A Predictive Model for Personalized Fitting of Peripheral Defocus Spectacles Based on Ocular Biometric Parameters

To construct a predictive model for personalized recommendation of peripheral defocus spectacle designs based on ocular biometric parameters.

This retrospective cohort study included 2824 myopic children who wore peripheral defocus spectacles at the Ophthalmology Center of Peking University People's Hospital. Participants were divided into five groups according to the spectacle designs (Diversified Segmental Defocus Optimization [DSDO], Diffusion Optics Technology [DOT], Defocus Induced Multiple Segment [DIMS], Highly Aspherical Lenslets [HALT], Hexagon Optimized Reticular Integration [HORI]) and matched for gender, age, and baseline biometric parameters. The dataset was randomly split into training (80%) and validation (20%) sets for model evaluation. Separate multiple linear regression prediction models based on ocular biometric parameters were constructed for each designs. Finally, an R language function was developed for clinical personalized recommendation.

After matching baseline parameters, no statistically significant difference was observed in axial elongation control efficacy among the five peripheral defocus spectacles designs (P = 0.28). The coefficient of determination (R²) ranged from 0.89 to 0.96 in the training set and from 0.88 to 0.97 in the validation set; the root mean square error (RMSE) ranged from 0.05 mm to 0.07 mm in the training set and from 0.04 mm to 0.07 mm in the validation set. Calibration plots showed good agreement between predicted and observed values.

Although no significant differences in average efficacy were observed among the five designs, this study successfully established predictive models based on ocular biometric parameters to guide personalized fitting of peripheral defocus spectacles.