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已发表论文
基于眼部生物测量参数的周边离焦眼镜个性化配适预测模型
Authors Zhou P , Li Y, Chen S, Wang K, Li Y
Received 25 October 2025
Accepted for publication 13 January 2026
Published 19 January 2026 Volume 2026:18 576749
DOI https://doi.org/10.2147/OPTO.S576749
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Chris Lievens
Peng Zhou,1,2,* Yingli Li,1,3– 6,* Sitong Chen,1,3– 6 Kai Wang,1,3– 6 Yan Li1,3– 6
1Optometry Center, Peking University People’s Hospital, Beijing, 100034, People’s Republic of China; 2Department of Ophthalmology, Parkway Gleneagles Medical and Surgical Center, Shanghai, 200003, People’s Republic of China; 3Department of Ophthalmology, Peking University People’s Hospital, Beijing, 100034, People’s Republic of China; 4Beijing Key Laboratory of Diagnosis and Therapy of Ocular Diseases and Optometry, Beijing, 100034, People’s Republic of China; 5School of Optometry and Ophthalmology, Peking University Health Science Center, Beijing, 100034, People’s Republic of China; 6Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yan Li, Department of ophthalmology, Peking University People’s Hospital, 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China, Email eyedrliyan@outlook.com Kai Wang, Department of ophthalmology, Peking University People’s Hospital, 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China, Email wang_kai@bjmu.edu.cn
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 (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.
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.
Plain Language Summary: Defocus glasses have been proven to slow down the progression of myopia, but there is currently no method for selecting the type of defocus lenses on an individual basis. This study proposes a model for personalized selection of defocus lenses based on ocular biometric data. This model will offer guidance in clinical decisions for myopia management strategies.
Keywords: myopia control, defocus spectacle lenses, personalized selection, ocular biometry, predictive model
1Optometry Center, Peking University People’s Hospital, Beijing, 100034, People’s Republic of China; 2Department of Ophthalmology, Parkway Gleneagles Medical and Surgical Center, Shanghai, 200003, People’s Republic of China; 3Department of Ophthalmology, Peking University People’s Hospital, Beijing, 100034, People’s Republic of China; 4Beijing Key Laboratory of Diagnosis and Therapy of Ocular Diseases and Optometry, Beijing, 100034, People’s Republic of China; 5School of Optometry and Ophthalmology, Peking University Health Science Center, Beijing, 100034, People’s Republic of China; 6Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yan Li, Department of ophthalmology, Peking University People’s Hospital, 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China, Email eyedrliyan@outlook.com Kai Wang, Department of ophthalmology, Peking University People’s Hospital, 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China, Email wang_kai@bjmu.edu.cn
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 (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.
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.
Plain Language Summary: Defocus glasses have been proven to slow down the progression of myopia, but there is currently no method for selecting the type of defocus lenses on an individual basis. This study proposes a model for personalized selection of defocus lenses based on ocular biometric data. This model will offer guidance in clinical decisions for myopia management strategies.
Keywords: myopia control, defocus spectacle lenses, personalized selection, ocular biometry, predictive model