Optimization of Linear Filtering Model to Predict Post-LASIK Corneal Smoothing Based on Training Data Sets

Anatoly Fabrikant, Guang-Ming Dai, Dimitri Chernyak

Research and Development, Abbott Medical Optics, Milpitas, USA.

DOI: 10.4236/am.2013.412230                

Abstract

Laser vision correction is a rapidly growing field for correcting nearsightedness, farsightedness as well as astigmatism with dominating laser-assisted in situ keratomileusis (LASIK) procedures. While the technique works well for correcting spherocylindrical aberrations, it does not fully correct high order aberrations (HOAs), in particular spherical aberration (SA), due to unexpected induction of HOAs post-surgery. Corneal epithelial remodeling was proposed as one source to account for such HOA induction process. This work proposes a dual-scale linear filtering kernel to model such a process. Several retrospective clinical data sets were used as training data sets to construct the model, with a downhill simplex algorithm to optimize the two free parameters of the kernel. The performance of the optimized kernel was testedon new clinical data sets that were not previously used for the optimization.

Keywords

Simulation-Driven Optimization; Downhill Simplex Method; Corneal Smoothing; LASIK

Conflicts of Interest

The authors declare no conflicts of interest.

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