Statistical Model of Ocular Wavefronts With Accommodation.

IF 5 2区 医学 Q1 OPHTHALMOLOGY
María Mechó-García, María Arcas-Carbonell, Elvira Orduna-Hospital, Ana Sánchez-Cano, Norberto López-Gil, Rute J Macedo-de-Araújo, Miguel Faria-Ribeiro, Paulo Fernandes, José Manuel González-Méijome, Jos Rozema
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Abstract

Purpose: The purpose of this study was to determine the minimum number of orthonormal basis functions, applying Principal Component Analysis (PCA), to represent the most wavefront aberrations at different accommodation stages. The study also aims to generate synthetic wavefront data using these functions.

Methods: Monocular wavefront data from 191 subjects (26.15 ± 5.56 years old) were measured with a Hartmann-Shack aberrometer, simulating accommodation from 0 diopters (D) to 5 D in 1 D steps. The wavefronts for each accommodative demand were rescaled for different pupil sizes: 4.66, 4.76, 4.40, 4.09, 4.07, and 3.68 mm. PCA was applied to 150 wavefront parameters (25 Zernike coefficients × 6 accommodation levels) to obtain eigenvectors for dimensional reduction. A total of 49 eigenvectors were modeled as a sum of 2 multivariate Gaussians, from which 1000 synthetic data sets were generated.

Results: The first 49 eigenvectors preserved 99.97% of the original data variability. No significant differences were observed between the mean values and standard deviation of the generated and original 49 eigenvectors (two one-sided test [TOST], P > 0.05/49) and (F-test, P > 0.05/49), both with Bonferroni correction. The mean values of the generated parameters (1000) were statistically equal to those of the original data (TOST, P > 0.05/150). The variability of the generated data was similar to the original data for the most important Zernike coefficients (F-test, P > 0.05/150).

Conclusions: PCA significantly reduces the dimensionality of wavefront aberration data across 6 accommodative demands, reducing the variable space by over 66%. The synthetic data generated by the proposed wavefront model for accommodation closely resemble the original clinical data.

适应性眼波前沿统计模型
目的:本研究的目的是利用主成分分析法(PCA)确定代表不同调适阶段大多数波前像差的正交基函数的最小数目。研究还旨在利用这些函数生成合成波前数据:使用哈特曼-沙克像差仪测量了 191 名受试者(26.15 ± 5.56 岁)的单眼波前数据,模拟了从 0 度(D)到 5 度(以 1 度为单位)的调节。根据不同的瞳孔大小,对每种适应性需求的波前进行了重新调整:4.66、4.76、4.40、4.09、4.07 和 3.68 毫米。对 150 个波前参数(25 个 Zernike 系数 × 6 个适应度水平)进行 PCA 处理,以获得用于降维的特征向量。共有 49 个特征向量被建模为 2 个多元高斯之和,并从中生成了 1000 个合成数据集:前 49 个特征向量保留了原始数据 99.97% 的可变性。生成的 49 个特征向量的平均值和标准偏差与原始 49 个特征向量的平均值和标准偏差无明显差异(两个单侧检验[TOST],P > 0.05/49)和(F 检验,P > 0.05/49),均进行了 Bonferroni 校正。生成参数的平均值(1000)与原始数据的平均值在统计学上相等(TOST,P > 0.05/150)。就最重要的 Zernike 系数而言,生成数据的变异性与原始数据相似(F 检验,P > 0.05/150):PCA大大降低了6种适应性要求下波前像差数据的维度,将变量空间缩小了66%以上。所提出的适应性波前模型生成的合成数据与原始临床数据非常相似。
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来源期刊
CiteScore
6.90
自引率
4.50%
发文量
339
审稿时长
1 months
期刊介绍: Investigative Ophthalmology & Visual Science (IOVS), published as ready online, is a peer-reviewed academic journal of the Association for Research in Vision and Ophthalmology (ARVO). IOVS features original research, mostly pertaining to clinical and laboratory ophthalmology and vision research in general.
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