Anatomically Accurate Modeling of Spine Movement to Depict the Scoliosis Condition

Little attention has been paid to how scoliosis movements deviate from intact spines and the consequent response to surgical instrumentation. Embedding such deviations into scoliosis simulation models can significantly improve their predictive capability for surgical outcome and to mitigate complication risks and thus bring more satisfaction to patients and clinicians. Scoliosis models are mainly intact spine models adapted by merely adjusting model parameters to produce scoliotic-like asymmetry, overlooking that the scoliosis condition results in significant deviations of movements. Thus, these adapted models might provide misleading predictive information. This paper aims to uncover the behaviors emerging out of scoliotic spine movements for simulation. A multibody model with micro-scale motion segments was utilized to study movement of nine adolescent idiopathic scoliosis patients. Statistical analysis was used to identify the shared movement behavior and to test their significance in terms of occurrence and their effects on the simulation results and prediction accuracy. Four movement behaviors were uncovered: (1) negligible change of spinal length, (2) bounded rotational displacements, (3) unilateral rotational displacements of certain vertebrae, (4) negligible rotational displacements around inflection vertebrae. Simulation results were improved significantly by incorporating these findings: location and orientation errors of vertebrae from 2.9±2.5 mm to 1.1±0.4 mm and 2.0±1.3to 1.0±0.4, respectively, approximation error of spine curvature from 2.1±2.0 mm to 0.6±0.3 mm. Therefore, scoliosis exhibits unique movements, and it is essential that scoliosis models comply for improved predictive capability.

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Publication Details

Journal
IEEE Transactions on Medical Robotics and Bionics
Published
2025-05-23
DOI
https://doi.org/10.1109/tmrb.2025.3573068
Primary Topic
Medical Imaging and Analysis
Type
article
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article

Anatomically Accurate Modeling of Spine Movement to Depict the Scoliosis Condition

Ian Gibson, Soheil Arastehfar, G. Liu, Athena Jalalian et al.
IEEE Transactions on Medical Robotics and Bionics
Medical Imaging and Analysis
article

Anatomically Accurate Modeling of Spine Movement to Depict the Scoliosis Condition

Ian Gibson, Soheil Arastehfar, G. Liu, Athena Jalalian, Francis E. H. Tay
article en

Abstract

Little attention has been paid to how scoliosis movements deviate from intact spines and the consequent response to surgical instrumentation. Embedding such deviations into scoliosis simulation models can significantly improve their predictive capability for surgical outcome and to mitigate complication risks and thus bring more satisfaction to patients and clinicians. Scoliosis models are mainly intact spine models adapted by merely adjusting model parameters to produce scoliotic-like asymmetry, overlooking that the scoliosis condition results in significant deviations of movements. Thus, these adapted models might provide misleading predictive information. This paper aims to uncover the behaviors emerging out of scoliotic spine movements for simulation. A multibody model with micro-scale motion segments was utilized to study movement of nine adolescent idiopathic scoliosis patients. Statistical analysis was used to identify the shared movement behavior and to test their significance in terms of occurrence and their effects on the simulation results and prediction accuracy. Four movement behaviors were uncovered: (1) negligible change of spinal length, (2) bounded rotational displacements, (3) unilateral rotational displacements of certain vertebrae, (4) negligible rotational displacements around inflection vertebrae. Simulation results were improved significantly by incorporating these findings: location and orientation errors of vertebrae from 2.9±2.5 mm to 1.1±0.4 mm and 2.0±1.3to 1.0±0.4, respectively, approximation error of spine curvature from 2.1±2.0 mm to 0.6±0.3 mm. Therefore, scoliosis exhibits unique movements, and it is essential that scoliosis models comply for improved predictive capability.

IEEE Transactions on Medical Robotics and BionicsVol. 7(3)
National University of Singapore (SG), National University Hospital (SG), University of Twente (NL)
Gender equality
Openalex Percentile: Top 89%
Medical Imaging and Analysis
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