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

机译:基于训练数据集的预测LASIK后角膜平滑度的线性滤波模型的优化

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摘要

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.
机译:激光视力矫正是一个快速发展的领域,它通过主要的激光辅助原位角膜磨镶术(LASIK)程序来矫正近视,远视和散光。尽管该技术对校正球面像差效果很好,但由于手术后意外引发HOA,因此不能完全校正高阶像差(HOA),尤其是球面像差(SA)。角膜上皮重塑被提议作为解释这种HOA诱导过程的一种来源。这项工作提出了一种双尺度线性滤波内核来对这种过程进行建模。使用几个回顾性临床数据集作为训练数据集来构建模型,并使用下坡单纯形算法来优化内核的两个自由参数。在先前未用于优化的新临床数据集上测试了优化内核的性能。

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