Philippe Poncet

Geometric Torsion in Idiopathic Scoliosis : Three-Dimensional Analysis and Proposal for a New Classification

Study Design. Three-dimensionally reconstructed spines of 62 subjects with idiopathic scoliosis were reviewed for three-dimensional pattern classification based on the measurement of geometric torsion. Objectives. To evaluate the relevance of geometric torsion as a three-dimensional index of scoliosis, and to develop a three-dimensional classification of deformity for idiopathic scoliosis as opposed to the current classifications based on two-dimensional frontal views. Summary of Background Data. Attempts have been made to measure the geometric torsional shape of scoliotic curves represented curvilinearly. However, the geometric torsion phenomenon has never been properly analyzed and thus has never been precisely defined. Methods. Standardized stereoradiographs of 62 patients with idiopathic scoliosis were obtained and used to generate three-dimensional reconstructions. A continuous parametric form of the curved line that passes through the vertebrae was created by least square fitting of Fourier series functions. Frenet’s formulas then were used to calculate the geometric torsion. Results. Analysis of geometric torsion associated with 94 major scoliotic curves allowed three basic categories of torsion curve patterns to be identified. Scoliotic spines with multiple major curves are described by a combination of basic torsion patterns, one for each curve. Conclusions. A three-dimensional analysis of the spine in terms of geometric torsion has defined three distinct patterns of torsion in a group of scoliotic curves. Geometric torsion had extreme values at the levels of upper and lower vertebrae, but zero or nearly zero values at the levels of the apexes. The torsional phenomenon can be unidirectional or bidirectional in both single and double major curves.

Indices of torso asymmetry related to spinal deformity in scoliosis.

OBJECTIVE To develop indices that quantify 360 degrees torso surface asymmetry sufficiently well to estimate the Cobb angle of scoliotic spinal deformity within the clinically important 5-10 degrees range. DESIGN Prospective study in 48 consecutive adolescent scoliosis patients (Cobb angles 10-71 degrees ). BACKGROUND Scoliotic surface asymmetry has been quantified on the back surface by indices such as back surface rotation (BSR) and curvature of the spinous process line and torso centroid line, though with limited success in spinal deformity estimation. Quantification of 360 degrees torso shape may enhance surface-spine correlation and permit reduced use of harmful X-rays in scoliosis. METHODS For each patient a 3D torso surface model was generated concurrently with postero-anterior X-rays. We computed indices describing principal axis orientation, back surface rotation, and asymmetry of the torso centroid line, left and right half-areas and the spinous process line. We calculated correlations of each index to the Cobb angle and used stepwise regression to estimate the Cobb angle. RESULTS Several torso asymmetry indices correlated well to the Cobb angle (r up to 0.8). The Cobb angle was best estimated by age, rib hump and left-right variation in torso width in unbraced patients and by centroid lateral deviation in braced patients. A regression model estimated the Cobb angle from torso indices within 5 degrees in 65% of patients and 10 degrees in 88% (r=0.91, standard error=6.1 degrees ). CONCLUSION Consideration of 360 degrees torso surface data yielded indices that correlated well to the Cobb angle and estimated the Cobb angle within 10 degrees in 88% of cases. RELEVANCE The torso asymmetry indices developed here show a strong surface-spine relation in scoliosis, encouraging development of a model to detect scoliosis magnitude and progression from the surface shape with minimal X-ray radiation.

Estimation of spinal deformity in scoliosis from torso surface cross sections

Study Design. Correlation of torso scan and three-dimensional radiographic data in 65 scans of 40 subjects. Objectives. To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an artificial neural network. Summary of Background Data. Quantification of torso surface asymmetry may aid diagnosis and monitoring of scoliosis and thereby minimize the use of radiographs. Artificial neural networks are computing tools designed to relate input and output data when the form of the relation is unknown. Methods. A three-dimensional torso scan taken concurrently with a pair of radiographs was used to generate an integrated three-dimensional model of the spine and torso surface. Sixty-five scan–radiograph pairs were generated during 18 months in 40 patients (Cobb angles 0–58°): 34 patients with adolescent idiopathic scoliosis and six with juvenile scoliosis. Sixteen (25%) were randomly selected for testing and the remainder (n = 49) used to train the artificial neural network. Contours were cut through the torso model at each vertebral level, and the line joining the centroids of area of the torso contours was generated. Lateral deviations and angles of curvature of this line, and the relative rotations of the principal axes of each contour were computed. Artificial neural network estimations of maximal computer Cobb angle were made. Results. Torso–spine correlations were generally weak (r < 0.5), although the range of torso rotation related moderately well to the maximal Cobb angle (r = 0.64). Deformity of the torso centroid line was minimal despite significant spinal deformity in the patients studied. Despite these limitations and the small data set, the artificial neural network estimated the maximal Cobb angle within 6° in 63% of the test data set and was able to distinguish a Cobb angle greater than 30° with a sensitivity of 1.0 and specificity of 0.75. Conclusions. Neural-network analysis of full-torso scan imaging shows promise to accurately estimate scoliotic spinal deformity in a variety of patients.

