CC BY-NC-ND 4.0 · Journal of Academic Ophthalmology 2019; 11(01): e22-e24
DOI: 10.1055/s-0039-1685535
Editorial
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Learning Algorithms for Ophthalmologists: A Conceptual Primer

Ariana M. Levin
1   John A. Moran Eye Center, University of Utah, Salt Lake City, Utah
,
Rebekah H. Gensure
1   John A. Moran Eye Center, University of Utah, Salt Lake City, Utah
,
Jeff Pettey
1   John A. Moran Eye Center, University of Utah, Salt Lake City, Utah
› Author Affiliations
Further Information

Publication History

26 November 2018

05 March 2019

Publication Date:
02 May 2019 (online)

Recent advances, including the first Food and Drug Administration (FDA)-approved artificial intelligence system for detection of diabetic retinopathy, have brought machine learning algorithms to the spotlight in ophthalmology clinical practice.[1] To determine the potential utility and applicability of these systems, it is important for the ophthalmology community to have a conceptual understanding of machine learning principles. Learning algorithms date back at least to the 1950s. In review of published literature, the earliest available studies of machine learning in ophthalmology dated back to 2002 by Sample et al who described patterns in glaucomatous field defects.[2] One of the earliest studies on machine learning for vitreoretinal diseases was an analysis of genetic predictors of proliferative vitreoretinopathy published in 2009.[3] The vast majority of papers on machine learning in ophthalmology have been published in the past 5 years, on a broad range of topics with an increasing emphasis on image analysis. Machine learning has the potential to facilitate diagnosis and optimize treatments. In this article, we aim to provide a broad audience with a framework for understanding and applying basic principles of learning algorithms.

 
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