Experimental Artificial Intelligence Systems in Ophthalmology: An Overview
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-78601-4_7
Reference67 articles.
1. Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–75. https://doi.org/10.1136/bjophthalmol-2018-313173.
2. Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Exp Ophthalmol. 2019;47(1):128–39. https://doi.org/10.1111/ceo.13381.
3. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25:24–9. https://doi.org/10.1038/s41591-018-0316-z.
4. Treder M, Lauermann JL, Eter N. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier. Graefes Arch Clin Exp Ophthalmol. 2018;256(11):2053–60. https://doi.org/10.1007/s00417-018-4098-2.
5. Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136(7):803–10. https://doi.org/10.1001/jamaophthalmol.2018.1934.
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