Abstract：
OBJECTIVE: To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluations, thereby augmenting patient safety in the context of increasing global myopia.
METHODS: A retrospective study of successful orthokeratology treatment was conducted on 266 patients, with 449 eyes being analysed. A DNN model with an 80%-20% training-validation split predicted lens parameters (curvature, power and diameter) using corneal topography and refractive indices. The model featured two hidden layers for precision.
RESULTS: The DNN model achieved mean absolute errors of 0.21 D for alignment curvature (AC), 0.19 D for target power (TP) and 0.02 mm for lens diameter (LD), with R2 values of 0.97, 0.95 and 0.91, respectively. Accuracy decreased for myopia of less than 1.00 D, astigmatism exceeding 2.00 D and corneal curvatures >45.00 D. Approximately, 2% of cases with unique physiological characteristics showed notable prediction variances.
CONCLUSIONS: While exhibiting high accuracy, the DNN model\'s limitations in specifying myopia, cylinder power and corneal curvature cases highlight the need for algorithmic refinement and clinical validation in orthokeratology practice.
METHODS: A retrospective study of successful orthokeratology treatment was conducted on 266 patients, with 449 eyes being analysed. A DNN model with an 80%-20% training-validation split predicted lens parameters (curvature, power and diameter) using corneal topography and refractive indices. The model featured two hidden layers for precision.
RESULTS: The DNN model achieved mean absolute errors of 0.21 D for alignment curvature (AC), 0.19 D for target power (TP) and 0.02 mm for lens diameter (LD), with R2 values of 0.97, 0.95 and 0.91, respectively. Accuracy decreased for myopia of less than 1.00 D, astigmatism exceeding 2.00 D and corneal curvatures >45.00 D. Approximately, 2% of cases with unique physiological characteristics showed notable prediction variances.
CONCLUSIONS: While exhibiting high accuracy, the DNN model\'s limitations in specifying myopia, cylinder power and corneal curvature cases highlight the need for algorithmic refinement and clinical validation in orthokeratology practice.
摘要：
目的：为了优化角膜塑形术的精度和疗效，这项调查评估了用于镜头拟合的深度神经网络（DNN）模型。目的是完善拟合程序的标准化并减少主观评估，从而在全球近视增加的背景下提高患者的安全性。
方法：对266例角膜塑形术治疗成功的患者进行了回顾性研究，449只眼睛被分析。具有80%-20%训练验证的DNN模型分割预测透镜参数(曲率，功率和直径）使用角膜地形图和折射率。该模型具有两个隐藏层的精度。
结果：DNN模型对于对准曲率（AC）实现了0.21D的平均绝对误差，目标光焦度(TP)为0.19D，透镜直径(LD)为0.02mm，R2值分别为0.97、0.95和0.91。小于1.00D的近视精度下降，散光超过2.00D，角膜曲率>45.00D。2％具有独特生理特征的病例显示出显着的预测差异。
结论：虽然具有很高的准确性，DNN模型在指定近视方面的局限性，柱面屈光力和角膜曲率病例强调了在角膜塑形术实践中需要算法改进和临床验证。
方法：对266例角膜塑形术治疗成功的患者进行了回顾性研究，449只眼睛被分析。具有80%-20%训练验证的DNN模型分割预测透镜参数(曲率，功率和直径）使用角膜地形图和折射率。该模型具有两个隐藏层的精度。
结果：DNN模型对于对准曲率（AC）实现了0.21D的平均绝对误差，目标光焦度(TP)为0.19D，透镜直径(LD)为0.02mm，R2值分别为0.97、0.95和0.91。小于1.00D的近视精度下降，散光超过2.00D，角膜曲率>45.00D。2％具有独特生理特征的病例显示出显着的预测差异。
结论：虽然具有很高的准确性，DNN模型在指定近视方面的局限性，柱面屈光力和角膜曲率病例强调了在角膜塑形术实践中需要算法改进和临床验证。
