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Predictive Modeling of Cycloplegic Refraction Using Non-Cycloplegia Ocular Parameters With Emphasis on Lens-Related
Qiang Su1,2, Bei Du1, Bingqin Li1
1Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
Insights
This study developed a predictive model for refraction after cycloplegia using non-cycloplegia ocular and lens-related features. The model incorporating lens features significantly improved refractive prediction accuracy in children.
Area of Science:
- Ophthalmology
- Optometry
- Biomedical Engineering
Background:
- Accurate prediction of refractive error after cycloplegia is crucial for pediatric eye care.
- Cycloplegia, while effective, can be associated with complications and discomfort.
- Leveraging non-cycloplegia parameters offers a potential alternative for refractive assessment.
Purpose of the Study:
- To develop and evaluate a predictive model for post-cycloplegia refraction.
- To investigate the impact of lens-related ocular parameters on refractive prediction.
- To compare models with and without lens-related features for improved accuracy.
Main Methods:
- Developed four models for spherical refraction prediction in 153 children (4-15 years old).
- Utilized non-cycloplegia ocular parameters including intraocular pressure and optical biometry.
- Incorporated lens-related features (e.g., lens diopter, anterior curvature radius, thickness) and employed XGBoost with LASSO regression.
Main Results:
- The predictive model incorporating lens-related features significantly outperformed the control model.
- The IOL of contact lens algorithm (IOLcl) model achieved the highest accuracy (r2=0.964, MSE=0.241, RMSE=0.472, MAE=0.307).
- Lens-related features were identified as significant predictors of refraction.
Conclusions:
- Lens-related features are critical for developing robust predictive models of post-cycloplegia refraction.
- The developed model offers a promising, potentially less invasive method for refractive assessment.
- This approach may enhance clinical vision screening and reduce cycloplegia-associated complications.
Purpose:
The study aimed to develop a predictive model for refraction after cycloplegia by leveraging non-cycloplegia ocular parameters and focusing on lens-related features.
Methods:
A total of 153 children 4 to 15 years old were enrolled in this study. This study randomized gender distribution. Sex, age, intraocular pressure (IOP), refraction before and after cycloplegia, and optical biometry (OB) parameters were collected. Four prediction models for spherical refraction were developed: a control group without lens-related features and three experimental groups incorporating lens-related features. Features such as lens diopter, anterior surface curvature radius, and lens thickness played significant roles. The models were evaluated using statistical measures: mean square error (MSE), Root mean square error (RSME), Mean absolute error (MAE) and r-square (r2). Least absolute shrinkage and selection operator (LASSO) regression and the L1 regularization term were used for feature screening and machine learning for extreme gradient enhancement. The extreme gradient boosting (XGBoost) method was used to develop the model.
Results:
The predictive model incorporating lens-related features demonstrated superior performance in estimating refraction after cycloplegia compared to the model without such features. Among the models with lens-related features, the IOL of contact lens algorithm (IOLcl) group exhibited the highest efficacy, boasting an r2 of 0.964, MSE of 0.241, RMSE of 0.472, and MAE of 0.307.
Conclusions:
The study provided valuable insights into developing a robust predictive model for refraction after cycloplegia, emphasizing the importance of lens-related features and the morphological changes in the crystalline lens during accommodation.
Translational Relevance:
This predictive model has potential advantages in avoiding complications associated with cycloplegia and can be widely applied for clinic vision screening in optometry.
