Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

Journal: Translational vision science & technology
PMID: 39102242

Abstract

PURPOSE: To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.

Authors

  • Julia Cluceru
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
  • Neha Anegondi
    Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California.
  • Simon S Gao
    gRED Computational Science, Genentech, Inc., South San Francisco, California.
  • Aaron Y Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington.
  • Eleonora M Lad
    Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina.
  • Usha Chakravarthy
    Centre for Experimental Medicine, Institute of Clinical Science, Queen's University Belfast, Belfast, UK. u.chakravarthy@qub.ac.uk.
  • Qi Yang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.).
  • Verena Steffen
    gRED Computational Science, Genentech, Inc., South San Francisco, California.
  • Michel Friesenhahn
    Genentech, Inc., South San Francisco, CA, USA.
  • Christina Rabe
    Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biostatistics, Genentech, Inc., South San Francisco, California.
  • Daniela Ferrara
    Genentech, Inc., South San Francisco, California.

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