scholarly journals The CURRENT TRENDS AND TREATMENTS IN DIABETIC RETINOPATHY

2019 ◽  
pp. 27-33
Author(s):  
FIROZ MV ◽  
VISHAL GUPTA N ◽  
SANDEEP KANNA
Keyword(s):  
Diabetic Retinopathy ◽  
Laser Photocoagulation ◽  
Vision Loss ◽  
Renin Angiotensin System ◽  
Chemical Degradation ◽  
Common Causes ◽  
The World ◽  
Different Types ◽  
New Treatment ◽  
Endothelial Growth Factor

The drastically increasing issues of the disease scenario currently are with different types of diabetes all over the world. It has been reported, approximately 592 million are suffering from the disease throughout the world. It affects differently in different patients with the disease. There have been reports that it is affected differently and also has different side effects. It is also been reported that diabetes mellitus leads to the cause of diabetic retinopathy (DR) and also diabetic macular edema. It is considered as one of the most common causes which is linked to DR. DR has been considered as one of the most important causes for the loss of vision or impaired vision. The drugs show different types of incompatibility such as toxicity, solubility issues, aggregation, and chemical degradation these can be improved by applying several methods. DR is classified according to “Airlie House” into different categories and based on different strategies and consideration. It was found that DR is the main cause for vision loss and also there no much strategies for development of new treatment. The treatment involved is laser photocoagulation and vitrectomy, among these the effective treatment, was found to be laser photocoagulation. This is mainly characterized as proliferative and non-proliferative DR. Different therapeutic agents have been taken for the study these includes vascular endothelial growth factor, renin-angiotensin system inhibitors and nonsteroidal anti-inflammatory drugs, they are certainly different interventions for the treatment, they are nanotechnology and liposome. Nanotechnology applied is the most effective and also acceptable way of treatment.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

scholarly journals Secretogranin III as a disease-associated ligand for antiangiogenic therapy of diabetic retinopathy

2017 ◽  
Vol 214 (4) ◽  
pp. 1029-1047 ◽  
Author(s):  
Michelle E. LeBlanc ◽  
Weiwen Wang ◽  
Xiuping Chen ◽  
Nora B. Caberoy ◽  
Feiye Guo ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Neutralizing Antibodies ◽  
Vision Loss ◽  
New Technology ◽  
Antiangiogenic Therapy ◽  
Vascular Leakage ◽  
Oxygen Induced Retinopathy ◽  
Promising Target ◽  
And Control ◽  
Endothelial Growth Factor

Diabetic retinopathy (DR) is a leading cause of vision loss with retinal vascular leakage and/or neovascularization. Current antiangiogenic therapy against vascular endothelial growth factor (VEGF) has limited efficacy. In this study, we applied a new technology of comparative ligandomics to diabetic and control mice for the differential mapping of disease-related endothelial ligands. Secretogranin III (Scg3) was discovered as a novel disease-associated ligand with selective binding and angiogenic activity in diabetic but not healthy vessels. In contrast, VEGF bound to and induced angiogenesis in both diabetic and normal vasculature. Scg3 and VEGF signal through distinct receptor pathways. Importantly, Scg3-neutralizing antibodies alleviated retinal vascular leakage in diabetic mice with high efficacy. Furthermore, anti-Scg3 prevented retinal neovascularization in oxygen-induced retinopathy mice, a surrogate model for retinopathy of prematurity (ROP). ROP is the most common cause of vision impairment in children, with no approved drug therapy. These results suggest that Scg3 is a promising target for novel antiangiogenic therapy of DR and ROP.

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.

Combination Approaches Targeting Neurodegeneration, Oxidative stress and Inflammation in the Treatment of Diabetic Retinopathy

2021 ◽  
Vol 22 ◽  
Author(s):  
Siddhi Dilip Chalke ◽  
Pravin Popatrao Kale
Keyword(s):  
Oxidative Stress ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Early Stages ◽  
Advanced Stages ◽  
Severe Vision Loss ◽  
Angiopoietin 2 ◽  
Endothelial Growth Factor

: Diabetic Retinopathy (DR) is one of the most severe ocular problems of diabetes. It is a microvascular complication that impairs the vision of diabetic individuals and can cause acquired blindness. Currently available treatment options like laser therapy, vitrectomy, intravitreal anti-vascular endothelial growth factor (VEGF) agents, and glucocorticoids help to reduce vision loss at advanced stages. In spite of the available therapies, patients with severe vision loss face difficulty in achieving normal vision. There is a need for development of newer treatment strategies to address the condition from the early stages. Multiple factors owing to complex pathophysiological events are responsible for this long-term complication. Neurodegeneration, inflammation, and oxidative stress are the three important factors associated with the development of DR. Oxidative stress is a major contributor to the onset and progression of DR. Pathological events like retinal neurodegeneration and inflammation damage the retina right in the early stages of DR. Different combinations of treatments targeting these pathological events are discussed in the present review. The first combination discussed is citicoline and resveratrol. The second combination is duloxetine and N-acetyl cysteine (NAC). These combinations may help in the early stages of DR. CD5-2 and angiopoietin-2 inhibitors is the third combination. This combination may help to manage diabetic macular edema. The main purpose of this article is to discuss the link between these pathologies and the three combination approaches with the objective of consideration of newer therapeutic approaches in research related to DR treatment.

