TITLE:
Optimization of Linear Filtering Model to Predict Post-LASIK Corneal Smoothing Based on Training Data Sets
AUTHORS:
Anatoly Fabrikant, Guang-Ming Dai, Dimitri Chernyak
KEYWORDS:
Simulation-Driven Optimization; Downhill Simplex Method; Corneal Smoothing; LASIK
JOURNAL NAME:
Applied Mathematics,
Vol.4 No.12,
December
5,
2013
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.