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Data Mining in Medicine: Relationship of Scoliotic Spine Curvature to the Movement Sequence of Lateral Bending Positions
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  • 关键词:Adolescent idiopathic scoliosis ; Lateral bending positions ; Multi ; body kinematic model ; Spine curvature ; Spine movement sequence
  • 刊名:Lecture Notes in Computer Science
  • 出版年:2016
  • 出版时间:2016
  • 年:2016
  • 卷:9728
  • 期:1
  • 页码:29-40
  • 全文大小:2,268 KB
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  • 作者单位:Athena Jalalian (14)
    Francis E. H. Tay (14)
    Gabriel Liu (15)

    14. Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore
    15. Department of Orthopedic Surgery, National University of Singapore, Singapore, Singapore
  • 丛书名:Advances in Data Mining. Applications and Theoretical Aspects
  • ISBN:978-3-319-41561-1
  • 刊物类别:Computer Science
  • 刊物主题:Artificial Intelligence and Robotics
    Computer Communication Networks
    Software Engineering
    Data Encryption
    Database Management
    Computation by Abstract Devices
    Algorithm Analysis and Problem Complexity
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1611-3349
  • 卷排序:9728
文摘
We aim to determine relationships between scoliotic spine curvatures in movement sequence from left bending to erect to right bending positions in the frontal plane. A multi-body kinematic modelling approach is utilized to reconstruct the curvatures and study the relationships. The spine is considered as a chain of micro-scale motion-segments (MMSs). Linear regression method is adopted to identify relationships between angles of MMSs in erect and lateral bending positions. Excellent linear relationships (R2 = 0.93 ± 0.09) were identified between angles of MMSs placed between each two successive vertebrae. We showed that these relationships give good estimates of the curvatures (Root-mean-square-error = 0.0172 ± 0.0114 mm) and the key parameters for scoliosis surgery planning; estimation errors for Cobb angle, spinal mobility, and flexibility were 0.0016 ± 0.0122°, 0.0010 ± 0.086°, and 0.0002 ± 0.0002 respectively. This paper provides an important insight: scoliotic spine curvatures in lateral bending positions and the key parameters for surgery planning can be predicted using spine curvature in erect position.

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