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

@article{Peng2018DeepSeeNetAD,
  title={DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs},
  author={Yifan Peng and Shazia Dharssi and Qingyu Chen and Tiarnan D. Keenan and Elvira Agr{\'o}n and Wai T. Wong and Emily Y. Chew and Zhiyong Lu},
  journal={Ophthalmology},
  year={2018}
}
PURPOSE 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. While several automated deep learning (DL) systems have been developed for classifying color fundus photographs of individual eyes by AREDS severity score, none to date has utilized a patient-based scoring system that… CONTINUE READING
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