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Invited Commentary
March 26, 2020

Artificial Intelligence for Refractive Surgery Screening: Finding the Balance Between Myopia and Hype-ropia

Author Affiliations
  • 1Francis I. Proctor Foundation, University of California, San Francisco
  • 2Department of Ophthalmology, University of California, San Francisco,
  • 3Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
  • 4Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland
JAMA Ophthalmol. 2020;138(5):526-527. doi:10.1001/jamaophthalmol.2020.0515

Research involving applications of artificial intelligence (AI) in health care has expanded rapidly in recent years. In particular, deep learning methods using convolutional neural networks has demonstrated the ability to achieve expert-level performance in image-based diagnosis. Although the highest-profile AI work in ophthalmology has applied this technology to retinal disease, there are several potential applications to corneal pathologic conditions.1,2 Most work to date has focused on keratoconus owing to its high prevalence and primarily image-based diagnosis. Convolutional neural networks have been trained to detect keratoconus from corneal tomographic imaging with high accuracy and also show potential for monitoring progression of disease, but the ability to indicate the risk of postrefractive ectasia or future development of keratoconus remains elusive.3

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