scholarly journals Automated Retinal Image Analysis for Evaluation of Focal Hyperpigmentary Changes in Intermediate Age-Related Macular Degeneration

2016 ◽  
Vol 5 (2) ◽  
pp. 3 ◽  
Author(s):  
Steffen Schmitz-Valckenberg ◽  
Arno P. Göbel ◽  
Stefan C. Saur ◽  
Julia S. Steinberg ◽  
Sarah Thiele ◽  
...  
Keyword(s):  
Image Analysis ◽  
Macular Degeneration ◽  
Retinal Image ◽  
Age Related Macular Degeneration ◽  
Retinal Image Analysis ◽  
Age Related

scholarly journals Progress on retinal image analysis for age related macular degeneration

2014 ◽  
Vol 38 ◽  
pp. 20-42 ◽  
Author(s):  
Yogesan Kanagasingam ◽  
Alauddin Bhuiyan ◽  
Michael D. Abràmoff ◽  
R. Theodore Smith ◽  
Leonard Goldschmidt ◽  
...  
Keyword(s):  
Image Analysis ◽  
Macular Degeneration ◽  
Retinal Image ◽  
Age Related Macular Degeneration ◽  
Retinal Image Analysis ◽  
Age Related

Retinal Image Analysis: A Review

2012 ◽  
pp. 265-269
Author(s):  
S. R. Nirmala ◽  
Malaya Kumar Nath ◽  
Samarendra Dandapat
Keyword(s):  
Image Analysis ◽  
Retinal Image ◽  
Screening Tool ◽  
Review Paper ◽  
Age Related Macular Degeneration ◽  
Hypertensive Retinopathy ◽  
Human Errors ◽  
Remote Areas ◽  
Age Related ◽  
The Relationship

Images of the eye ground or retina not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automated retina image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This can in turn be used to reduce human errors or to provide services to remote areas. In this review paper, we discuss some of the current techniques used to automatically detect the important clinical features of retinal image, such as the blood vessels, optic disc and macula. The quantitative analysis and measurements of these features can be used to better understand the relationship between various diseases and the retinal features.

scholarly journals Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration

2021 ◽  
Vol 11 (11) ◽  
pp. 1127
Author(s):  
Arun Govindaiah ◽  
Abdul Baten ◽  
R. Theodore Smith ◽  
Siva Balasubramanian ◽  
Alauddin Bhuiyan
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Retinal Image ◽  
Prediction Models ◽  
Retinal Imaging ◽  
Image Data ◽  
Age Related Macular Degeneration ◽  
Learning Models ◽  
Age Related ◽  
Machine Learning Models

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.

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