Development and evaluation of a deep neural network model for orthokeratology lens fitting.

Abstract

Purpose: 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.

Conclusion: 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.