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Prediction of spherical equivalent refraction and axial length in children based on machine learning
Shaojun Zhu1,2, Haodong Zhan1, Zhipeng Yan3
1School of Information Engineering, Huzhou University, Huzhou, China.
Insights
Machine learning models accurately predict changes in children's spherical equivalent refraction (SER) and axial length (AL). Orthogonal Matching Pursuit (OMP) excelled in SER prediction, while Kernel Ridge (KR) and Multilayer Perceptron (MLP) were superior for AL.
Area of Science:
- Ophthalmology
- Computer Science
- Data Science
Background:
- High myopia is increasing in younger populations.
- Accurate prediction of myopia progression is crucial for early intervention.
Purpose of the Study:
- To predict changes in spherical equivalent refraction (SER) and axial length (AL) in children using machine learning.
- To evaluate the performance of different machine learning models for myopia progression prediction.
Main Methods:
- Retrospective study using data from 179 childhood myopia examinations (grades 1-6).
- Employed six machine learning models to predict SER and AL.
- Utilized six evaluation indicators to assess model performance.
Main Results:
- Orthogonal Matching Pursuit (OMP) demonstrated superior performance in predicting SER across multiple grade levels.
- Kernel Ridge (KR) and Multilayer Perceptron (MLP) algorithms showed the best results for predicting axial length (AL).
- High R-squared values indicate strong predictive accuracy for the selected models.
Conclusions:
- Machine learning models, particularly OMP for SER and KR/MLP for AL, can effectively predict myopia progression in children.
- These findings support the use of AI in managing and potentially mitigating childhood myopia.
Purpose:
Recently, the proportion of patients with high myopia has shown a continuous growing trend, more toward the younger age groups. This study aimed to predict the changes in spherical equivalent refraction (SER) and axial length (AL) in children using machine learning methods.
Methods:
This study is a retrospective study. The cooperative ophthalmology hospital of this study collected data on 179 sets of childhood myopia examinations. The data collected included AL and SER from grades 1 to 6. This study used the six machine learning models to predict AL and SER based on the data. Six evaluation indicators were used to evaluate the prediction results of the models.
Results:
For predicting SER in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the multilayer perceptron (MLP) algorithm, MLP algorithm, orthogonal matching pursuit (OMP) algorithm, OMP algorithm, and OMP algorithm, respectively. The R2 of the five models were 0.8997, 0.7839, 0.7177, 0.5118, and 0.1758, respectively. For predicting AL in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the Extra Tree (ET) algorithm, MLP algorithm, kernel ridge (KR) algorithm, KR algorithm, and MLP algorithm, respectively. The R2 of the five models were 0.7546, 0.5456, 0.8755, 0.9072, and 0.8534, respectively.
Conclusion:
Therefore, in predicting SER, the OMP model performed better than the other models in most experiments. In predicting AL, the KR and MLP models were better than the other models in most experiments.
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