Article Text

Download PDFPDF
Glaucoma management in the era of artificial intelligence
  1. Sripad Krishna Devalla1,
  2. Zhang Liang1,
  3. Tan Hung Pham1,2,
  4. Craig Boote1,3,4,
  5. Nicholas G Strouthidis2,5,6,
  6. Alexandre H Thiery7,
  7. Michael J A Girard1,2
  1. 1Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
  2. 2Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
  3. 3School of Optometry & Vision Sciences, Cardiff University, Cardiff, UK
  4. 4Newcastle Research & Innovation Institute, Singapore, Singapore
  5. 5NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
  6. 6Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Sydney, New South Wales, Australia
  7. 7Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
  1. Correspondence to Dr Michael J A Girard, Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore; mgirard{at}nus.edu.sg

Abstract

Glaucoma is a result of irreversible damage to the retinal ganglion cells. While an early intervention could minimise the risk of vision loss in glaucoma, its asymptomatic nature makes it difficult to diagnose until a late stage. The diagnosis of glaucoma is a complicated and expensive effort that is heavily dependent on the experience and expertise of a clinician. The application of artificial intelligence (AI) algorithms in ophthalmology has improved our understanding of many retinal, macular, choroidal and corneal pathologies. With the advent of deep learning, a number of tools for the classification, segmentation and enhancement of ocular images have been developed. Over the years, several AI techniques have been proposed to help detect glaucoma by analysis of functional and/or structural evaluations of the eye. Moreover, the use of AI has also been explored to improve the reliability of ascribing disease prognosis. This review summarises the role of AI in the diagnosis and prognosis of glaucoma, discusses the advantages and challenges of using AI systems in clinics and predicts likely areas of future progress.

  • Glaucoma
  • Imaging
  • Optic Nerve
View Full Text

Statistics from Altmetric.com

Footnotes

  • Contributors SKD was the first author and drafted the manuscript. ZL coauthored and provided review on the usage of AI for functional analysis. THP coauthored and provided review on the usage of AI for structural analysis. CB, NGS, AHT and MJAG critically reviewed the manuscript.

  • Funding This work was supported by the Singapore Ministry of Education Academic Research Funds Tier 1 (R-397-000-294-114 (MJAG)); and the Singapore Ministry of Education Tier 2 (R-397-000-280-112, R-397-000-308-112 (MJAG)).

  • Competing interests MJAG and AHT are co-founders of Abyss Processing.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; externally peer reviewed.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.