Applications of Artificial Intelligence and Deep Learning in Glaucoma.

Journal: Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Published Date: Jan 12, 2023

Abstract

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

Authors

  • Dinah Chen
    NYU Langone Health, Department of Ophthalmology, New York University School of Medicine, New York, New York.
  • An Ran Ran
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.
  • Ting Fang Tan
    Singapore Eye Research Institute, Singapore.
  • Rithu Ramachandran
    Kellogg Eye Center, University of Michigan, Ann Arbor, MI.
  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, 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.
  • Siamak Yousefi
    Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.
  • Clement C Y Tham
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.
  • Daniel S W Ting
    Singapore National Eye Center, Duke-National University of Singapore Medical School, Singapore 168751, Singapore; National Institutes of Health Research Biomedical Research Centre Biomedical Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Electronic address: daniel.ting.s.w@singhealth.com.sg.
  • Xiulan Zhang
    Zhongshan Ophthalmic Center, Sun Yat-sen University, China. Electronic address: zhangxl2@mail.sysu.edu.cn.
  • Lama A Al-Aswad
    Columbia University Medical Center, Harkness Eye Institute, New York, New York, USA. Electronic address: laa2003@cumc.columbia.edu.

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