Artificial Intelligence for Keratoconus Detection and Refractive Surgery Screening
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-78601-4_15
Reference35 articles.
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2. Kovács I, Miháltz K, Kránitz K, Juhász É, Takács Á, Dienes L, et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg. 2016;42(2):275–83. https://doi.org/10.1016/j.jcrs.2015.09.020.
3. Jonas JB, Nangia V, Matin A, Kulkarni M, Bhojwani K. Prevalence and associations of keratoconus in rural Maharashtra in Central India: the Central India eye and medical study. Am J Ophthalmol. 2009;148(5):760–5. https://doi.org/10.1016/j.ajo.2009.06.024.
4. Kennedy RH, Bourne WM, Dyer JA. A 48-year clinical and epidemiologic study of keratoconus. Am J Ophthalmol. 1986;101(3):267–73.
5. Lin SR, Ladas JG, Bahadur GG, Al-Hashimi S, Pineda R. A review of machine learning techniques for keratoconus detection and refractive surgery screening. Semin Ophthalmol. 2019;34(4):317–26. https://doi.org/10.1080/08820538.2019.1620812.
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1. Deep learning models based on CNN architecture for early keratoconus detection using corneal topographic maps;Multimedia Tools and Applications;2023-10-28
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