scholarly journals An automated drusen detection system for classifying age-related macular degeneration with color fundus photographs

2013 ◽  
Author(s):  
Yuanjie Zheng ◽  
Brian Vanderbeek ◽  
Ebenezer Daniel ◽  
Dwight Stambolian ◽  
Maureen Maguire ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Detection System ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Fundus Photographs

Automated Classification of Severity of Age-Related Macular Degeneration from Fundus Photographs

2013 ◽  
Vol 54 (3) ◽  
pp. 1789 ◽  
Author(s):  
Srihari Kankanahalli ◽  
Philippe M. Burlina ◽  
Yulia Wolfson ◽  
David E. Freund ◽  
Neil M. Bressler
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Automated Classification ◽  
Age Related ◽  
Fundus Photographs

scholarly journals Screening for age-related macular degeneration using nonstereo digital fundus photographs

Eye ◽  
2005 ◽  
Vol 20 (4) ◽  
pp. 471-475 ◽  
Author(s):  
S Jain ◽  
S Hamada ◽  
W L Membrey ◽  
V Chong
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Fundus Photographs

scholarly journals Automated analysis of retinal imaging using machine learning techniques for computer vision

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Human Error ◽  
Machine Learning Algorithms ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Sound Waves ◽  
Age Related ◽  
Fundus Photographs

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.

scholarly journals Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Vaghefi ◽  
Sophie Hill ◽  
Hannah M. Kersten ◽  
David Squirrell
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Multiple Modalities ◽  
Age Related ◽  
Dry Amd ◽  
Fundus Photographs

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.

scholarly journals Automated analysis of retinal imaging using machine learning techniques for computer vision

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Human Error ◽  
Machine Learning Algorithms ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Sound Waves ◽  
Age Related ◽  
Fundus Photographs

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.

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 ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Southern China ◽  
Mainland China ◽  
Population Data ◽  
Age Related Macular Degeneration ◽  
Health Concern ◽  
Cluster Sampling ◽  
World Population ◽  
Age Related ◽  
Fundus Photographs

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.

scholarly journals One optometrist’s personal experience with age-related macular degeneration (AMD) and nutritional supplementation

2012 ◽  
Vol 74 (3) ◽  
pp. 38
Author(s):  
Stuart Richer ◽  
Albert Licup ◽  
Tere Porter ◽  
Frederick Collison
Keyword(s):  
Visual Acuity ◽  
Macular Degeneration ◽  
Complete Resolution ◽  
Nutritional Supplement ◽  
Lifestyle Changes ◽  
Age Related Macular Degeneration ◽  
Visual Disability ◽  
Age Related ◽  
History Of ◽  
Fundus Photographs

Background: Age-related macular degeneration (AMD) is the leading cause of blindness in ageing western societies and accounts for greater than 50% of all US visual disability. This report describes the 25-year history of a 66-year-old optometrist who has successfully endured AMD.  Case Report: Visual acuity and serial retinal photographs from 1983 to 2009 as various nutritional modalities and non-dietary lifestyle changes were introduced. After starting lutein-based nutritional supplements beginning at approximately 15 years from diagnosis, the optometrist’s Snellen visual acuity improved in his right eye from 20/40 to 20/25 with a subjective improvement in distortion, but eventually regressed to 20/70-20/80 with some increase in metamorphopsia. The left eye, initially 20/30, improved to 20/15 and has remained stable at 20/20 with complete resolution of metamorphopsia and near complete resolution of a parafoveal scotoma. Fundus photographs demonstrate a reduction in soft and hard drusen count over time in each retina and possible parafoveal repigmentation of atrophic areas with later addition of higher dose zeaxanthin. Conclusions: AMD is a nutritionresponsive disease. The carotenoids, lutein and zeaxanthin appear to be particularly robust therapeutic components of nutritional supplement formulations.

Sign in / Sign up

Export Citation Format

Share Document