A new method for determining lumbar spine motion using Bayesian belief network.
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 degrees. 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.
Ma
Heather Ting
HT
Department of Diagnostic Radiology and Organ Imaging, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong.
Yang
Zhengyi
Z
Griffith
James F
JF
Leung
Ping Chung
PC
Lee
Raymond Y W
RY
D000368:Aged; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D005260:Female; D006801:Humans; D007403:Intervertebral Disc; D008159:Lumbar Vertebrae; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D016059:Range of Motion, Articular / Q000502:physiology*