Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions

Athena Jalalian; Francis E.H. Tay; Gabriel Liu

doi:10.1007/978-3-319-41561-1_3

Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions

Athena Jalalian, Francis E.H. Tay^*, Gabriel Liu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Advances in Data Mining: Applications and Theoretical Aspects
Subtitle of host publication	16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016: Proceedings
Editors	Petra Perner
Place of Publication	Cham
Publisher	Springer
Pages	29-40
Number of pages	12
ISBN (Electronic)	978-3-319-41561-1
ISBN (Print)	978-3-319-41560-4
DOIs	https://doi.org/10.1007/978-3-319-41561-1_3
Publication status	Published - 1 Jan 2016
Externally published	Yes
Event	16th Industrial Conference on Advances in Data Mining, ICDM 2016 - New York, United States Duration: 13 Jul 2016 → 17 Jul 2016

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9728
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer

Conference

Conference	16th Industrial Conference on Advances in Data Mining, ICDM 2016
Country/Territory	United States
City	New York
Period	13/07/16 → 17/07/16

Keywords

Adolescent idiopathic scoliosis
Lateral bending positions
Multi-body kinematic model
Spine curvature
Spine movement sequence

Access to Document

10.1007/978-3-319-41561-1_3

Cite this

Jalalian, A., Tay, F. E. H., & Liu, G. (2016). Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions. In P. Perner (Ed.), Advances in Data Mining: Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016: Proceedings (pp. 29-40). (Lecture Notes in Computer Science; Vol. 9728), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-319-41561-1_3

Jalalian, Athena ; Tay, Francis E.H. ; Liu, Gabriel. / Data mining in medicine : Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions. Advances in Data Mining: Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016: Proceedings. editor / Petra Perner. Cham : Springer, 2016. pp. 29-40 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence).

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title = "Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions",

abstract = "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.",

keywords = "Adolescent idiopathic scoliosis, Lateral bending positions, Multi-body kinematic model, Spine curvature, Spine movement sequence",

author = "Athena Jalalian and Tay, {Francis E.H.} and Gabriel Liu",

year = "2016",

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Jalalian, A, Tay, FEH & Liu, G 2016, Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions. in P Perner (ed.), Advances in Data Mining: Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016: Proceedings. Lecture Notes in Computer Science, vol. 9728, Lecture Notes in Artificial Intelligence, Springer, Cham, pp. 29-40, 16th Industrial Conference on Advances in Data Mining, ICDM 2016, New York, New York, United States, 13/07/16. https://doi.org/10.1007/978-3-319-41561-1_3

Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions. / Jalalian, Athena; Tay, Francis E.H.; Liu, Gabriel.
Advances in Data Mining: Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016: Proceedings. ed. / Petra Perner. Cham: Springer, 2016. p. 29-40 (Lecture Notes in Computer Science; Vol. 9728), (Lecture Notes in Artificial Intelligence).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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T2 - 16th Industrial Conference on Advances in Data Mining, ICDM 2016

AU - Jalalian, Athena

AU - Tay, Francis E.H.

AU - Liu, Gabriel

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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.

AB - 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.

KW - Adolescent idiopathic scoliosis

KW - Lateral bending positions

KW - Multi-body kinematic model

KW - Spine curvature

KW - Spine movement sequence

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SN - 978-3-319-41560-4

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SP - 29

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BT - Advances in Data Mining: Applications and Theoretical Aspects

A2 - Perner, Petra

PB - Springer

CY - Cham

Y2 - 13 July 2016 through 17 July 2016

ER -

Jalalian A, Tay FEH, Liu G. Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions. In Perner P, editor, Advances in Data Mining: Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016: Proceedings. Cham: Springer. 2016. p. 29-40. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-319-41561-1_3

Data mining in medicine: Relationship of scoliotic spine curvature to the movement sequence of lateral bending positions

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A patient-specific multibody model of scoliotic spine for surgical correction prediction in the coronal plane

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