scholarly journals Identification of Prognostic Factors and Predicting the Therapeutic Effect of Laser Photocoagulation for DME Treatment

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1420
Author(s):  
Nataly Ilyasova ◽  
Aleksandr Shirokanev ◽  
Dmitriy Kirsh ◽  
Nikita Demin ◽  
Evgeniy Zamytskiy ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Prognostic Factors ◽  
Therapeutic Effect ◽  
Laser Photocoagulation ◽  
Vision Loss ◽  
Laser Coagulation ◽  
Factors Affecting ◽  
Quality Of Therapy ◽  
Positive Therapeutic Effect ◽  
Intelligent Analysis

Diabetic retinopathy is among the most severe complications of diabetes, most often leading to rapid and irreversible vision loss. The laser coagulation procedure, which consists of applying microburns to the fundus, has proven to be an effective method for treating diabetic retinopathy. Unfortunately, modern research does not pay enough attention to the study of the arrangement of microburns in the edema area—one of the key factors affecting the quality of therapy. The aim of this study was to propose a computational decision-making support system for retina laser photocoagulation based on the analysis of photocoagulation plans. Firstly, we investigated a set of prognostic factors based on 29 features describing the geometric arrangement of coagulates. Secondly, we designed a technology for the intelligent analysis of the photocoagulation plan that allows the effectiveness of the treatment to be predicted. The studies were carried out using a large database of fundus images from 108 patients collected in clinical trials. The results demonstrated a high classification accuracy at a level of over 85% using the proposed prognostic factors. Moreover, the designed technology proved the superiority of the proposed algorithms for the automatic arrangement of coagulates, predicting a 99% chance of a positive therapeutic effect.

scholarly journals Early Detection of Diabetic Retinopathy

2021 ◽  
Vol 9 (IX) ◽  
pp. 429-435
Author(s):  
Vamsi Krishna Mekala
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Network Models ◽  
Medical System ◽  
Neural Network Models ◽  
Contemporary World ◽  
Common Causes ◽  
Medical Photography ◽  
Speed Up

Diabetic retinopathy are among the most common causes of vision loss in today's world. Visual impairment impacts about one in 3 diabetics, according to an epidemiological research. Diagnostic imaging is an important aspect of medical photography in contemporary world. Deep learning improves the eyesight for identifying illness in radiography. The goal is to use machine learning to diagnose vision loss. Deep learning in diagnostic devices might improve and speed up the diagnosis of sugar-related vision loss. This research will look at neural network models, algorithms, and simulations in order to diagnose diabetic retinopathy rapidly and help the medical system. The classifier is constructed using CNN.

scholarly journals Photoreceptor responses to light in the pathogenesis of diabetic retinopathy

2020 ◽  
Vol 37 ◽  
Author(s):  
Shahriyar P. Majidi ◽  
Rithwick Rajagopal
Keyword(s):  
Diabetic Retinopathy ◽  
Clinical Evidence ◽  
Vision Loss ◽  
Light Modulation ◽  
Retinal Neurons ◽  
Microvascular Networks ◽  
Common Cause ◽  
The World ◽  
Growing Body ◽  
Retinal Vascular Disease

Abstract Vision loss, among the most feared complications of diabetes, is primarily caused by diabetic retinopathy, a disease that manifests in well-recognized, characteristic microvascular lesions. The reasons for retinal susceptibility to damage in diabetes are unclear, especially considering that microvascular networks are found in all tissues. However, the unique metabolic demands of retinal neurons could account for their vulnerability in diabetes. Photoreceptors are the first neurons in the visual circuit and are also the most energy-demanding cells of the retina. Here, we review experimental and clinical evidence linking photoreceptors to the development of diabetic retinopathy. We then describe the influence of retinal illumination on photoreceptor metabolism, effects of light modulation on the severity of diabetic retinopathy, and recent clinical trials testing the treatment of diabetic retinopathy with interventions that impact photoreceptor metabolism. Finally, we introduce several possible mechanisms that could link photoreceptor responses to light and the development of retinal vascular disease in diabetes. Collectively, these concepts form the basis for a growing body of investigative efforts aimed at developing novel pharmacologic and nonpharmacologic tools that target photoreceptor physiology to treat a very common cause of blindness across the world.

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