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

objectiveTo develop a predictive model based on ocular biometric parameters and deep learning for studying the progression of refractive errors in children and adolescents.

methodsThis 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.

resultsBaseline 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.

conclusionThe deep learning model developed in this study, based on ocular biometric parameters, demonstrated high accuracy and stability in refractive error prediction.