Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.

Journal: Translational vision science & technology
PMID: 39847375

Abstract

PURPOSE: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.

Authors

  • Kornchanok Sriwatana
    Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
  • Chanon Puttanawarut
    Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand.
  • Yanin Suwan
    Glaucoma Service (W.S., Y.S.), Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
  • Titipat Achakulvisut
    Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.

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