Corpus ID: 56657796

Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images

@article{Phene2018DeepLT,
  title={Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images},
  author={Sonia Phene and R. Carter Dunn and Naama Hammel and Yun Liu and Jonathan Krause and Naho Kitade and Mike Schaekermann and Rory Sayres and Derek J. Wu and Ashish Bora and Christopher Semturs and Anita Misra and Abigail E. Huang and Arielle Spitze and Felipe A. Medeiros and April Y. Maa and Monica Gandhi and Greg S Corrado and Lily H. Peng and Dale R. Webster},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.08911}
}
Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Thus, glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand healthcare access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning (DL) algorithm using a retrospective dataset… Expand
3 Citations
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