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Binocular Non-Cycloplegic Ocular Parameters for Modeling Cycloplegic SER in Children
Ranran Chen1, Juan Zhang2, Jinming Lei3
1Department of Ophthalmology, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Insights
This study developed an interpretable binocular model to accurately predict children's refractive error, improving vision screening tools. The model accounts for inter-eye correlation, enhancing prediction accuracy for pediatric myopia management.
Area of Science:
- Ophthalmology
- Biomedical Engineering
- Data Science
Background:
- Accurate refractive error prediction in children is crucial for early detection and management of vision disorders like myopia.
- Existing methods may not fully account for the correlation between the two eyes, potentially limiting prediction accuracy.
Purpose of the Study:
- To evaluate an interpretable binocular modeling approach for predicting cycloplegic spherical equivalent refraction (SER) in children.
- To assess the model's performance while considering inter-eye correlation.
Main Methods:
- Collected clinical data from children aged 4-17 years across two centers.
- Developed a binocular approach incorporating inter-eye features and applied a linear mixed-effects model (LMM).
- Utilized SHapley Additive exPlanations (SHAP) for feature interpretability and validated models internally and externally.
Main Results:
- Optimal binocular models achieved high R² values (up to 0.969) and low Mean Absolute Error (MAE) (as low as 0.232 D) across age groups in internal validation.
- External validation showed comparable performance with R² up to 0.969 and MAE as low as 0.287 D.
- SHAP analysis identified key predictors varying by age, including axial length and non-cycloplegic SER.
Conclusions:
- The interpretable binocular modeling approach shows promise for enhancing refractive status prediction accuracy in children.
- This method can serve as a valuable auxiliary tool for large-scale vision screening programs.
- The study translates ocular biometric modeling into clinical practice for pediatric myopia risk assessment and screening.
Purpose:
The purpose of this study was to evaluate an interpretable binocular modeling approach for predicting cycloplegic spherical equivalent refraction (SER) in children while accounting for inter-eye correlation.
Methods:
Clinical data from children aged 4 to 17 years were collected at 2 centers. Participants were stratified into three age groups. The binocular approach constructed inter-eye features, including means, differences, and error-related metrics. A linear mixed-effects model (LMM) with subject-specific random intercepts was applied. Model performance was evaluated using R², mean absolute error (MAE), mean square error (MSE), and root mean square error RMSE. SHapley Additive exPlanations (SHAP) were used to quantify feature contributions.
Results:
In internal validation, the optimal binocular models achieved R² values of 0.882, 0.957, and 0.969, with MAE values of 0.399 diopter (D), 0.285 D, and 0.232 D in the 4 to 5, 6 to 12, and 13 to 17 years age groups, respectively. In external validation, the best binocular models achieved R² values of 0.834, 0.947, and 0.969, with MAE values of 0.488 D, 0.287 D, and 0.354 D in the 3 age groups. SHAP analysis revealed axial length/corneal curvature radius (AL/CR) and AL predominated in children aged 4 to 5 years; non-cycloplegic SER was the determinant in those aged 6 to 12 years; non-cycloplegic SER and its mean became dominant in adolescents aged 13 to 17 years.
Conclusions:
The binocular modeling approach shows potential for improving accuracy of refractive status prediction in children, and the integration of interpretability methods further supports its utility as an auxiliary tool in large-scale vision screening programs.
Translational Relevance:
This study translates ocular biometric modeling into clinical practice by providing an interpretable binocular approach to support risk assessment and preliminary screening in pediatric myopia management.