Reconstruction of laser-scanned 3D torso topography and stereoradiographical spine and rib-cage geometry in scoliosis

Assessments of scoliosis are routinely done by means of clinical examination and full spinal x-rays. Multiple exposure to ionization radiation, however, can be hazardous to the child and is costly. Here, we explain the use of a noninvasive imaging technique, based on laser optical scanning, for quantifying the three-dimensional (3D) trunk surface topography that can be used to estimate parameters of 3D deformity of the spine. The laser optical scanning system consisted of four BIRIS laser cameras mounted on a ring moving along a vertical axis, producing a topographical mapping of the entire torso. In conjunction with the laser scans, an accurate 3D reconstruction of the spine and rib cage were developed from the digitized x-ray images. Results from 14 scoliotic patients are reported. The digitized surfaces provided the foundation data to start studying concordance of trunk surface asymmetry and spinal shape in idiopathic scoliosis.

Genetic Algorithm–Neural Network Estimation of Cobb Angle from Torso Asymmetry in Scoliosis

Scoliosis severity, measured by the Cobb angle, was estimated by artificial neural network from indices of torso surface asymmetry using a genetic algorithm to select the optimal set of input torso indices. Estimates of the Cobb angle were accurate within 5 degrees in two-thirds, and within 10 degrees in six-sevenths, of a test set of 115 scans of 48 scoliosis patients, showing promise for future longitudinal studies to detect scoliosis progression without use of X-rays.

Comparison of Cobb Angles Measured Manually, Calculated from 3-D Spinal Reconstruction, and Estimated from Torso Asymmetry

While scoliotic spinal deformity is traditionally measured by the Cobb angle, we seek to estimate scoliosis severity from the torso surface without X-ray radiation. Here, we measured the Cobb angle in three ways: by protractor from postero-anterior X-ray, by computer from a 3-D digitized model of the vertebral body line, and by neural-network estimation from indices of torso surface asymmetry. The estimates of the Cobb angle by computer and by neural network were equally accurate in 153 records from 52 patients (standard deviation of 6° from the Cobb angle, r =0.93 ), showing that torso asymmetry reliably predicted spinal deformity. Further improvements in predictive accuracy may require estimation of other 3-D indices of spinal deformity besides the Cobb angle with its wide measurement variability.

Use of neural networks to correlate spine and rib deformity in scoliosis

Abstract Artificial neural networks (ANNs) recognize patterns relating input and output data in a manner analogous to the function of biological neurons. Here, we show that ANNs can predict rib deformity in scoliosis more accurately than regression analysis. ANNs and linear regression models were developed to predict rib rotation from several combinations of input spinal indices including Cobb angle, vertebral rotation, apex location and orientation of the plane of maximal curvature. ANNs averaged 60% correct predictions compared to 34% for regression analysis. This study provides evidence for the utility of artificial neural networks in scoliosis research. These data lend credence to the use of ANNs in future work on the prediction of scoliotic spinal deformity from torso surface data, which would permit assessment of scoliosis severity with minimal use of harmful X-rays.

Design and Manufacturing of Customized Braces for Scoliosis Treatment

A new method to design and manufacture customized braces is introduced in this research for scoliosis treatment. In this method, a geometric model of a scoliosis patient’s torso is achieved using a laser optical scanning device. The brace geometry is obtained by generating the offset geometry of the torso’s surface, selecting vertical boundaries, removing holes and noise data, creating symmetrical geometry, and modifying the geometry near the pelvis curves. Manufacturing of the brace is conducted by producing a male die with a sculptured surface using a custom-designed 5-axis CNC milling machine and creating the plastic brace using a thermoforming process.© 2002 ASME