scholarly journals Automated Detection and Differentiation of Drusen, Exudates, and Cotton-Wool Spots in Digital Color Fundus Photographs for Diabetic Retinopathy Diagnosis

2007 ◽  
Vol 48 (5) ◽  
pp. 2260 ◽  
Author(s):  
Meindert Niemeijer ◽  
Bram van Ginneken ◽  
Stephen R. Russell ◽  
Maria S. A. Suttorp-Schulten ◽  
Michael D. Abra`moff
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Detection ◽  
Cotton Wool ◽  
Cotton Wool Spots ◽  
Fundus Photographs

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

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.

Background diabetic retinopathy

2020 ◽  
pp. 37-42
Author(s):  
Rachel Stockwin ◽  
Emma Shepherd
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Glucose ◽  
Capillary Network ◽  
Cotton Wool ◽  
Vascular Changes ◽  
Normal Variants ◽  
Cotton Wool Spots ◽  
Referral Criteria

Background diabetic retinopathy (DR) can involve several different microvascular pathologies, which will be explained with example images. These include microaneurysms, haemorrhages, exudates, cotton wool spots, and venous loops. The reader will learn how these features relate to the grading and referral criteria. This chapter aims to provide information on how these pathologies develop and why it is important that they are recognized in the earlier stages of background DR. The chapter will demonstrate how ischaemia can affect the capillary network and also how related conditions, such as hypertension and blood glucose, can contribute to vascular changes. In addition, this chapter will explain how to differentiate normal variants from DR changes.

scholarly journals Retinal cotton-wool spots: an early finding in diabetic retinopathy?

1986 ◽  
Vol 70 (10) ◽  
pp. 772-778 ◽  
Author(s):  
M S Roy ◽  
M E Rick ◽  
K E Higgins ◽  
J C McCulloch
Keyword(s):  
Diabetic Retinopathy ◽  
Cotton Wool ◽  
Cotton Wool Spots

Diabetic Retinopathy: Screening to Treatment (Oxford Diabetes Library)

2020 ◽  
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Glucose ◽  
Capillary Network ◽  
Cotton Wool ◽  
Vascular Changes ◽  
Normal Variants ◽  
Diabetic Retinopathy Screening ◽  
Cotton Wool Spots ◽  
Referral Criteria ◽  
Retinopathy Screening

Background diabetic retinopathy (DR) can involve several different microvascular pathologies, which will be explained with example images. These include microaneurysms, haemorrhages, exudates, cotton wool spots, and venous loops. The reader will learn how these features relate to the grading and referral criteria. This book aims to provide information on how these pathologies develop and why it is important that they are recognized in the earlier stages of background DR. It will demonstrate how ischaemia can affect the capillary network and also how related conditions, such as hypertension and blood glucose, can contribute to vascular changes. In addition, this book will explain how to differentiate normal variants from DR changes.

Features of cotton wool spots in diabetic retinopathy: a spectral-domain optical coherence tomography angiography study

2020 ◽  
Vol 40 (7) ◽  
pp. 1625-1640 ◽  
Author(s):  
Amir Mahdjoubi ◽  
Youcef Bousnina ◽  
Gaelle Barrande ◽  
Faïza Bensmaine ◽  
Sadri Chahed ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Cotton Wool ◽  
Cotton Wool Spots

The Diagnosis Of Diabetic Retinopathy By Means Of Transfer Learning With Conventional Machine Learning Pipeline

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 62-67
Author(s):  
Farhan Nabil Mohd Noor ◽  
Wan Hasbullah Mohd Isa ◽  
Anwar P.P. Abdul Majeed
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Detection Method ◽  
Automated Detection ◽  
High Blood Glucose ◽  
Cotton Wool Spots ◽  
High Blood Glucose Level ◽  
Conventional Machine ◽  
The Common

Diabetic Retinopathy is one of the common eye diseases due to the complication of diabetes mellitus. Cotton wool spots, rough exudates, haemorrhages and microaneurysms are the symptoms of the diabetic retinopathy due to the fluid leakage that is caused by the high blood glucose level disorder. Early treatment to prevent a permanent blindness is important as it could save the diabetic retinopathy vision. Hence, in this study, we proposed to employ an automated detection method to diagnose the diabetic retinopathy. The dataset was obtained from the Kaggle Database and been divided for training, testing and validation purposes. Furthermore, Transfer Learning models, namely VGG19 were employed to extract the features before being processed by Machine Learning classifiers which are SVM, kNN and RF to classify the diabetic retinopathy. VGG19-SVM pipeline produced the best accuracy in training, testing and validation processes, achieving 99, 99 and 96 percents respectively.

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