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      DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs

      , , , , , , ,
      Ophthalmology
      Elsevier BV

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          Abstract

          In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score.

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          Author and article information

          Journal
          Ophthalmology
          Ophthalmology
          Elsevier BV
          01616420
          November 2018
          November 2018
          Article
          10.1016/j.ophtha.2018.11.015
          6435402
          30471319
          44b6240a-4f5f-43b2-9a1b-010891ff4963
          © 2018

          https://www.elsevier.com/tdm/userlicense/1.0/

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