Langenbucher Achim, Szentmáry Nóra, Weisensee Johannes, Cayless Alan, Menapace Rupert, Hoffmann Peter
Department of Experimental Ophthalmology, Saarland University, Homburg/Saar, Germany.
Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research, Saarland University, Homburg/Saar, Germany.
Acta Ophthalmol. 2021 Dec;99(8):843-849. doi: 10.1111/aos.14794. Epub 2021 Feb 11.
This study aims to develop a raytracing-based strategy for calculating corneal power from anterior segment optical coherence tomography data and extracting the individual keratometer index, which converts the corneal front surface radius to corneal power.
A large OCT dataset (10,218 eyes of 8,430 patients) from the Casia 2 (Tomey, Japan) was post-processed in MATLAB (MathWorks, USA). Radius of curvature, asphericity of the corneal front and back surface, central corneal thickness and pupil size (aperture) were used to trace a bundle of rays through the cornea and derive the best focus plane. Corneal power was calculated with respect to the corneal front vertex plane, and the keratometer index was back-calculated using corneal power and front surface radius. Keratometer index was analysed in a multivariate linear model.
The averaged resulting keratometer index was 1.3317 ± 0.0017 with a median of 1.3317 and range from 1.3233 to 1.3390. In a univariate model, only the front surface asphericity affected the keratometer index. The multivariate model for modelling the keratometer index using all 6 input parameters performed very well (RMS error: 5.54e-4, R : 0.90, significance vs. constant model: <0.0001).
In the classical calculation, the keratometer index used for converting corneal radius to dioptric power uses several model assumptions. As these assumptions are not generally satisfied, corneal power cannot be calculated from corneal front surface radius alone. Considering all 6 input variables, the linear prediction model performs well and can be used if all input parameters are measured with a tomographer.
本研究旨在开发一种基于光线追踪的策略，用于从前节光学相干断层扫描数据计算角膜屈光力，并提取个体角膜曲率计指数，该指数可将角膜前表面半径转换为角膜屈光力。
对来自日本多美公司的Casia 2的大型光学相干断层扫描数据集（8430例患者的10218只眼）在MATLAB（美国MathWorks公司）中进行后处理。利用角膜前、后表面的曲率半径、非球面性、中央角膜厚度和瞳孔大小（孔径）来追踪一束光线穿过角膜，并得出最佳焦平面。相对于角膜前顶点平面计算角膜屈光力，并使用角膜屈光力和前表面半径反算角膜曲率计指数。在多变量线性模型中分析角膜曲率计指数。
所得角膜曲率计指数的平均值为1.3317±0.0017，中位数为1.3317，范围为1.3233至1.3390。在单变量模型中，仅前表面非球面性影响角膜曲率计指数。使用所有6个输入参数对角膜曲率计指数进行建模的多变量模型表现良好（均方根误差：5.54e - 4，R：0.90，与常数模型相比的显著性：<0.0001）。
在经典计算中，用于将角膜半径转换为屈光力的角膜曲率计指数使用了几个模型假设。由于这些假设通常不成立，仅根据角膜前表面半径无法计算角膜屈光力。考虑所有6个输入变量时，线性预测模型表现良好，并且如果所有输入参数都通过断层扫描仪测量，则可以使用该模型。
