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Artificial intelligence and deep learning in ophthalmology
  1. Daniel Shu Wei Ting1,
  2. Louis R Pasquale2,
  3. Lily Peng3,
  4. John Peter Campbell4,
  5. Aaron Y Lee5,
  6. Rajiv Raman6,
  7. Gavin Siew Wei Tan1,
  8. Leopold Schmetterer1,7,8,9,
  9. Pearse A Keane10,
  10. Tien Yin Wong1
  1. 1 Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
  2. 2 Department of Ophthalmology, Mt Sinai Hospital, New York City, New York, USA
  3. 3 Google AI Healthcare, Mountain View, California, USA
  4. 4 Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
  5. 5 Department of Ophthalmology, University of Washington, School of Medicine, Seattle, Washington, USA
  6. 6 Vitreo-retinal Department, Sankara Nethralaya, Chennai, Tamil Nadu, India
  7. 7 Department of Ophthalmology, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
  8. 8 Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
  9. 9 Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
  10. 10 Vitreo-retinal Service, Moorfields Eye Hospital, London, UK
  1. Correspondence to Dr Daniel Shu Wei Ting, Assistant Professor in Ophthalmology, Duke-NUS Medical SchoolSingapore National Eye Center, Singapore 168751, Singapore; daniel.ting.s.w{at}singhealth.com.sg

Abstract

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

  • imaging
  • retina
  • glaucoma
  • telemedicine
  • public health

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Footnotes

  • Contributors DSWT, LRP, LP, JPC, AYL, RR, GSWT, LS, PAK and TYW have all contributed to manuscript drafting, literature review, critical appraisal and final approval of the manuscript.

  • Funding This project received funding from the National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022), SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation, and unrestricted donations to the Retina Division, Johns Hopkins University School of Medicine. For the Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008 and STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550 and 09/1/35/19/616). The Singapore Integrated Diabetic Retinopathy Programme (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 and AIC/HPD/FY2016/0912). In USA, it is supported by the National Institutes of Health (K12 EY027720, R01EY019474, P30EY10572, P41EB015896), by the National Science Foundation (SCH-1622542, SCH-1622536, SCH-1622679) and by unrestricted departmental funding from Research to Prevent Blindness. PAK is supported by a UK National Institute for Health Research (NIHR) Clinician Scientist Award (NIHR-CS--2014-12-023). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

  • Competing interests DSWT and TYW are the coinventors of a deep learning system for retinal diseases. LP is a member of Google AI Healthcare. LRP is a non-paid consultant for Visulytix. PAK is a consultant for DeepMind.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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