Computer Classification of Nonproliferative Diabetic Retinopathy

Diabetic retinopathy (DR) stays as an eye issue that has continuously developed in individuals who experienced diabetes. The complexities in diabetes cause harm to the vein at the back of the retina. In outrageous cases, DR could swift apparition disaster or visual impairment. This genuine impact had the option to charge through convenient treatment and early recognition. As of late, this issue has been spreading quickly, particularly in the working region, which in the end constrained the interest of an analysis of this disease from the most prompt stage. Therefore, that are castoff to protect the progressions of this disorder, revealing of the retinal blood vessels (RBVs) play a foremost role. The growth of an abnormal vessel leads to the development steps of DR, where it can be well known by extracting the RBV. The recognition of the BV for DR by developing an automatic approach is a major aim of our research study. In the proposed method, there are two major steps: one is segmentation and the second one is classification of affected retinal BV. The proposed method uses the Kinetic Gas Molecule Optimization based on centroid initialization used for the Fuzzy C-means Clustering. In the classification step, those segmented images are given as input to hybrid techniques such as a convolution neural network with bidirectional-long short-term memory (CNN with Bi-LSTM). The learning degree of Bi-LSTM is revised by using the self-attention mechanism for refining the classification accuracy. The trial consequences disclosed that the mixture algorithm achieved higher accuracy, specificity, and sensitivity than existing techniques.

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The Association Between Obstructive Sleep Apnea and Cotton-Wool Spots in Nonproliferative Diabetic Retinopathy

Journal of VitreoRetinal Diseases ◽

10.1177/2474126421989576 ◽

2021 ◽

pp. 247412642198957

Author(s):

Halward M.J. Blegen ◽

Grant A. Justin ◽

Bradley A. Bishop ◽

Anthony R. Cox ◽

James K. Aden ◽

...

Keyword(s):

Body Mass Index ◽

Obstructive Sleep Apnea ◽

Diabetic Retinopathy ◽

Sleep Apnea ◽

Body Mass ◽

Smoking Status ◽

Cotton Wool ◽

Nonproliferative Diabetic Retinopathy ◽

Cotton Wool Spots ◽

Obstructive Sleep

Purpose: This work reports the association of obstructive sleep apnea (OSA) and cotton-wool spots (CWS) seen in patients with nonproliferative diabetic retinopathy (DR). Methods: A random sample of patients diagnosed with DR between January 1, 2015 and December 31, 2018, were selected from medical-billing codes. Dilated funduscopic examination findings and medical history were analyzed by reviewing medical records. Results: CWS were present in 12 of 118 patients without OSA, compared with 11 of 32 patients with OSA (10.2% vs 34.4%, respectively; P = .002). OSA was more common in men (68.8%, P = .03) and associated with a higher body mass index (30.0 ± 5.0 without OSA vs 33.6 ± 5.5 with OSA, P < .001). When comparing those with and without OSA, there was no association with age; glycated hemoglobin A1c; stage of DR; insulin dependence; presence of diabetic macular edema; smoking status; or a history of hypertension, hyperlipidemia, cardiovascular disease, or other breathing disorder. Conclusions: The presence of OSA is associated with CWS in patients with DR, as well as male sex and a higher body mass index. Further research is needed to determine the ophthalmologist’s role in the timely referral of patients with CWS for OSA evaluation.

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INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519415500852 ◽

2015 ◽

Vol 15 (05) ◽

pp. 1550085 ◽

Cited By ~ 2

Author(s):

MADHURI TASGAONKAR ◽

MADHURI KHAMBETE

Keyword(s):

Diabetic Retinopathy ◽

Early Detection ◽

Ground Truth ◽

Vital Role ◽

Detection Accuracy ◽

Fuzzy C Means ◽

Fundus Imaging ◽

Imaging Detection ◽

Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

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Efficacy of Intravitreal Triamcinolone after or Concomitant with Laser Photocoagulation in Nonproliferative Diabetic Retinopathy with Macular Edema

European Journal of Ophthalmology ◽

10.1177/112067210901900418 ◽

2009 ◽

Vol 19 (4) ◽

pp. 630-637 ◽

Cited By ~ 2

Author(s):

Erdinc Aydin ◽

Helin Deniz Demir ◽

Huseyin Yardim ◽

Unal Erkorkmaz

Keyword(s):

Diabetic Retinopathy ◽

Macular Edema ◽

Laser Photocoagulation ◽

Control Group ◽

Clinical Effects ◽

Intravitreal Triamcinolone ◽

Nonproliferative Diabetic Retinopathy ◽

Significant Difference ◽

Group 3 ◽

The Mean

Purpose To investigate the clinical effects and outcomes of intravitreal injection of 4 mg of triamcinolone acetonide (IVTA) after or concomitant with macular laser photocoagulation (MP) for clinically significant macular edema (CSME). Methods Forty-nine eyes of 49 patients with nonproliferative diabetic retinopathy and CSME were randomized into three groups. The eyes in the laser group (n=17), group 1, were subjected to MP 3 weeks after IVTA; the eyes in the IVTA group (n=13), group 2, were subjected to MP, concomitant with IVTA; the eyes in the control group (n=19), group 3, underwent only IVTA application. Visual acuity (VA), fundus fluorescein angiography, and photography were performed in each group. Results In the first group, the mean VA improved from 0.17±0.09 at baseline to 0.28±0.15 (p=0.114) and in the second group, deteriorated from 0.19±0.08 at baseline to 0.14±0.08 at the sixth month (p=0.141), respectively. In Group 3, the mean VA improved from 0.16±0.08 at baseline to 0.28±0.18 (p=0.118) at the end of the follow-up. When VA was compared between the control and study groups, significant difference was detected at the sixth month (p=0.038). Conclusions MP after IVTA improved VA, rather than MP concomitant with IVTA, and only IVTA application for CSME. MP after IVTA may reduce the recurrence of CSME and needs further investigations in a longer period.

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