Craniofacial growth, modeling, and estimation of milestones.

IF 2.1
Richard J Sherwood, Dana L Duren, Christina L Holzhauser, Hee Soo Oh, Manish Valiathan, Kieran P McNulty, Kevin M Middleton
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Abstract

Understanding craniofacial growth is foundational for research into intra- and interspecies variation, evolution, and clinical care. The Craniofacial Growth Consortium Study (CGCS), combines cephalographs from historical growth studies to create a dense longitudinal record of growth from 6 to 22 years of age. Originally comprised of six historical studies, two new collections (Burlington Growth study and the ADA Forsyth Study) are described and added to the CGCS increasing the sample to 2,100 individuals (1,056 males, 1,044 females; median of 9 cephalographs/individual). Coordinate data for 16 cephalometric landmarks, and 48 semilandmarks, were collected from each cephalograph. Cephalometric points were collected independently by three assessors. Previously established error envelopes for each landmark are used to compare the three assessments. If two or more coordinates for a landmark grossly differ, the assessors have the option to retrace; if acceptable, they are averaged for each point. A total of 32 measures were calculated from the coordinates. A multilevel double logistic equation was implemented to estimate growth trajectories fitting each trait separately by sex. The models successfully identified growth milestones (e.g., age at peak growth velocity) for all but a single trait (N-ANS in females). Results demonstrate similarity among milestone estimates and the overall shape of the growth curve for most traits describing craniofacial. A subset of measures of small magnitude had early estimates of age at peak growth velocity and a small peak growth velocity. The six neurocranial traits were characterized as an asymptotic curve with difficulties in milestone estimation.

颅面生长,建模和里程碑的估计。
了解颅面生长是研究种内和种间变异、进化和临床护理的基础。颅面生长联合研究(CGCS)结合了来自历史生长研究的脑电图,创建了从6岁到22岁的密集纵向生长记录。最初由六个历史研究组成,两个新的收集(伯灵顿生长研究和ADA福赛斯研究)被描述并添加到CGCS中,将样本增加到2100个个体(1,056个男性,1,044个女性;中位数为9个脑电图仪/个体)。从每台脑电图仪收集16个头测标志和48个半标志的坐标数据。头测点由三名评估员独立收集。以前建立的误差包络用于每个里程碑比较三个评估。如果一个地标的两个或多个坐标严重不同,评估员可以选择重描;如果可以接受,则对每个点取平均值。根据座标一共计算出32个度量。采用多水平双logistic方程,按性别分别拟合各性状的生长轨迹。这些模型成功地确定了除单一性状(雌性N-ANS)外的所有性状的生长里程碑(例如,生长速度峰值时的年龄)。结果表明,里程碑估计和大多数描述颅面特征的生长曲线的整体形状之间存在相似性。小量级测量的子集在峰值生长速度下有早期的年龄估计和较小的峰值生长速度。六个神经颅骨特征的特征是一个渐进曲线,在里程碑估计困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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