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Jos J. Rozema, David A. Atchison, Marie-José Tassignon; Statistical Eye Model for Normal Eyes. Invest. Ophthalmol. Vis. Sci. 2011;52(7):4525-4533. doi: https://doi.org/10.1167/iovs.10-6705.
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© ARVO (1962-2015); The Authors (2016-present)
To create a binocular statistical eye model based on previously measured ocular biometric data.
Thirty-nine parameters were determined for a group of 127 healthy subjects (37 male, 90 female; 96.8% Caucasian) with an average age of 39.9 ± 12.2 years and spherical equivalent refraction of −0.98 ± 1.77 D. These parameters described the biometry of both eyes and the subjects' age. Missing parameters were complemented by data from a previously published study. After confirmation of the Gaussian shape of their distributions, these parameters were used to calculate their mean and covariance matrices. These matrices were then used to calculate a multivariate Gaussian distribution. From this, an amount of random biometric data could be generated, which were then randomly selected to create a realistic population of random eyes.
All parameters had Gaussian distributions, with the exception of the parameters that describe total refraction (i.e., three parameters per eye). After these non-Gaussian parameters were omitted from the model, the generated data were found to be statistically indistinguishable from the original data for the remaining 33 parameters (TOST [two one-sided t tests]; P < 0.01). Parameters derived from the generated data were also significantly indistinguishable from those calculated with the original data (P > 0.05). The only exception to this was the lens refractive index, for which the generated data had a significantly larger SD.
A statistical eye model can describe the biometric variations found in a population and is a useful addition to the classic eye models.
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