ArticleClinical optometry2026
A Predictive Model for Personalized Fitting of Peripheral Defocus Spectacles Based on Ocular Biometric Parameters.
Article in Clinical optometry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
Authors and funding
5 authors.
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Abstract
Purpose: To construct a predictive model for personalized recommendation of peripheral defocus spectacle designs based on ocular biometric parameters. Methods: 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. Results: 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 (R Conclusion: 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.
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