de Vries Lily E, van Loon Derek F R, van Es Eline M, Veeger DirkJan H E J, Colaris Joost W
Erasmus MC, University Medical Center Rotterdam, Department of Orthopedics and Sports Medicine, 3000 CA Rotterdam, the Netherlands.
Educational program Technical Medicine, Leiden University Medical Center, Delft University of Technology, Erasmus University Medical Center Rotterdam, the Netherlands.
Bone Rep. 2024 Nov 22;24:101817. doi: 10.1016/j.bonr.2024.101817. eCollection 2025 Mar.
Analyzing population trends of bone shape variation can provide valuable insights into growth processes. This review aims to overview state-of-the-art spatiotemporal statistical shape modeling techniques, emphasizing their application to 3D skeletal structures during healthy growth.
We searched PubMed and Scopus for articles on statistical shape modeling using a pediatric spatiotemporal dataset of 3D healthy bone models. Dataset characteristics and details on the shape models' development, analyses, and potential clinical use were extracted.
Fourteen studies were found eligible, modeling one or multiple lower limb bones, the mandible, the skull, and vertebrae. The majority applied Principal Component Analysis on point distribution models to create a statistical shape model. Shape variation was analyzed based on shape modes, representing a specific shape change as a part of the overall variance. Unscaled models resulted in a more compact statistical shape model than scaled models. The latter represented more subtle shape variations due to the absence of size differences between the bone models. Four studies reported a significant correlation between the first shape mode and age, indicating a relationship between that type of shape variation and growth. Three studies reconstructed 3D models using prediction features of statistical shape modeling. Measuring difference between predicted and actual anatomy resulted in Root Mean Squared Errors below 3 mm.
Spatiotemporal statistical shape modeling provides insight into modes of shape variation during growth. Such a model can be used to find predictive factors, like age or sex, and deploy these characteristics to predict someone's bone geometry.
分析骨形状变异的人群趋势可为生长过程提供有价值的见解。本综述旨在概述最先进的时空统计形状建模技术，重点介绍其在健康生长过程中对三维骨骼结构的应用。
我们在PubMed和Scopus上搜索了使用三维健康骨模型的儿科时空数据集进行统计形状建模的文章。提取了数据集特征以及形状模型的开发、分析和潜在临床应用的详细信息。
发现14项研究符合条件，对一个或多个下肢骨、下颌骨、颅骨和椎骨进行建模。大多数研究在点分布模型上应用主成分分析来创建统计形状模型。基于形状模式分析形状变异，将特定的形状变化表示为总体变异的一部分。未缩放的模型比缩放的模型产生更紧凑的统计形状模型。由于骨模型之间不存在大小差异，后者表示更细微的形状变异。四项研究报告了第一形状模式与年龄之间存在显著相关性，表明这种形状变异类型与生长之间存在关系。三项研究使用统计形状建模的预测特征重建了三维模型。测量预测解剖结构与实际解剖结构之间的差异，均方根误差低于3毫米。
时空统计形状建模提供了对生长过程中形状变异模式的见解。这样的模型可用于找到预测因素，如年龄或性别，并利用这些特征来预测某人的骨骼几何形状。
