PDF(Pubmed)
Abstract：
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.
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.
摘要：
■这项研究的目的是调查远视儿童的光学生物识别组件的发展，并应用机器学习模型来预测轴向长度。
■远视儿童（+1屈光度[D]至+10D）3个年龄组：3至5岁（n=74），6至8年（n=102），包括9至11年（n=36）。轴向长度,前房深度,透镜厚度,中央角膜厚度,并测量角膜屈光力;所有参与者在6个月内都有睫状肌麻痹屈光.计算球形当量(SEQ)。使用混合效应模型比较性别和年龄组，并调整眼间相关性。使用分类和回归树（CART）分析来预测轴向长度，并与线性回归进行比较。
所有3个年龄组的平均SEQ相似,但9至11岁组的远视比3至5岁组少0.49D(P<0.001)。除了角膜厚度，所有其他眼部成分均具有显着性别差异（P<0.05）。与老年组相比，3至5年组的眼轴长度和前房深度明显较短，角膜屈光力较高（P<0.001）。使用SEQ,年龄,和性,轴向长度可以用CART模型预测，结果平均绝对误差为0.60，低于线性回归模型(0.76)。
■尽管屈光不正的值相似，远视儿童的眼部生物特征参数随年龄而变化，因此，眼轴长度的增长被角膜屈光力的降低所抵消。
■我们为远视儿童的光学元件提供参考，和基于SEQ的方便轴向长度估计的机器学习模型，年龄,和性爱。
■远视儿童（+1屈光度[D]至+10D）3个年龄组：3至5岁（n=74），6至8年（n=102），包括9至11年（n=36）。轴向长度,前房深度,透镜厚度,中央角膜厚度,并测量角膜屈光力;所有参与者在6个月内都有睫状肌麻痹屈光.计算球形当量(SEQ)。使用混合效应模型比较性别和年龄组，并调整眼间相关性。使用分类和回归树（CART）分析来预测轴向长度，并与线性回归进行比较。
所有3个年龄组的平均SEQ相似,但9至11岁组的远视比3至5岁组少0.49D(P<0.001)。除了角膜厚度，所有其他眼部成分均具有显着性别差异（P<0.05）。与老年组相比，3至5年组的眼轴长度和前房深度明显较短，角膜屈光力较高（P<0.001）。使用SEQ,年龄,和性,轴向长度可以用CART模型预测，结果平均绝对误差为0.60，低于线性回归模型(0.76)。
■尽管屈光不正的值相似，远视儿童的眼部生物特征参数随年龄而变化，因此，眼轴长度的增长被角膜屈光力的降低所抵消。
■我们为远视儿童的光学元件提供参考，和基于SEQ的方便轴向长度估计的机器学习模型，年龄,和性爱。
