional activation. This review focusses on the role of Rac1 and its regulation in the development and progression of diabetic retinopathy, and discusses some possible avenues for therapeutic interventions.

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