Eye (London, England) · 2026
Development of a deep learning model to predict cycloplegic myopia and pre- myopia: based on a large-scale screening of school-aged children.
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Wang Zixun, Song Desheng, Li Yifan, Yu Jingtao, Rong Hua, Hu Xiaoxue, Sun Boxuan, Du Bei, Wei Ruihua
Аннотация
PURPOSE: This study aims to develop and validate practical deep learning (DL) and machine learning (ML) models to predict cycloplegic myopia, pre-myopia, and non-myopia in school-aged children using non-cycloplegic parameters, thereby facilitating early detection and intervention.
METHODS: A cross-sectional study enrolled 3,077 children aged 6-10 years from Tianjin, China. Ocular parameters, including non-cycloplegic dioptre sphere (DS), axial length (AL), corneal curvature (K1, K2), and sex, were incorporated into four single multi-class classification models: CatBoost, XGBoost, Random Forest (RF), and TabPFN (a tabular DL model). Performance was evaluated using AUC, accuracy, sensitivity, specificity, and SHAP analysis for interpretability.
RESULTS: The TabPFN model demonstrated slightly higher overall performance (AUC = 0.89), with AUCs of 0.93 for myopia and 0.78 for pre-myopia prediction. AL emerged as the most influential feature, while K2 contributed to model predictions alongside other biometric parameters. Decision curve analysis demonstrated clinical utility for combined pre-myopia+myopia classification at intermediate thresholds.
CONCLUSION: The TabPFN model outperformed traditional ML models in accurately classifying myopia and pre-myopia using non-cycloplegic parameters, offering a practical tool for large-scale school screenings. AL was the dominant contributor to model predictions, while K2 provided complementary information within the multivariable framework.
METHODS: A cross-sectional study enrolled 3,077 children aged 6-10 years from Tianjin, China. Ocular parameters, including non-cycloplegic dioptre sphere (DS), axial length (AL), corneal curvature (K1, K2), and sex, were incorporated into four single multi-class classification models: CatBoost, XGBoost, Random Forest (RF), and TabPFN (a tabular DL model). Performance was evaluated using AUC, accuracy, sensitivity, specificity, and SHAP analysis for interpretability.
RESULTS: The TabPFN model demonstrated slightly higher overall performance (AUC = 0.89), with AUCs of 0.93 for myopia and 0.78 for pre-myopia prediction. AL emerged as the most influential feature, while K2 contributed to model predictions alongside other biometric parameters. Decision curve analysis demonstrated clinical utility for combined pre-myopia+myopia classification at intermediate thresholds.
CONCLUSION: The TabPFN model outperformed traditional ML models in accurately classifying myopia and pre-myopia using non-cycloplegic parameters, offering a practical tool for large-scale school screenings. AL was the dominant contributor to model predictions, while K2 provided complementary information within the multivariable framework.