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Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
  1. Darren Shu Jeng Ting1,2,3,
  2. Valencia HX Foo4,
  3. Lily Wei Yun Yang5,
  4. Josh Tjunrong Sia6,
  5. Marcus Ang3,7,
  6. Haotian Lin8,
  7. James Chodosh9,
  8. Jodhbir S Mehta3,7,
  9. Daniel Shu Wei Ting3,10
  1. 1 Academic Ophthalmology, University of Nottingham, Nottingham, UK
  2. 2 Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK
  3. 3 Singapore Eye Research Institute, Singapore
  4. 4 Singapore National Eye Centre, Singapore
  5. 5 NUS Yong Loo Lin School of Medicine, Singapore
  6. 6 Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  7. 7 Cornea And Ext Disease, Singapore National Eye Centre, Singapore
  8. 8 Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
  9. 9 Ophthalmology, Massachusetts Eye and Ear Infirmary Howe Laboratory Harvard Medical School, Boston, Massachusetts, USA
  10. 10 Vitreo-retinal Department, Singapore National Eye Center, Singapore
  1. Correspondence to Dr Daniel Shu Wei Ting, Vitreo-retinal Department, Singapore National Eye Center, Singapore 168751, Singapore; daniel.ting.s.w{at}


With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for ‘intelligent’ healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.

  • cornea
  • conjunctiva
  • telemedicine
  • lens and zonules
  • glaucoma

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  • DSJT and VHF are joint first authors.

  • Correction notice This paper has been updated slightly since it was published online. A middle initial has been added to the eighth author's name.

  • Contributors DSJT, VHXF, LWYY and JTS contributed to the drafting of manuscript. DSJT, VHXF, MA, HL, JC, JM and DSWT contributed to the data analysis and interpretation. All authors contributed to the critical appraisal and final approval of the manuscript. DSWT provided the overall supervision of this work.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests DSWT is the coinventor and patent holder of a deep learning system for retinal diseases. JC is the editor-in-chief for British Journal of Ophthalmology.

  • Patient consent for publication Not required.

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