Using a Deep Learning Model to Predict Postoperative Visual Outcomes of Idiopathic Epiretinal Membrane Surgery.

Journal: American journal of ophthalmology
PMID: 39814096

Abstract

PURPOSE: This study assessed the performance of various deep learning models in predicting the postoperative outcomes of idiopathic epiretinal membrane (ERM) surgery based on preoperative optical coherence tomography (OCT) images.

Authors

  • Hsin-LE Lin
    From the Department of Ophthalmology (H.L.L, P.C.T), Ren-Ai Branch, Taipei City Hospital, Taipei, Taiwan; Graduate Institute of Data Science (H.L.L, M.H.H), College of Management, Taipei Medical University, Taiwan.
  • Po-Chen Tseng
    From the Department of Ophthalmology (H.L.L, P.C.T), Ren-Ai Branch, Taipei City Hospital, Taipei, Taiwan; Department of Special Education (P.C.T), University of Taipei, Taipei, Taiwan; Department of Ophthalmology, School of Medicine (P.C.T), College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Optometry, University of Kang-Ning, Taipei, Taiwan.
  • Min-Huei Hsu
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Syu-Jyun Peng
    Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsin-Chu, Taiwan; Institute of Electronics, National Chiao Tung University, Hsin-Chu, Taiwan. Electronic address: blue.year@msa.hinet.net.

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