Optimized prediction models from fundus imaging and genetics for late age-related macular degeneration

Arun Govindaiah, Abdul Baten, R. Theodore Smith, Siva Balasubramanian, Alauddin Bhuiyan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Article number1127
JournalJournal of Personalized Medicine
Issue number11
StatePublished - Nov 2021
Externally publishedYes


  • Deep learning
  • Fundus imaging
  • Genetics
  • Macular degeneration


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