Literature DB >> 28874278

Characterization of the age-dependent shape of the pediatric thoracic spine and vertebrae using generalized procrustes analysis.

James R Peters1, Robert M Campbell2, Sriram Balasubramanian3.   

Abstract

Generalized Procrustes Analysis (GPA) is a superimposition method used to generate size-invariant distributions of homologous landmark points. Several studies have used GPA to assess the three-dimensional (3D) shapes of or to evaluate sex-related differences in the human brain, skull, rib cage, pelvis and lower limbs. Previous studies of the pediatric thoracic vertebrae suggest that they may undergo changes in shape asa result of normative growth. This study uses GPA and second order polynomial equations to model growth and age- and sex-related changes in shape of the pediatric thoracic spine. We present a thorough analysis of the normative 3D shape, size, and orientation of the pediatric thoracic spine and vertebrae as well as equations which can be used to generate models of the thoracic spine and vertebrae for any age between 1 and 19years. Such models could be used to create more accurate 3D reconstructions of the thoracic spine, generate improved age-specific geometries for finite element models (FEMs) and used to assist clinicians with patient-specific planning and surgical interventions for spine deformity.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Generalized procrustes analysis; Growth; Model; Thoracic spine; Vertebrae

Mesh:

Year:  2017        PMID: 28874278     DOI: 10.1016/j.jbiomech.2017.07.030

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  2 in total

1.  Thoracic vertebral morphology in normal and scoliosis deformity in skeletally immature rabbits: A Longitudinal study.

Authors:  Ausilah Alfraihat; John Casey Olson; Brian D Snyder; Patrick J Cahill; Sriram Balasubramanian
Journal:  JOR Spine       Date:  2020-09-17

2.  Predicting curve progression for adolescent idiopathic scoliosis using random forest model.

Authors:  Ausilah Alfraihat; Amer F Samdani; Sriram Balasubramanian
Journal:  PLoS One       Date:  2022-08-11       Impact factor: 3.752

  2 in total

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