Purpose

To construct a machine learning (ML)-based model for estimating the alignment curve (AC) curvature in orthokeratology lens fitting for vision shaping treatment (VST), which can minimize the number of lens trials, improving efficiency while maintaining accuracy, with regards to its improvement over a previous calculation method.

Methods

Data were retrospectively collected from the clinical case files of 1271 myopic subjects (1271 right eyes). The AC curvatures calculated with a previously published algorithm were used as the target data sets. Four kinds of machine learning algorithms were implemented in the experimental analyses to predict the targeted AC curvatures: robust linear regression models, support vector machine (SVM) regression models with linear kernel functions, bagging decision trees, and Gaussian processes. The previously published calculation method and the novel machine learning method were then compared to assess the final parameters of ordered lenses.

Results

The linear SVM and Gaussian process machine learning models achieved the best performance. The input variables included sex, age, horizontal visible iris diameter (HVID), spherical refraction (SER), cylindrical refraction, eccentricity value (e value), flat K (K1) and steep K (K2) readings, anterior chamber depth (ACD), and axial length (AL). The R-squared values for the output AC1K1, AC1K2 and AC2K1 values were 0.91, 0.84, and 0.73, respectively. The previous calculation method and machine learning methods displayed excellent consistency, and the proposed methods performed best based on flat K reading and e values.

Conclusions

The ML model can provide practitioners with an efficient method for estimating the AC curvatures of VST lenses and reducing the probability of cross-infection originating from trial lenses, which is especially useful during pandemics, such as that for COVID-19.

中文翻译：

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机器学习算法提高了视力整形治疗中 Ortho-K 镜片拟合的准确性

目的

构建基于机器学习 (ML) 的模型，用于估计用于视力整形治疗 (VST) 的角膜塑形镜验配中的对准曲线 (AC) 曲率，该模型可以最大限度地减少镜片试验次数，在保持准确性的同时提高效率，关于它对以前的计算方法的改进。

方法

从 1271 名近视受试者（1271 只右眼）的临床病例档案中回顾性收集数据。使用先前发布的算法计算的 AC 曲率用作目标数据集。在实验分析中实施了四种机器学习算法来预测目标 AC 曲率：鲁棒线性回归模型、具有线性核函数的支持向量机 (SVM) 回归模型、装袋决策树和高斯过程。然后将先前公布的计算方法和新的机器学习方法进行比较，以评估有序镜片的最终参数。

结果

线性 SVM 和高斯过程机器学习模型取得了最佳性能。输入变量包括性别、年龄、水平可见虹膜直径 (HVID)、球面折射 (SER)、柱面折射、偏心率值 (e 值)、平坦 K (K1) 和陡峭 K (K2) 读数、前房深度 (ACD ) 和轴向长度 (AL)。输出 AC1K1、AC1K2 和 AC2K1 值的R平方值分别为 0.91、0.84 和 0.73。先前的计算方法和机器学习方法表现出极好的一致性，所提出的方法基于平坦的 K 读数和 e 值表现最佳。

结论

ML 模型可以为从业者提供一种有效的方法来估计 VST 镜片的 AC 曲率并降低源自试验镜片的交叉感染的可能性，这在大流行期间尤其有用，例如 COVID-19。