%0 Journal Article %T Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length. %A Wang J %A Jost RM %A Birch EE %J Transl Vis Sci Technol %V 13 %N 5 %D 2024 May 1 %M 38809529 %F 3.048 %R 10.1167/tvst.13.5.25 %X UNASSIGNED: 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.
UNASSIGNED: Children with hyperopia (+1 diopters [D] to +10 D) in 3 age groups: 3 to 5 years (n = 74), 6 to 8 years (n = 102), and 9 to 11 years (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.
UNASSIGNED: Mean SEQ for all 3 age groups were similar but the 9 to 11 year old group had 0.49 D less hyperopia than the 3 to 5 year 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 5 year 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).
UNASSIGNED: 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.
UNASSIGNED: 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.