diabetic retinopathy
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Prakruthi Mandya Krishnegowda ◽  
Komarasamy Ganesan

<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.</p>

Mulagala Sandhya ◽  
Mahesh Kumar Morampudi ◽  
Rushali Grandhe ◽  
Richa Kumari ◽  
Chandanreddy Banda ◽  

10.29007/h46n ◽  
2022 ◽  
Hoang Nhut Huynh ◽  
Minh Thanh Do ◽  
Gia Thinh Huynh ◽  
Anh Tu Tran ◽  
Trung Nghia Tran

Diabetic retinopathy (DR) is a complication of diabetes mellitus that causes retinal damage that can lead to vision loss if not detected and treated promptly. The common diagnosis stages of the disease take time, effort, and cost and can be misdiagnosed. In the recent period with the explosion of artificial intelligence, deep learning has become the most popular tool with high performance in many fields, especially in the analysis and classification of medical images. The Convolutional Neural Network (CNN) is more widely used as a deep learning method in medical imaging analysis with highly effective. In this paper, the five-stage image of modern DR (healthy, mild, moderate, severe, and proliferative) can be detected and classified using the deep learning technique. After cross-validation training and testing on the corresponding 5,590-image dataset, a pre-MobileNetV2 training model is proposed in classifying stages of diabetic retinopathy. The average accuracy of the model achieved was 93.89% with the precision of 94.00%, recall 92.00% and f1-score 90.00%. The corresponding thermal image is also given to help experts for evaluating the influence of the retina in each different stage.

Elva Adán‐Castro ◽  
Lourdes Siqueiros‐Márquez ◽  
Gabriela Ramírez‐Hernández ◽  
Nundehui Díaz‐Lezama ◽  
Xarubet Ruiz‐Herrera ◽  

Sadashiv ◽  
Praveen Sharma ◽  
Shailendra Dwivedi ◽  
Sunita Tiwari ◽  
Pankaj Kumar Singh ◽  

Meng-Yuan Zhang ◽  
Lingpeng Zhu ◽  
Xinhua Zheng ◽  
Tian-Hua Xie ◽  
Wenjuan Wang ◽  

Background: Diabetic retinopathy (DR) is one of the most important microvascular diseases of diabetes. Our previous research demonstrated that bile acid G-protein-coupled membrane receptor (TGR5), a novel cell membrane receptor of bile acid, ameliorates the vascular endothelial cell dysfunction in DR. However, the precise mechanism leading to this alteration remains unknown. Thus, the mechanism of TGR5 in the progress of DR should be urgently explored.Methods: In this study, we established high glucose (HG)-induced human retinal vascular endothelial cells (RMECs) and streptozotocin-induced DR rat in vitro and in vivo. The expression of TGR5 was interfered through the specific agonist or siRNA to study the effect of TGR5 on the function of endothelial cell in vitro. Western blot, immunofluorescence and fluorescent probes were used to explore how TGR5 regulated mitochondrial homeostasis and related molecular mechanism. The adeno-associated virus serotype 8-shTGR5 (AAV8-shTGR5) was performed to evaluate retinal dysfunction in vivo and further confirm the role of TGR5 in DR by HE staining, TUNEL staining, PAS staining and Evans Blue dye.Results: We found that TGR5 activation alleviated HG-induced endothelial cell apoptosis by improving mitochondrial homeostasis. Additionally, TGR5 signaling reduced mitochondrial fission by suppressing the Ca2+-PKCδ/Drp1 signaling and enhanced mitophagy through the upregulation of the PINK1/Parkin signaling pathway. Furthermore, our result indicated that Drp1 inhibited mitophagy by facilitating the hexokinase (HK) 2 separation from the mitochondria and HK2-PINK1/Parkin signaling. In vivo, intraretinal microvascular abnormalities, including retinal vascular leakage, acellular capillaries and apoptosis, were poor in AAV8-shTGR5-treated group under DR, but this effect was reversed by pretreatment with the mitochondrial fission inhibitor Mdivi-1 or autophagy agonist Rapamycin.Conclusion: Overall, our findings indicated that TGR5 inhibited mitochondrial fission and enhanced mitophagy in RMECs by regulating the PKCδ/Drp1-HK2 signaling pathway. These results revealed the molecular mechanisms underlying the protective effects of TGR5 and suggested that activation of TGR5 might be a potential therapeutic strategy for DR.

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