Literature DB >> 27927474

Correlation Between a Novel Surface Topography Asymmetry Analysis and Radiographic Data in Scoliosis.

Amin Komeili1, Lindsey Westover2, Eric C Parent3, Marwan El-Rich1, Samer Adeeb1.   

Abstract

STUDY
DESIGN: Cross-sectional study.
OBJECTIVE: To investigate the correlation between parameters extracted from a three-dimensional (3D) asymmetry analysis of the torso and the internal deformities of the spine presented on radiographs, including 1) curve number, direction and location; 2) location of the apical vertebra; and 3) curve severity. SUMMARY OF BACKGROUND DATA: Surface topography (ST) is used to assess external torso deformities and may predict important characteristics of the underlying spinal curves. ST does not expose patients to radiation and could safely be used clinically for scoliosis patients. Most ST indices rely on anatomical landmarks on the torso and 2D measurements.
METHODS: The ability of a 3D markerless asymmetry technique to predict radiographic characteristics was assessed for 100 scoliosis patients with full torso ST scans. Twenty-four additional patients were used for validation. The number, direction, and location of curves were determined by three examiners using ST deviation color maps. The inter-method percentage of agreement and Kappa coefficient were estimated for each measure. Linear regression predicted the vertical location of the apical vertebra from ST. Curve severity (mild, moderate, severe) was predicted with a decision tree analysis using ST parameters.
RESULTS: The average percentage of agreement was 62%, 66%, and 23% for single, double, and triple curves, respectively. Curve direction was always correctly identified. The average percentages of agreement for curve location were 63%, 92%, and 62% for proximal thoracic, thoracic/thoracolumbar (T-TL), and lumbar (L) curves, respectively. Apical vertebra location was predicted with R2 = 0.89 for T-TL and R2 = 0.58 for L curves. ST parameters classified curve severity for T-TL and L curves with 73% and 59% accuracy, respectively.
CONCLUSIONS: The method presented here improves upon current ST techniques by using the entire torso surface and both a visual and quantitative representation of the asymmetry to better capture the torso deformity.
Copyright © 2015 Scoliosis Research Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adolescent idiopathic; Asymmetry analysis; Radiography; Scoliosis; Surface topography

Year:  2015        PMID: 27927474     DOI: 10.1016/j.jspd.2015.02.002

Source DB:  PubMed          Journal:  Spine Deform        ISSN: 2212-134X


  6 in total

1.  Assessment of the reliability of hand-held surface scanner in the evaluation of adolescent idiopathic scoliosis.

Authors:  Yılmaz Yıldırım; Kadriye Tombak; Sezen Karaşin; İnci Yüksel; Ahmet Hakan Nur; Umut Ozsoy
Journal:  Eur Spine J       Date:  2021-02-24       Impact factor: 3.134

2.  Automated noninvasive detection of idiopathic scoliosis in children and adolescents: A principle validation study.

Authors:  Hideki Sudo; Terufumi Kokabu; Yuichiro Abe; Akira Iwata; Katsuhisa Yamada; Yoichi M Ito; Norimasa Iwasaki; Satoshi Kanai
Journal:  Sci Rep       Date:  2018-12-07       Impact factor: 4.379

Review 3.  A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

Authors:  Ilona Karpiel; Adam Ziębiński; Marek Kluszczyński; Daniel Feige
Journal:  Sensors (Basel)       Date:  2021-12-16       Impact factor: 3.576

4.  3D Markerless asymmetry analysis in the management of adolescent idiopathic scoliosis.

Authors:  Maliheh Ghaneei; Amin Komeili; Yong Li; Eric C Parent; Samer Adeeb
Journal:  BMC Musculoskelet Disord       Date:  2018-10-24       Impact factor: 2.362

5.  Decreased Vertical Trunk Inclination Angle and Pelvic Inclination as the Result of Mid-High-Heeled Footwear on Static Posture Parameters in Asymptomatic Young Adult Women.

Authors:  Jakub Michoński; Marcin Witkowski; Bożena Glinkowska; Robert Sitnik; Wojciech Glinkowski
Journal:  Int J Environ Res Public Health       Date:  2019-11-18       Impact factor: 3.390

6.  Development and validation of deep learning algorithms for scoliosis screening using back images.

Authors:  Junlin Yang; Kai Zhang; Hengwei Fan; Zifang Huang; Yifan Xiang; Jingfan Yang; Lin He; Lei Zhang; Yahan Yang; Ruiyang Li; Yi Zhu; Chuan Chen; Fan Liu; Haoqing Yang; Yaolong Deng; Weiqing Tan; Nali Deng; Xuexiang Yu; Xiaoling Xuan; Xiaofeng Xie; Xiyang Liu; Haotian Lin
Journal:  Commun Biol       Date:  2019-10-25
  6 in total

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