Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma.

Journal: Translational vision science & technology
Published Date:

Abstract

PURPOSE: Correcting retinal nerve fiber layer thickness (RNFLT) artifacts in glaucoma with deep learning and evaluate its clinical usefulness.

Authors

  • Min Shi
    School of Education, Fuzhou University of International Studies and Trade, 350000, China.
  • Jessica A Sun
    Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
  • Anagha Lokhande
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
  • Yu Tian
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Yan Luo
    School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social risk Governance in Health, Chongqing Medical University, Chongqing 400016, China.
  • Tobias Elze
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Complex Structures in Biology and Cognition, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address: tobias-elze@tobias-elze.de.
  • Lucy Q Shen
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
  • Mengyu Wang
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts.