Feature-based retinal image registration for longitudinal analysis of patients with age-related macular degeneration

2020 ◽  
Author(s):  
Tharindu S. De Silva ◽  
Nathan Hotaling ◽  
Emily Y. Chew ◽  
Catherine Cukras
Keyword(s):  
Image Registration ◽  
Macular Degeneration ◽  
Longitudinal Analysis ◽  
Retinal Image ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Feature Based

Longitudinal Analysis of Reticular Drusen Associated With Geographic Atrophy in Age-Related Macular Degeneration

2013 ◽  
Vol 54 (6) ◽  
pp. 4054 ◽  
Author(s):  
Julia S. Steinberg ◽  
Jasmin Auge ◽  
Glenn J. Jaffe ◽  
Monika Fleckenstein ◽  
Frank G. Holz ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Longitudinal Analysis ◽  
Geographic Atrophy ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Reticular Drusen

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.

Retinal-Image Quality and Contrast-Sensitivity Function in Age-Related Macular Degeneration

2010 ◽  
Vol 35 (8) ◽  
pp. 757-761 ◽  
Author(s):  
Carolina Ortiz ◽  
José R. Jiménez ◽  
Francisco Pérez-Ocón ◽  
José J Castro ◽  
Rosario González-Anera
Keyword(s):  
Image Quality ◽  
Macular Degeneration ◽  
Contrast Sensitivity ◽  
Retinal Image ◽  
Sensitivity Function ◽  
Age Related Macular Degeneration ◽  
Contrast Sensitivity Function ◽  
Age Related
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