A new method for determining lumbar spine motion using Bayesian belief network

Ma, Heather Ting, Yang, Zhengyi, Griffith, James F., Leung, Ping Chung and Lee, Raymond Y. W. (2008) A new method for determining lumbar spine motion using Bayesian belief network. Medical and Biological Engineering and Computing, 46 4: 333-340. doi:10.1007/s11517-008-0318-y

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Author Ma, Heather Ting
Yang, Zhengyi
Griffith, James F.
Leung, Ping Chung
Lee, Raymond Y. W.
Title A new method for determining lumbar spine motion using Bayesian belief network
Journal name Medical and Biological Engineering and Computing   Check publisher's open access policy
ISSN 0140-0118
Publication date 2008-04-01
Year available 2008
Sub-type Article (original research)
DOI 10.1007/s11517-008-0318-y
Open Access Status
Volume 46
Issue 4
Start page 333
End page 340
Total pages 8
Editor J. A. E. Spaan
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Subject C1
110601 Biomechanics
920116 Skeletal System and Disorders (incl. Arthritis)
Abstract A Bayesian network dynamic model was developed to determine the kinematics of the intervertebral joints of the lumbar spine. Radiographic images in flexion and extension postures were used as input data for modeling, together with movement information from the skin surface using an electromagnetic motion tracking system. Intervertebral joint movements were then estimated by the graphic network. The validity of the model was tested by comparing the predicted position of the vertebrae in the neutral position with those obtained from the radiographic image in the neutral posture. The correlation between the measured and predicted movements was 0.99 (p < 0.01) with a mean error of less than 1.5°. The movement sequence of the various vertebrae was examined based on the model output, and wide variations in the kinematic patterns were observed. The technique is non-invasive and has potential to be used clinically to measure the kinematics of lumbar intervertebral movement.
Keyword intervertebral kinematics
Bayesian belief networks
lumbar spine motion
dynamic modeling
spine biomechanics
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: 2009 Higher Education Research Data Collection
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 31 Mar 2009, 23:27:49 EST by Lesley-Jayne Jerrard on behalf of Centre For Magnetic Resonance