Automated classification of severity of age-related macular degeneration from fundus photographs

Srihari Kankanahalli, Philippe M. Burlina, Yulia Wolfson, David E. Freund, Neil M. Bressler

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose. To evaluate an automated analysis of retinal fundus photographs to detect and classify severity of age-related macular degeneration compared with grading by the Age-Related Eye Disease Study (AREDS) protocol. Methods. Following approval by the Johns Hopkins University School of Medicine's Institution Review Board, digitized images (downloaded at http://www.ncbi.nlm.nih.gov/gap/) of field 2 (macular) fundus photographs from AREDS obtained over a 12-year longitudinal study were classified automatically using a visual words method to compare with severity by expert graders. Results. Sensitivities and specificities, respectively, of automated imaging, when compared with expert fundus grading of 468 patients and 2145 fundus images are: 98.6% and 96.3% when classifying categories 1 and 2 versus categories 3 and 4; 96.1% and 96.1% when classifying categories 1 and 2 versus category 3; 98.6% and 95.7% when classifying category 1 versus category 3; and 96.0% and 94.7% when classifying category 1 versus categories 3 and 4; Conclusions. Development of an automated analysis for classification of age-related macular degeneration from digitized fundus photographs has high sensitivity and specificity when compared with expert graders and may have a role in screening or monitoring.

Original languageEnglish (US)
Pages (from-to)1789-1796
Number of pages8
JournalInvestigative Ophthalmology and Visual Science
Volume54
Issue number3
DOIs
StatePublished - 2013

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

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