Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.

Journal: Ophthalmology
PMID: 32019699

Abstract

PURPOSE: To validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2).

Authors

  • Jessica Loo
    Department of Biomedical Engineering, Duke University, Durham, North Carolina.
  • Traci E Clemons
    Ophthalmology, Emmes, Rockville, Maryland.
  • Emily Y Chew
    National Eye Institute, National Institutes of Health, Bethesda, Maryland. Electronic address: echew@nei.nih.gov.
  • Martin Friedlander
    Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA.
  • Glenn J Jaffe
    Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina.
  • Sina Farsiu
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Keywords

External Resources

Popular Topics
Recent Journals