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Artificial intelligence and deep learning in ophthalmology.

Publication ,  Journal Article
Ting, DSW; Pasquale, LR; Peng, L; Campbell, JP; Lee, AY; Raman, R; Tan, GSW; Schmetterer, L; Keane, PA; Wong, TY
Published in: Br J Ophthalmol
February 2019

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.

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Published In

Br J Ophthalmol

DOI

EISSN

1468-2079

Publication Date

February 2019

Volume

103

Issue

2

Start / End Page

167 / 175

Location

England

Related Subject Headings

  • Ophthalmology & Optometry
  • Ophthalmology
  • Humans
  • Eye Diseases
  • Deep Learning
  • Artificial Intelligence
  • Animals
  • 3212 Ophthalmology and optometry
  • 3202 Clinical sciences
  • 1117 Public Health and Health Services
 

Citation

APA
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Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., … Wong, T. Y. (2019). Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol, 103(2), 167–175. https://doi.org/10.1136/bjophthalmol-2018-313173
Ting, Daniel Shu Wei, Louis R. Pasquale, Lily Peng, John Peter Campbell, Aaron Y. Lee, Rajiv Raman, Gavin Siew Wei Tan, Leopold Schmetterer, Pearse A. Keane, and Tien Yin Wong. “Artificial intelligence and deep learning in ophthalmology.Br J Ophthalmol 103, no. 2 (February 2019): 167–75. https://doi.org/10.1136/bjophthalmol-2018-313173.
Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019 Feb;103(2):167–75.
Ting, Daniel Shu Wei, et al. “Artificial intelligence and deep learning in ophthalmology.Br J Ophthalmol, vol. 103, no. 2, Feb. 2019, pp. 167–75. Pubmed, doi:10.1136/bjophthalmol-2018-313173.
Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019 Feb;103(2):167–175.

Published In

Br J Ophthalmol

DOI

EISSN

1468-2079

Publication Date

February 2019

Volume

103

Issue

2

Start / End Page

167 / 175

Location

England

Related Subject Headings

  • Ophthalmology & Optometry
  • Ophthalmology
  • Humans
  • Eye Diseases
  • Deep Learning
  • Artificial Intelligence
  • Animals
  • 3212 Ophthalmology and optometry
  • 3202 Clinical sciences
  • 1117 Public Health and Health Services