scholarly journals Psycho physical test in normal individual and diabetic patients with and without diabetic retinopathy: comparative study

2018 ◽  
Vol 6 (2) ◽  
pp. 93-101
Author(s):  
Dr. Sapna Raghuwanshi ◽  
◽  
Dr. Rashmi Kumar ◽  
Dr. Shivkumar Raghuwanshi ◽  
◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Comparative Study ◽  
Normal Individual ◽  
Diabetic Patients ◽  
Physical Test

Comparative study of the central corneal thickness in healthy individuals and diabetic patients with and without retinopathy using anterior segment optical coherence tomography

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Tamer F El-Mekkawi ◽  
Hazem O Rashed ◽  
Hisham S. Saad Eldin ◽  
Hagar M Faisal
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Comparative Study ◽  
Central Corneal Thickness ◽  
Corneal Thickness ◽  
Anterior Segment ◽  
Diabetic Patients ◽  
Optical Coherence ◽  
Chronic Hyperglycemia ◽  
Significant Difference

Abstract Background Chronic hyperglycemia in diabetes mellitus causes different morphologic and functional corneal changes. Aim of the Work to assess the central corneal thickness in diabetic patients with and without diabetic retinopathy using anterior segment optical coherence tomography. Patients and Methods This comparative study was conducted at Ain Shams University hospitals . It included 45 eyes divided into three groups: Group A: 15 eyes of diabetic patients without diabetic retinopathy , Group B: 15 eyes of diabetic patients with diabetic retinopathy and Group C: 15 eyes of non-diabetic individuals. Results : The mean CCT in diabetic patients without diabetic retinopathy was 551.13µ ± 37.93 with range 475-622. Diabetics with retinopathy was 558.93µ ± 39.32 with range 508-618 and non diabetics 534.73µ ± 33.67 with range 475-588. There was no significant difference in corneal thickness between the three groups (p = 0.201) Conclusion : Diabetic corneas tended to be thicker though this was not statistically significant in our work.

Male Type 2 Diabetic Patients Have Higher Diabetic Retinopathy Prevalence

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 599-P ◽  
Author(s):  
SARA CHERCHI ◽  
ALFONSO GIGANTE ◽  
PIERPAOLO CONTINI ◽  
DANILA PISTIS ◽  
ROSANGELA M. PILOSU ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Diabetic Patients ◽  
Type 2 Diabetic Patients ◽  
Male Type ◽  
Type 2 Diabetic

Proteomic Analysis of the Vitreous Body in Proliferative and Non-Proliferative Diabetic Retinopathy

2020 ◽  
Vol 17 ◽  
Author(s):  
Van-An Duong ◽  
Jeeyun Ahn ◽  
Na-Young Han ◽  
Jong-Moon Park ◽  
Jeong-Hun Mok ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Microvascular Complications ◽  
Treatment Options ◽  
Vitreous Body ◽  
Control Group ◽  
Diabetic Patients ◽  
Protein Protein Interaction ◽  
Alpha Beta ◽  
New Treatment

Background: Diabetic Retinopathy (DR), one of the major microvascular complications commonly occurring in diabetic patients, can be classified into Proliferative Diabetic Retinopathy (PDR) and Non-Proliferative Diabetic Retinopathy (NPDR). Currently available therapies are only targeted for later stages of the disease in which some pathologic changes may be irreversible. Thus, there is a need to develop new treatment options for earlier stages of DR through revealing pathological mechanisms of PDR and NPDR. Objective: The purpose of this study was to characterize proteomes of diabetic through quantitative analysis of PDR and NPDR. Methods: Vitreous body was collected from three groups: control (non-diabetes mellitus), NPDR, and PDR. Vitreous proteins were digested to peptide mixtures and analyzed using LC-MS/MS. MaxQuant was used to search against the database and statistical analyses were performed using Perseus. Gene ontology analysis, related-disease identification, and protein-protein interaction were performed using the differential expressed proteins. Results: Twenty proteins were identified as critical in PDR and NPDR. The NPDR group showed different expressions of kininogen-1, serotransferrin, ribonuclease pancreatic, osteopontin, keratin type II cytoskeletal 2 epidermal, and transthyretin. Also, prothrombin, signal transducer and activator of transcription 4, hemoglobin subunit alpha, beta, and delta were particularly up-regulated proteins for PDR group. The up-regulated proteins related to complement and coagulation cascades. Statherin was down-regulated in PDR and NPDR compared with the control group. Transthyretin was the unique protein that increased its abundance in NPDR compared with the PDR and control group. Conclusion: This study confirmed the different expressions of some proteins in PDR and NPDR. Additionally, we revealed uniquely expressed proteins of PDR and NPDR, which would be differential biomarkers: prothrombin, alpha-2-HS-glycoprotein, hemoglobin subunit alpha, beta, and transthyretin.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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