Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography.

Abstract

Authors

• Michael G Kawczynski
  From the Department of Radiology and Biomedical Imaging (Y.D., J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H., Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences (J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, Calif (Y.D.); and Department of Radiology, University of California, Davis, Sacramento, Calif (L.N.).
• Thomas Bengtsson
  Genentech, Inc., South San Francisco, CA, USA.
• Jian Dai
  Genentech, Inc., South San Francisco, CA, USA.
• J Jill Hopkins
  Genentech, Inc., South San Francisco, CA, USA.
• Simon S Gao
  gRED Computational Science, Genentech, Inc., South San Francisco, California.
• Jeffrey R Willis
  Genentech, Inc., San Francisco, California, United States.

Keywords

External Resources