Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

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The purpose of this study was to investigate the development of optical biometric components in children with hyperopia, and apply a machine-learning model to predict axial length. Children with hyperopia (+1 diopters [D] to +10 D) in 3 age groups: 3 to 5years (n=74), 6 to 8years (n=102), and 9 to 11years (n=36) were included. Axial length, anterior chamber depth, lens thickness, central corneal thickness, and corneal power were measured; all participants had cycloplegic refraction within 6 months. Spherical equivalent (SEQ) was calculated. A mixed-effects model was used to compare sex and age groups and adjust for interocular correlation. A classification and regression tree (CART) analysis was used to predict axial length and compared with the linear regression. Mean SEQ for all 3 age groups were similar but the 9 to 11year old group had 0.49D less hyperopia than the 3 to 5year old group (P < 0.001). With the exception of corneal thickness, all other ocular components had a significant sex difference (P < 0.05). The 3 to 5year group had significantly shorter axial length and anterior chamber depth and higher corneal power than older groups (P < 0.001). Using SEQ, age, and sex, axial length can be predicted with a CART model, resulting in lower mean absolute error of 0.60 than the linear regression model (0.76). Despite similar values of refractive errors, ocular biometric parameters changed with age in hyperopic children, whereby axial length growth is offset by reductions in corneal power. We provide references for optical components in children with hyperopia, and a machine-learning model for convenient axial length estimation based on SEQ, age, and sex.