Enhanced morphological assessment based on interocular asymmetry analysis for keratoconus detection
Author:
Funder
Peking University Third Hospital
Publisher
Springer Science and Business Media LLC
Subject
Cellular and Molecular Neuroscience,Sensory Systems,Ophthalmology
Link
https://link.springer.com/content/pdf/10.1007/s00417-023-06250-7.pdf
Reference29 articles.
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2. Cao K, Verspoor K, Sahebjada S, Baird PN (2022) Accuracy of machine learning assisted detection of keratoconus: a systematic review and meta-analysis. J Clin Med 11(3). https://doi.org/10.3390/jcm11030478
3. Xie Y, Zhao L, Yang X, Wu X, Yang Y, Huang X, Liu F, Xu J, Lin L, Lin H, Feng Q, Lin H, Liu Q (2020) Screening candidates for refractive surgery with corneal tomographic-based deep learning. JAMA Ophthalmol 138(5):519–526. https://doi.org/10.1001/jamaophthalmol.2020.0507
4. Falavarjani KG, Modarres M, Joshaghani M, Azadi P, Afshar AE, Hodjat P (2010) Interocular differences of the Pentacam measurements in normal subjects. Clin Exp Optom 93(1):26–30. https://doi.org/10.1111/j.1444-0938.2009.00446.x
5. Durr GM, Auvinet E, Ong J, Meunier J, Brunette I (2015) Corneal shape, volume, and interocular symmetry: parameters to optimize the design of biosynthetic corneal substitutes. Invest Ophthalmol Vis Sci 56(8):4275–4282. https://doi.org/10.1167/iovs.15-16710
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