Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.

Journal: Translational vision science & technology
PMID: 38809531

Abstract

PURPOSE: To develop convolutional neural network (CNN)-based models for predicting the axial length (AL) using color fundus photography (CFP) and explore associated clinical and structural characteristics.

Authors

  • Che-Ning Yang
    School of Medicine, National Taiwan University, Taipei, Taiwan.
  • Wei-Li Chen
    Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan.
  • Hsu-Hang Yeh
    Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA.
  • Hsiao-Sang Chu
    Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan.
  • Jo-Hsuan Wu
    Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
  • Yi-Ting Hsieh
    Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan.

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