Exploring shape changes in healthy bone growth through 3D spatiotemporal statistical shape models: A scoping review
Bone Reports, vol. 24, pp. 101817
Abstract
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. • Spatiotemporal statistical shape modeling can capture and visualize shape changes in bone anatomy during healthy growth. • Principal Component Analysis and Partial Least Squares Regression are common techniques for statistical shape modeling. • Statistical shape modeling enables the prediction of skeletal geometry based on personal characteristics. • Spatiotemporal statistical shape modeling could be valuable for diagnosing and treating orthopedic deformities.
Authors 5
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Leiden University Medical Center · Erasmus MC · Delft University of Technology
Affiliation as printed
Educational program Technical Medicine, Leiden University Medical Center, Delft University of Technology, Erasmus University Medical Center Rotterdam, the Netherlands
Erasmus MC, University Medical Center Rotterdam, Department of Orthopedics and Sports Medicine, 3000 CA Rotterdam, the Netherlands
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Derek F.R. van Loon corresponding
Affiliation as printed
Erasmus MC, University Medical Center Rotterdam, Department of Orthopedics and Sports Medicine, 3000 CA Rotterdam, the Netherlands
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Affiliation as printed
Erasmus MC, University Medical Center Rotterdam, Department of Orthopedics and Sports Medicine, 3000 CA Rotterdam, the Netherlands
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Delft University of Technology
Affiliation as printed
Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, the Netherlands
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Affiliation as printed
Erasmus MC, University Medical Center Rotterdam, Department of Orthopedics and Sports Medicine, 3000 CA Rotterdam, the Netherlands
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