A refractive error prediction model for children and adolescents based on ocular biometric parameters

OBJECTIVE: To develop a predictive model based on ocular biometric parameters and deep learning for studying the progression of refractive errors in children and adolescents. METHODS: This longitudinal observational cohort study included 559 children and adolescents (1,118eyes; 252males, 307females) aged 5–18 years, enrolled at Shanxi provincial Eye Hospital (Taiyuan, China) between 2019 and 2023. Refractive error and ocular biometric parameters were prospectively assessed through serial non-cycloplegic automated refraction and LENSAR 900 biometry, with participants undergoing 2–5 follow-up evaluations at irregular intervals. After data cleaning, preprocessing, and augmentation through truncation techniques, Long Short-Term Memory (LSTM) and Time-aware LSTM (T-LSTM) models were used to predict refractive changes over the next three years. Mean Absolute Error (MAE) and Standard Deviation (SD) were used to evaluate model performance, reported as MAE ± SD. RESULTS: Baseline MAEs measured 0.40 ± 0.39D (sphere) and 0.30 ± 0.46D (cylinder), exhibiting temporal deterioration over 12 quarters (sphere: 0.35 ± 0.42D→0.52 ± 0.37D; cylinder: 0.20 ± 0.21D→0.33 ± 0.30D).Additional biometric measurements reduced errors across cohorts: with five measurements ≤ 6y achieved 0.11 ± 0.10D (sphere)/0.09 ± 0.10D (cylinder); 10-12y />12y attained 0.18 ± 0.13D/0.10 ± 0.08D. The steepest error reduction occurred in older cohorts (10-12y/>12y), suggesting that fewer measurements achieve lower prediction errors with increasing age. CONCLUSION: The deep learning model developed in this study, based on ocular biometric parameters, demonstrated high accuracy and stability in refractive error prediction.