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DOI: 10.1055/s-0039-1685535
Learning Algorithms for Ophthalmologists: A Conceptual Primer
Publikationsverlauf
26. November 2018
05. März 2019
Publikationsdatum:
02. Mai 2019 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00033285/201901/lookinside/thumbnails/10-1055-s-0039-1685535_115ed-1.jpg)
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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References
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