Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

Journal: Translational vision science & technology
PMID: 35551345

Abstract

PURPOSE: To develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datasets and on cropped ONH scans.

Authors

  • Erfan Noury
    Matroid, Palo Alto, CA, USA.
  • Suria S Mannil
    Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA.
  • Robert T Chang
    Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
  • An Ran Ran
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Carol Y Cheung
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China. Electronic address: carolcheung@cuhk.edu.hk.
  • Suman S Thapa
    Tilganga Institute of Ophthalmology, Kathmandu, Nepal.
  • Harsha L Rao
    Narayana Nethralaya Foundation, Bangalore-India.
  • Srilakshmi Dasari
    Narayana Nethralaya Foundation, Bangalore-India.
  • Mohammed Riyazuddin
    Narayana Nethralaya, Bangalore, India.
  • Dolly Chang
    Byers Eye Institute, Stanford University, Palo Alto, CA, USA.
  • Sriharsha Nagaraj
    Narayana Nethralaya Foundation, Bangalore-India.
  • Clement C Tham
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Hong Kong Eye Hospital, Hong Kong Special Administrative Region, China; Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
  • Reza Zadeh
    Matroid, Palo Alto, CA, USA.

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