Prediction of vitreomacular traction syndrome outcomes with deep learning: A pilot study.

Journal: European journal of ophthalmology
PMID: 38809664

Abstract

PURPOSE: To investigate the potential of an Optical Coherence Tomography (OCT) based Deep-Learning (DL) model in the prediction of Vitreomacular Traction (VMT) syndrome outcomes.

Authors

  • Eiman Usmani
    Discipline of Ophthalmology and Visual Science, University of Adelaide, Adelaide, Australia.
  • Stephen Bacchi
    Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA 5000 Australia.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Chelsea Guymer
    Discipline of Ophthalmology and Visual Science, University of Adelaide, Adelaide, Australia.
  • Amber Kraczkowska
    Discipline of Ophthalmology and Visual Science, University of Adelaide, Adelaide, Australia.
  • Javen Qinfeng Shi
    Institute of Machine Learning, University of Adelaide, Adelaide, Australia.
  • Jagjit Gilhotra
    Discipline of Ophthalmology and Visual Science, University of Adelaide, Adelaide, Australia.
  • Weng Onn Chan
    Faculty of Health & Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.

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