Artificial intelligence for predicting the axial length response of orthokeratology in myopic children

Objective.This study aimed to automate the extraction of local corneal topography (CT) features in myopic children undergoing orthokeratology (OK), evaluate their causal effects on axial length (AL) control, and develop a predictive model for AL progression.Approach.We retrospectively analyzed myopic children who had received OK treatment for more than 12 months. Advanced digital image processing techniques were employed to automatically quantify three critical CT parameters: treatment zone area (TZA), eccentric distance (ED), and eccentric angle (EA). Counterfactual inference quantified causal relationships between these parameters and AL changes. Baseline characteristics and one-month CT features were used to train a CatBoost prediction model.Main results.This study included 143 myopic subjects (276 eyes) treated with OK lenses. The image processing algorithm performed comparably to manual annotation, with mean absolute percentage errors of 2.1% (TZA), 1.2% (ED), and 0.7% (EA). Per unit increase, TZA, ED, and EA were associated with AL changes of 0.054 mm, -0.161 mm, and -0.0003 mm, respectively. The CatBoost model, using initial AL and age, predicted six-month and one-year AL with absolute errors of 0.180 and 0.169 mm.Significance.This work establishes an integrated artificial intelligence (AI) framework that combines automated CT analysis, causal inference, and predictive modeling. It provides clinicians with an interpretable tool for assessing OK efficacy and forecasting myopia progression, while paving the way for next-generation healthcare AI systems with integrated perceptual, explanatory, and prognostic capabilities.