scholarly journals Cotton-wool spots in patients with migraine

Author(s):  
Po Hsiang (Shawn) Yuan ◽  
Jonathan A. Micieli
2021 ◽  
pp. 247412642198957
Author(s):  
Halward M.J. Blegen ◽  
Grant A. Justin ◽  
Bradley A. Bishop ◽  
Anthony R. Cox ◽  
James K. Aden ◽  
...  

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.


2021 ◽  
Vol 105 ◽  
pp. 414-415
Author(s):  
Alison X. Chan ◽  
Michele Ritter ◽  
Mathieu F. Bakhoum

2000 ◽  
Vol 214 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Andreas Remky ◽  
Oliver Arend

Author(s):  
Puspalata Sah ◽  
Kandarpa Kumar Sarma

Detection of diabetes using bloodless technique is an important research issue in the area of machine learning and artificial intelligence (AI). Here we present the working of a system designed to detect the abnormality of the eye with pain and blood free method. The typical features for diabetic retinopathy (DR) are used along with certain soft computing techniques to design such a system. The essential components of DR are blood vessels, red lesions visible as microaneurysms, hemorrhages and whitish lesions i.e., lipid exudates and cotton wool spots. The chapter reports the use of a unique feature set derived from the retinal image of the eye. The feature set is applied to a Support Vector Machine (SVM) which provides the decision regarding the state of infection of the eye. The classification ability of the proposed system for blood vessel and exudate is 91.67% and for optic disc and microaneurysm is 83.33%.


2014 ◽  
Vol 45 (2) ◽  
pp. 156-159 ◽  
Author(s):  
Mohammed A. Khan ◽  
John D. Pitcher ◽  
Steven M. Kawut ◽  
Allen C. Ho

2016 ◽  
Vol 05 (01) ◽  
Author(s):  
Valeria Kheir ◽  
Francois Xavier Borruat

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