Learning Algorithms for Ophthalmologists: A Conceptual Primer

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.

• References

• 1 Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Nature Digit Med 2018; 1 (01) 39
• 2 Sample PA, Goldbaum MH, Chan K. , et al. Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields. Invest Ophthalmol Vis Sci 2002; 43 (08) 2660-2665
• 3 Rojas J, Fernandez I, Pastor JC. , et al. Development of predictive models of proliferative vitreoretinopathy based on genetic variables: the Retina 4 project. Invest Ophthalmol Vis Sci 2009; 50 (05) 2384-2390
• 4 Goldbaum MH. Kozak I, Hao J. , et al. Pattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART. Graefes Arch Clin Exp Ophthalmol 2011; 294 (04) 491-498
• 5 Sample PA, Chan K, Boden C. , et al. Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 2004; 45 (08) 2596-2605
• 6 Elze T, Pasquale LR, Shen LQ, Chen TC, Wiggs JL, Bex PJ. Patterns of functional vision loss in glaucoma determined with archetypal analysis. J R Soc Interface 2015; 12 (103) 20141118
• 7 Gulshan V, Peng L, Coram M. , et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22) 2402-2410
• 8 Kass MA, Heuer DK, Higginbotham EJ. , et al. The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch Ophthalmol 2002; 120 (06) 701-713 , discussion 829–830