Prevalence of age-related macular degeneration in rural southern China: the Yangxi Eye Study

2017 ◽  
Vol 102 (5) ◽  
pp. 625-630 ◽  
Author(s):  
Guangming Jin ◽  
Xiaohu Ding ◽  
Wei Xiao ◽  
Xiao Xu ◽  
Lanhua Wang ◽  
...  

PurposeTo describe the prevalence of age-related macular degeneration (AMD) among older adults in rural southern mainland China.MethodsEligible persons aged 50 years or over were identified by geographically defined cluster sampling from Yangxi County, Guangdong Province, China. Participants underwent a standardised interview and comprehensive eye examinations from August to November in 2014. Digital retinal photographs were graded for AMD lesions using the Clinical Classification of Age-Related Macular Degeneration developed by the Beckman Initiative for Macular Research Classification Committee. Age-standardised prevalence of AMD and AMD lesions was calculated using the 2010 world population data and compared with those of other populations.ResultsOf 5825 subjects who participated (90.7% response rate), 4881 (83.8%) had fundus photographs gradable for AMD. Early, intermediate and late AMD were present in 2003 (41.0%), 879 (18.0%) and 42 (0.86%) participants. The age-standardised prevalence of early, intermediate and late AMD was 40.4% (95% CI 39.6% to 41.2%), 17.6% (95% CI 17.0% to 18.2%) and 0.79% (95% CI 0.65% to 0.95%), respectively. Total AMD was more prevalent in men than in women (62.8% vs 57.1%).ConclusionsAMD is an important public health concern for rural southern China, and the prevalence of AMD was higher in men than in women.

2011 ◽  
Vol 05 (01) ◽  
pp. 84
Author(s):  
Rajeev K Seth ◽  
Eric J Sigler ◽  
Ron A Adelman ◽  
◽  
◽  
...  

Age-related macular degeneration (ARMD) continues to be a significant public health concern, given its high prevalence and potential blinding prognosis in the elderly. Several risk factors have been identified in the development of ARMD, including age, race and family history. The pathogenesis of ARMD continues to be elucidated and recent research has focused on genetic factors. ARMD presents in either the atrophic or exudative form. Treatment for atrophic disease consists of antioxidants and zinc and close monitoring. Treatment for exudative disease is aimed at targeting choroidal neovascularisation. The development of anti-vascular endothelial growth factor (anti-VEGF) agents has revolutionised the treatment of exudative ARMD, providing a more favourable prognosis for previously blinding disease. A considerable amount of research is being carried out on new treatment modalities for both the atrophic and exudative forms of the disease.


2013 ◽  
Vol 54 (3) ◽  
pp. 1789 ◽  
Author(s):  
Srihari Kankanahalli ◽  
Philippe M. Burlina ◽  
Yulia Wolfson ◽  
David E. Freund ◽  
Neil M. Bressler

Eye ◽  
2005 ◽  
Vol 20 (4) ◽  
pp. 471-475 ◽  
Author(s):  
S Jain ◽  
S Hamada ◽  
W L Membrey ◽  
V Chong

2011 ◽  
Vol 04 (01) ◽  
pp. 96 ◽  
Author(s):  
Rajeev K Seth ◽  
Eric J Sigler ◽  
Ron A Adelman ◽  
◽  
◽  
...  

Age-related macular degeneration (ARMD) continues to be a significant public health concern, given its high prevalence and potential blinding prognosis in the elderly. Several risk factors have been identified in the development of ARMD, including age, race, and family history. The pathogenesis of ARMD continues to be elucidated, and recent research has focused on genetic factors. ARMD presents in either the atrophic or exudative form. Treatment for atrophic disease consists of antioxidants and zinc and close monitoring. Treatment for exudative disease is aimed at targeting choroidal neovascularization. The development of anti-vascular endothelial growth factor (anti-VEGF) agents has revolutionized the treatment of exudative ARMD, providing a more favorable prognosis for previously blinding disease. Considerable amount of research is being carried out on new treatment modalities for both the atrophic and exudative forms of the disease.


F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.


2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Vaghefi ◽  
Sophie Hill ◽  
Hannah M. Kersten ◽  
David Squirrell

Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.


F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.


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