Rozema Jos J, Rodriguez Pablo, Ruiz Hidalgo Irene, Navarro Rafael, Tassignon Marie-José, Koppen Carina
Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium.
Department of Medicine and Health Sciences, Antwerp University, Wilrijk, Belgium.
Ophthalmic Physiol Opt. 2017 May;37(3):358-365. doi: 10.1111/opo.12369. Epub 2017 Mar 17.
To present and validate a stochastic eye model for developing keratoconus to e.g. improve optical corrective strategies. This could be particularly useful for researchers that do not have access to original keratoconic data.
The Scheimpflug tomography, ocular biometry and wavefront of 145 keratoconic right eyes were collected. These data were processed using principal component analysis for parameter reduction, followed by a multivariate Gaussian fit that produces a stochastic model for keratoconus (SyntEyes KTC). The output of this model is filtered to remove the occasional incorrect topography patterns by either an automatic or manual procedure. Finally, the output of this keratoconus model is matched to that of the original model for normal eyes using the non-corneal biometry to obtain a description of keratoconus development.
The synthetic data generated by the model were found to be significantly equal to the original data (non-parametric Mann-Whitney equivalence test; 145/154 passed). The variability of the synthetic data, however, was often significantly less than that of the original data, especially for the higher order Zernike terms of corneal elevation (non-parametric Levene test; p < 0.05/154). These results remained generally the same after applying either filter procedure to remove the synthetic eyes with incorrect topographies. Interpolation between matched pairs of normal and keratoconic SyntEyes appears to provide an adequate model for keratoconus progression.
The synthetic data provided by the proposed keratoconus model closely resembles actual clinical data and may be used for a range of research applications when (sufficient) real data is not available.
提出并验证一种用于圆锥角膜发展的随机眼模型，例如改进光学矫正策略。这对于无法获取原始圆锥角膜数据的研究人员可能特别有用。
收集了145只圆锥角膜右眼的Scheimpflug断层扫描、眼生物测量和波前数据。这些数据通过主成分分析进行处理以减少参数，随后进行多变量高斯拟合，生成圆锥角膜随机模型（SyntEyes KTC）。该模型的输出通过自动或手动程序进行过滤，以去除偶尔出现的不正确地形图模式。最后，使用非角膜生物测量将该圆锥角膜模型的输出与正常眼原始模型的输出进行匹配，以获得圆锥角膜发展的描述。
发现该模型生成的合成数据与原始数据显著相等（非参数曼 - 惠特尼等效性检验；145/154通过）。然而，合成数据的变异性通常明显小于原始数据，特别是对于角膜高度的高阶泽尼克项（非参数莱文检验；p < 0.05/154）。在应用任何一种过滤程序以去除具有不正确地形图的合成眼后，这些结果总体上保持不变。正常和圆锥角膜SyntEyes匹配对之间的插值似乎为圆锥角膜进展提供了一个适当的模型。
所提出的圆锥角膜模型提供的合成数据与实际临床数据非常相似，并且在（足够的）真实数据不可用时可用于一系列研究应用。
