Jos J Rozema1,2, Pablo Rodriguez3, Irene Ruiz Hidalgo1,2, Rafael Navarro3, Marie-José Tassignon1,2, Carina Koppen1,2. 1. Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium. 2. Department of Medicine and Health Sciences, Antwerp University, Wilrijk, Belgium. 3. Facultad de Ciencias, ICMA, Consejo Superior de Investigaciones Científicas & Universidad de Zaragoza, Zaragoza, Spain.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Jos J Rozema; Gareth D Hastings; Marta Jiménez-García; Carina Koppen; Raymond A Applegate Journal: Ophthalmic Physiol Opt Date: 2022-09-16 Impact factor: 3.992
Authors: Pablo Pérez-Merino; Damian Siedlecki; Laura Remón; Maria Vinas; Jorge L Alió; Jos J Rozema Journal: J Ophthalmol Date: 2020-02-25 Impact factor: 1.909
Authors: Ali H Al-Timemy; Zahraa M Mosa; Zaid Alyasseri; Alexandru Lavric; Marcelo M Lui; Rossen M Hazarbassanov; Siamak Yousefi Journal: Transl Vis Sci Technol Date: 2021-12-01 Impact factor: 3.283